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Machines
Number of Followers: 4  Open Access journalISSN (Online) 2075-1702 Published by MDPI  [258 journals]
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- Machines, Vol. 11, Pages 1037: Melt Pool Monitoring and X-ray Computed
Tomography-Informed Characterisation of Laser Powder Bed Additively
Manufactured Silver–Diamond Composites
Authors: John Robinson, Abul Arafat, Aaron Vance, Arun Arjunan, Ahmad Baroutaji
First page: 1037
Abstract: In this study, silver (Ag) and silver–diamond (Ag-D) composites with varying diamond (D) content are fabricated using laser powder bed fusion (L-PBF) additive manufacturing (AM). The L-PBF process parameters and inert gas flow rate are optimised to control the build environment and the laser energy density at the powder bed to enable the manufacture of Ag-D composites with 0.1%, 0.2% and 0.3% D content. The Ag and D powder morphology are characterised using scanning electron microscopy (SEM). Ag, Ag-D0.1%, Ag-D0.2% and Ag-D0.3% tensile samples are manufactured to assess the resultant density and tensile strength. In-process EOSTATE melt pool monitoring technology is utilised as a comparative tool to assess the density variations. This technique uses in-process melt pool detection to identify variations in the melt pool characteristics and potential defects and/or density deviations. The resultant morphology and associated defect distribution for each of the samples are characterised and reported using X-ray computed tomography (xCT) and 3D visualisation techniques. Young’s modulus, the failure strain and the ultimate tensile strength of the L-PBF Ag and Ag-D are reported. The melt pool monitoring results revealed in-process variations in the build direction, which was confirmed through xCT 3D visualisations. Additionally, the xCT analysis displayed density variations for all the Ag-D composites manufactured. The tensile results revealed that increasing the diamond content reduced Young’s modulus and the ultimate tensile strength.
Citation: Machines
PubDate: 2023-11-21
DOI: 10.3390/machines11121037
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1038: ML-Enabled Piezoelectric-Driven Internal
Defect Assessment in Metal Structures
Authors: Daniel Adeleye, Mohammad Seyedi, Farzad Ferdowsi, Jonathan Raush, Ahmed Khattab
First page: 1038
Abstract: With the growth of 3D printing in the production space, it is inevitable that quality assurance will be needed to keep final products within the constraints of requirements. Also, the variety of materials that can be used with 3D printing has increased over the years. Testing also must consider the process of manufacturing. This paper focuses its efforts on the finished product and not the process of manufacturing. Ultrasonic testing is a type of nondestructive testing. The experiments performed in this study aim to explore the usefulness of ultrasonic testing in materials that are 3D printed. The two materials used in this study are steel alloy metals and aluminum blocks of the same dimensions—120 mm × 40 mm × 15 mm. These materials represent common choices in additive manufacturing processes. The chosen alloys, such as Aluminum (6063T6) and grade-304 stainless steel, possess distinct properties crucial for validating the proposed testing method. Metal 3D-printed materials play a pivotal role in diverse industries, since ensuring their structural integrity is imperative for reliability and safety. Testing is crucial to identify and mitigate defects that could compromise the functionality and longevity of the final products, especially in applications with demanding performance requirements. An ultrasonic transducer is used to scan for subsurface defects within the samples and an oscilloscope is used to analyze the signals. Furthermore, several Machine Learning (ML) techniques are used to estimate the severity of the defects. The application of Machine Learning methods in the manufacturing industry has proven advantageous in terms of detecting defects due to its practicality and wide application. Due to their distinct benefits in processing image information, convolutional neural networks (CNNs) are the preferred method when working with picture data. In order to perform binary and multi-class classification, support vector machines that employ the alternative kernel function are a viable option for processing sensor signals and picture data. The study reveals that ultrasonic tests are viable for metallic materials. The primary objective of this work is to evaluate and validate the application of ultrasonic testing for the inspection of 3D-printed steel alloy metals and aluminum blocks. The novelty lies in the integration of Machine Learning techniques to estimate defect severity, offering a comprehensive and non-invasive approach to quality assessment in 3D-printed materials. The proposed method can successfully detect the presence of internal defects in objects, as well as estimate the location and severity of the defects.
Citation: Machines
PubDate: 2023-11-21
DOI: 10.3390/machines11121038
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1039: Research on Belt Deviation Fault Detection
Technology of Belt Conveyors Based on Machine Vision
Authors: Xiangfan Wu, Chusen Wang, Zuzhi Tian, Xiankang Huang, Qian Wang
First page: 1039
Abstract: Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.
Citation: Machines
PubDate: 2023-11-22
DOI: 10.3390/machines11121039
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1040: Analysis and Design of Small-Impact
Magnetoelectric Generator
Authors: Shaohua Niu, Bing Li, Bingyang Li, Pengfei Wang, Yuxi Song
First page: 1040
Abstract: For projectile impact penetration experiment, batteries or capacitors are usually used as power sources for projectile-borne recording devices. However, these power sources are easy to fail under high impact. In this paper, a small-impact magnetoelectric generator is introduced, which converts impact force into electrical energy to supply power for devices. The influence of generator structure on force–electricity conversion efficiency is analyzed. Based on the analysis, a small-impact magnetoelectric generator with double springs and two-part coils is designed. A hammer test is carried out on the generator. The test results show that this generator structure would achieve higher force–electricity conversion efficiency under small space.
Citation: Machines
PubDate: 2023-11-22
DOI: 10.3390/machines11121040
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1041: An Augmented Reality-Assisted Disassembly
Approach for End-of-Life Vehicle Power Batteries
Authors: Jie Li, Bo Liu, Liangliang Duan, Jinsong Bao
First page: 1041
Abstract: The rapid expansion of the global electric vehicle industry has presented significant challenges in the management of end-of-life power batteries. Retired power batteries contain valuable resources, such as lithium, cobalt, nickel, and other metals, which can be recycled and reused in various applications. The existing disassembly processes rely on manual operations that are time-consuming, labour-intensive, and prone to errors. This research proposes an intelligent augmented reality (AR)-assisted disassembly approach that aims to increase disassembly efficiency by providing scene awareness and visual guidance to operators in real-time. The approach starts by employing a deep learning-based instance segmentation method to process the Red-Green-Blue-Dept (RGB-D) data of the disassembly scene. The segmentation method segments the disassembly object instances and reconstructs their point cloud representation, given the corresponding depth information obtained from the instance masks. In addition, to estimate the pose of the disassembly target in the scene and assess their disassembly status, an iterative closed point algorithm is used to align the segmented point cloud instances with the actual disassembly objects. The acquired information is then utilised for the generation of AR instructions, decreasing the need for frequent user interaction during the disassembly processes. To verify the feasibility of the AR-assisted disassembly system, experiments were conducted on end-of-life vehicle power batteries. The results demonstrated that this approach significantly enhanced disassembly efficiency and decreased the frequency of disassembly errors. Consequently, the findings indicate that the proposed approach is effective and holds promise for large-scale industrial recycling and disassembly operations.
Citation: Machines
PubDate: 2023-11-22
DOI: 10.3390/machines11121041
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1042: Prediction of SOx-NOx Emission in
Coal-Fired Power Plant Using Deep Neural Network
Authors: Min Seop So, Duncan Kibet, Tae Kyeong Woo, Seong-Joon Kim, Jong-Ho Shin
First page: 1042
Abstract: Coal has been used as the most commonly energy source for power plants since it is relatively cheap and readily available. Thanks to these benefits, many countries operate coal-fired power plants. However, the combustion of coal in the coal-fired power plant emits pollutants such as sulfur oxides (SOx) and nitrogen oxides (NOx) which are suspected to cause damage to the environment and also be harmful to humans. For this reason, most countries have been strengthening regulations on coal-consuming industries. Therefore, the coal-fired power plant should also follow these regulations. This study focuses on the prediction of harmful emissions when the coal is mixed with high-quality and low-quality coals during combustion in the coal-fired power plant. The emission of SOx and NOx is affected by the mixture ratio between high-quality and low-quality coals so it is very important to decide on the mixture ratio of coals. To decide the coal mixture, it is a prerequisite to predict the amount of SOx and NOx emission during combustion. To do this, this paper develops a deep neural network (DNN) model which can predict SOx and NOx emissions associated with coal properties when coals are mixed. The field data from a coal-fired power plant is used to train the model and it gives mean absolute percentage error (MAPE) of 7.1% and 5.68% for SOx and NOx prediction, respectively.
Citation: Machines
PubDate: 2023-11-23
DOI: 10.3390/machines11121042
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1043: Hardware–Software Embedded System for
Real-Time Trajectory Planning of Multi-Axis Machine Using B-Spline Curve
Interpolation Algorithm
Authors: Qitao Tan, Mohd Ariffanan Mohd Basri
First page: 1043
Abstract: This paper proposes a B-spline trajectory algorithm to realize multi-axis trajectory interpolation and analyzes the operating accuracy in an embedded system. However, the existing trajectory generation method needs to use computer-aided manufacturing (CAM) software to convert the interpolating trajectory into G code and download the code into the computer numerical control (CNC) system for processing. In this paper, the method of third-degree B-spline interpolation is proposed to generate a curved surface trajectory, and the trajectory generated by this algorithm can be run directly into a CNC system. The precision analysis of the ISO parameter segmentation interpolation algorithm and the theory of constant velocity motion is also presented. The significance of this project is that it designs a complete set of embedded systems, including hardware circuit design and software logic design, and uses low-cost STM32 architecture to realize a B-spline constant-speed interpolation algorithm, which is verified on CNC polishing equipment. A simulation conducted with the MATLAB software and the B-spline curve interpolation experiments performed on a multi-axis polishing machine tool demonstrate the effectiveness and accuracy of the optimized third-degree B-spline algorithm.
Citation: Machines
PubDate: 2023-11-23
DOI: 10.3390/machines11121043
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1044: Nonlinear Identification and Decoupling
Sliding Mode Control of Macro-Micro Dual-Drive Motion Platform with
Mechanical Backlash
Authors: Shuo Kang, Buyang Zhang, Xing Huang, Rijin Zhong, Shengzhao Huang
First page: 1044
Abstract: A macro–micro dual-drive motion platform is a class of key system utilized in ultra-precision instruments and equipment for realizing ultra-high-precision positioning, which relates to the fields of semiconductor manufacturing, ultra-precision testing and machining, etc. Aiming at the ultra-high-precision positioning control problem of macro–micro dual-drive systems containing mechanical backlash, this paper analyzes the combined effect of mechanical coupling and backlash, and proposes a macro–micro compound control strategy. Firstly, the system dynamic model, including mechanical coupling, is established, and a quasi-linear backlash model is also proposed. Secondly, based on the above model, a stepwise nonlinear identification method is proposed to obtain the backlash characteristic online, which is the basis of accurate backlash compensation. Then, for the macro–micro structure containing the backlash, a macro decoupling control method, combined with a micro adaptive integral sliding mode control method and backlash compensation, are designed coordinately to guarantee that the large-stroke macro–micro cooperative motion reaches micron-level accuracy. Moreover, the boundary of the positioning error is adjustable by tuning the controller parameters. Finally, both the simulation and experimental results demonstrate that the proposed identification method can estimate the time-varying backlash precisely in finite time, and the system positioning accuracy can achieve an average 20 μm with long stroke and backlash influence, which is much higher than that using the traditional method and provides theoretical guidance for high-precision positioning control of a class of dual-drive motion platform.
Citation: Machines
PubDate: 2023-11-23
DOI: 10.3390/machines11121044
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1045: Diagnosing Faults in Different Technical
Systems: How Requirements for Diagnosticians Can Be Revealed by Comparing
Domain Characteristics
Authors: Judith Schmidt, Romy Müller
First page: 1045
Abstract: In complex work domains, not all possible faults can be anticipated by designers or handled by automation. Humans therefore play an important role in fault diagnosis. To support their diagnostic reasoning, it is necessary to understand the requirements that diagnosticians face. While much research has dealt with identifying domain-general aspects of fault diagnosis, the present exploratory study examined domain-specific influences on the requirements for diagnosticians. Scenario-based interviews were conducted with nine experts from two domains: the car domain and the packaging machine domain. The interviews revealed several factors that influence the requirements for successful fault diagnosis. These factors were summarized in five categories, namely domain background, technical system, typical faults, diagnostic process, and requirements. Based on these factors, we developed the Domain Requirements Model to predict requirements for diagnosticians (e.g., the need for empirical knowledge) from domain characteristics (e.g., the degree to which changes in inputs are available as domain knowledge) or characteristics of the diagnostic process (e.g., the extent of support). The model is discussed considering the psychological literature on fault diagnosis, and first insights are provided that show how the model can be used to predict requirements of diagnostic reasoning beyond the two domains studied here.
Citation: Machines
PubDate: 2023-11-23
DOI: 10.3390/machines11121045
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1046: A New Direct and Inexpensive Method and the
Associated Device for the Inspection of Spur Gears
Authors: Stelian Alaci, Florina-Carmen Ciornei, Ionut-Cristian Romanu, Ioan Doroftei, Carmen Bujoreanu, Ioan Tamașag
First page: 1046
Abstract: This paper proposes a new rapid and straightforward method along with a related device for finding the three basic parameters of an actual external involute spur gear. The number of teeth is easily counted, but the other two parameters—the module and the coefficient of profile shift—are difficult to identify. The method is based on the principle of inspection of the precision of gear teeth, using the dimension over pins, when the maximum distance is measured between the lateral surfaces of two cylindrical rollers of well-controlled dimensions, placed into the spaces between teeth. The dimension over pins is applied as a function of the number of teeth (odd or even) and requires experience (and this is the main disadvantage of the method) for finding the correct maximum distance between pins. The new method eliminates this drawback as it proposes a measuring scheme where four identical rollers are used in a designed inspection device. The system is statically determinate and, therefore, the dimension to be measured is unequivocally found. A new relation for the dimension to be measured is deduced and allows for finding the module and the coefficient of profile shift. The inspection device is described and a concrete case is presented for exemplifying the methodology. A further application permits finding the centre distance for an external spur gearing. Unlike the classical technique where the centre distance is obtained based on the centring surfaces of the wheels, the new method implies only dimensions measured through flank measurements, thus eliminating errors introduced by the deviations between the flanks and the centring surfaces of the wheels.
Citation: Machines
PubDate: 2023-11-24
DOI: 10.3390/machines11121046
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1047: Topology Optimization of Geometrically
Nonlinear Structures Based on a Self-Adaptive Material Interpolation
Scheme
Authors: Junwen Liang, Xianmin Zhang, Benliang Zhu, Rixin Wang, Chaoyu Cui, Hongchuan Zhang
First page: 1047
Abstract: In this paper, a simple and effective self-adaptive material interpolation scheme is proposed to solve the numerical instability problem, which may occur in topology optimization considering geometrical nonlinearity when using density-based method. The primary concept of the proposed method revolves around enhancing the deformation resistance of minimum-density or intermediatedensity elements, thus avoiding numerical instability due to excessive distortion of these elements. The proposed self-adaptive material interpolation scheme is based on the power law method, and the stiffness of minimum-density or intermediate-density elements can be adjusted by a single parameter, α. During the optimization process, the parameter α will be changed according to an adaptive adjustment strategy to ensure that elements within the design domain are not excessively distorted, while the mechanical behavior of the structure can be approximated with acceptable accuracy. Numerical examples of minimizing compliance and maximizing displacement of structure are given to prove the validity of the proposed self-adaptive material interpolation scheme.
Citation: Machines
PubDate: 2023-11-24
DOI: 10.3390/machines11121047
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1048: On Drum Brake Squeal—Assessment of
Damping Measures by Time Series Data Analysis of Dynamometer Tests and
Complex Eigenvalue Analyses
Authors: Nils Gräbner, Dominik Schmid, Utz von Wagner
First page: 1048
Abstract: Brake squeal—an audible high-frequency noise phenomenon in the range between 1 kHz and 15 kHz resulting from self-excited vibrations—is one of the main cost drivers while developing brake systems. Increasing damping is often a crucial factor in the context of self-excited vibrations. Countermeasures applied for preventing brake squeal have been investigated particularly for disk brakes in the past. However, in recent years, drum brakes have once again become more important, partly because of the issue of particle emissions. Concerning noise problems, drum brakes have a decisive advantage compared to disk brake systems in that the outer drum surface is freely accessible for applying damping devices. This paper focuses on the fundamental proving and evaluation of passive damping measures on a simplex drum brake system. To obtain a detailed understanding of the influence of additional damping on the squealing behavior of drum brakes, extensive experimental investigations are performed on a brake with an intentionally introduced high squealing tendency in the initial configuration. This made it possible to investigate the influence of different types of damping measures on their effectiveness. Techniques from the field of big data analysis and machine learning are tested to detect squeal in measured time series data. These techniques were remarkably reliable and made it possible to detect squeal efficiently even in data that was not generated on a traditional costly NVH brake dynamometer. To investigate whether the simulation method usually used for the simulation of brake squeal is applicable to depicting the influence of additional damping in drum brakes, a complex eigenvalue analysis was performed with Abaqus, and the results were compared with those from the experiments.
Citation: Machines
PubDate: 2023-11-24
DOI: 10.3390/machines11121048
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1049: G-DMD: A Gated Recurrent Unit-Based Digital
Elevation Model for Crop Height Measurement from Multispectral Drone
Images
Authors: Jinjin Wang, Nobuyuki Oishi, Phil Birch, Bao Kha Nguyen
First page: 1049
Abstract: Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains.
Citation: Machines
PubDate: 2023-11-25
DOI: 10.3390/machines11121049
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1050: Efficient Navigation and Motion Control for
Autonomous Forklifts in Smart Warehouses: LSPB Trajectory Planning and MPC
Implementation
Authors: Vorasawad, Park, Kim
First page: 1050
Abstract: The rise of smart factories and warehouses has ushered in an era of intelligent manufacturing, with autonomous robots playing a pivotal role. This study focuses on improving the navigation and control of autonomous forklifts in warehouse environments. It introduces an innovative approach that combines a modified Linear Segment with Parabolic Blends (LSPB) trajectory planning with Model Predictive Control (MPC) to ensure efficient and secure robot movement. To validate the performance of our proposed path-planning method, MATLAB-based simulations were conducted in various scenarios, including rectangular and warehouse-like environments, to demonstrate the feasibility and effectiveness of the proposed method. The results demonstrated the feasibility of employing Mecanum wheel-based robots in automated warehouses. Also, to show the superiority of the proposed control algorithm performance, the navigation results were compared with the performance of a system using the PID control as a lower-level controller. By offering an optimized path-planning approach, our study enhances the operational efficiency and effectiveness of Mecanum wheel robots in real-world applications such as automated warehousing systems.
Citation: Machines
PubDate: 2023-11-25
DOI: 10.3390/machines11121050
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1051: Design and Analysis of an Adaptive
Obstacle-Overcoming Tracked Robot with Passive Swing Arms
Authors: Ruiming Li, Xianhong Zhang, Shaoheng Hu, Jianxu Wu, Yu Feng, Yan-an Yao
First page: 1051
Abstract: This paper presents a novel adaptive tracked robot equipped with passive swing arms for overcoming obstacles. First, the paper introduces the overall composition of the robot and focuses on the adaptive mechanism of the passive swing arms. Second, analyzing the single-step obstacle-overcoming process of the robot reveals the relationship between the obstacle height and the geometric parameters of the passive swing arms, establishing a kinematic model. Then, a dynamic model of the robot’s obstacle-overcoming process is established by simplifying the robot into a crank–slider linkage, and the time range for the robot to overcome obstacles is analyzed. Finally, through virtual simulation and a physical prototype, the feasibility and maneuverability of the robot’s design are verified. These findings demonstrate the potential of the robot in various applications, such as search and rescue missions and homeland security.
Citation: Machines
PubDate: 2023-11-27
DOI: 10.3390/machines11121051
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1052: The LESGIRgram: A New Method to Select the
Optimal Demodulation Frequency Band for Rolling Bearing Faults
Authors: Tian Tian, Guiji Tang, Xiaolong Wang
First page: 1052
Abstract: Resonance demodulation of vibration signals is a common method for extracting fault information from rolling bearings. Nonetheless, demodulation quality is dependent on frequency band location. Established methods such as the Fast Kurtogram, Autogram, SKRgram, etc. have achieved satisfactory results in some cases, but the results are not good in the presence of strong white Gaussian noise and random impulses. To solve these issues, an algorithm that selects the optimal demodulation frequency band (ODFB) based on the ratio of the logarithmic envelope spectrum Gini coefficient (LESGIRgram) is proposed. The core idea of this paper is to capture the difference between the LESGIgrams of health and fault signals and accordingly locate the frequency bands that contain the most fault information. Initially, the baseline is constructed by calculating the logarithmic envelope spectrum Gini coefficient matrix of the health bearing (LESGIbaseline). Next, the LESGI matrix of the fault bearing (LESGImeasured) is computed. The ratio of LESGImeasured to LESGIbaseline is calculated, and the ODFB can be selected with the maximum LESGIR. The fault signal is then filtered using this derived ODFB, and envelope analysis is performed to extract fault features. The proposed algorithm for detecting rolling bearing faults has been verified for accuracy and effectiveness through simulation and experimental data.
Citation: Machines
PubDate: 2023-11-27
DOI: 10.3390/machines11121052
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1053: Performance Evaluation of Per-Phase Model
Predictive Control Schemes for Extending Lifespan of Voltage Source
Converters
Authors: Minh Hoang Nguyen, Sangshin Kwak, Seungdeog Choi
First page: 1053
Abstract: Unequal thermal stress among the phase legs of a multiphase converter leads to a reduction in the useful lifespan and reliability of that converter in general. Increasing the converter’s lifespan by relieving the stressed phase leg, which suffers excessive thermal stress due to aging, is crucial. This paper evaluates two control concepts, including two per-phase model predictive control methods for extending the lifespan of a voltage source inverter. These two per-phase techniques alter the switching pattern to reduce the losses of the most aged phase leg. Hence, the loss and the corresponding thermal stress of the leg that has aged the most are reduced. In such a way, the lifespan and reliability of the converter are prolonged. Two per-phase model predictive control techniques are executed in both simulation and experiment environments, where the corresponding results are provided to evaluate the behavior of these control strategies, considering several operational aspects both in steady state and transient operation. In addition to static load conditions, two per-phase techniques are verified for the correct operation under dynamic load (induction motor) conditions.
Citation: Machines
PubDate: 2023-11-27
DOI: 10.3390/machines11121053
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1054: Development of Various Types of Independent
Phase Based Pulsewidth Modulation Techniques for Three-Phase Voltage
Source Inverters
Authors: Minh Hoang Nguyen, Sangshin Kwak, Seungdeog Choi
First page: 1054
Abstract: Discontinuous pulse-width-modulation (DPWM) methods have been extensively used in the industrial area to reduce overall losses, which decreases the corresponding thermal stress on the power switches of converters. However, local thermal overload can arise due to different aging conditions of semiconductor devices or failure in the cooling system. This leads to reduced reliability of the converter system due to the low expected lifespan of the most aged switches or phase legs. In this paper, the modified DPWM strategies for independent control of per-phase switching loss are introduced to deal with this matter. The proposed per-phase DPWM techniques are generated by modifying the conventional three-phase DPWM methods for reducing the switching loss in a specific leg, whereas the output performance is not degraded. This paper reports on output performance, including output current total harmonic distortion (THD) and power loss of switching devices, analysis for the various modified DPWM strategies for independent control of per-phase switching loss, which is applicable in 2-level 3-phase voltage source inverters (2L3P VSIs). The results are compared to the corresponding continuous PWM technique to verify and analyze the effectiveness and accuracy of the modified DPWM strategies for independent control of per-phase switching loss.
Citation: Machines
PubDate: 2023-11-27
DOI: 10.3390/machines11121054
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1055: A New Automated Classification Framework
for Gear Fault Diagnosis Using Fourier–Bessel Domain-Based Empirical
Wavelet Transform
Authors: Dada Saheb Ramteke, Anand Parey, Ram Bilas Pachori
First page: 1055
Abstract: Gears are the most important parts of a rotary system, and they are used for mechanical power transmission. The health monitoring of such a system is needed to observe its effective and reliable working. An approach that is based on vibration is typically utilized while carrying out fault diagnostics on a gearbox. Using the Fourier–Bessel series expansion (FBSE) as the basis for an empirical wavelet transform (EWT), a novel automated technique has been proposed in this paper, with a combination of these two approaches, i.e., FBSE-EWT. To improve the frequency resolution, the current empirical wavelet transform will be reformed utilizing the FBSE technique. The proposed novel method includes the decomposition of different levels of gear crack vibration signals into narrow-band components (NBCs) or sub-bands. The Kruskal–Wallis test is utilized to choose the features that are statistically significant in order to separate them from the sub-bands. Three classifiers are used for fault classification, i.e., random forest, J48 decision tree classifiers, and multilayer perceptron function classifier. A comparative study has been performed between the existing EWT and the proposed novel methodology. It has been observed that the FBSE-EWT with a random forest classifier shows a better gear fault detection performance compared to the existing EWT.
Citation: Machines
PubDate: 2023-11-28
DOI: 10.3390/machines11121055
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1056: Using Lie Derivatives with Dual Quaternions
for Parallel Robots
Authors: Stephen Montgomery-Smith, Cecil Shy
First page: 1056
Abstract: We introduce the notion of the Lie derivative in the context of dual quaternions that represent rigid motions and twists. First we define the wrench in terms of dual quaternions. Then we show how the Lie derivative helps understand how actuators affect an end effector in parallel robots, and make it explicit in the two cases case of Stewart Platforms, and cable-driven parallel robots. We also show how to use Lie derivatives with the Newton-Raphson Method to solve the forward kinematic problem for over constrained parallel actuators. Finally, we derive the equations of motion of the end effector in dual quaternion form, which include the effect of inertia from the actuators.
Citation: Machines
PubDate: 2023-11-28
DOI: 10.3390/machines11121056
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1057: Microwave Frequency Offset Induced by
Subsurface Damage in Abrasive-Machined Semiconductor Ceramic Waveguide
Authors: Haoji Wang, Jinhua Wei, Bin Lin, Xiaoqi Cui, Hetian Hou, Zhiyuan Fu, Jianchun Ding, Tianyi Sui
First page: 1057
Abstract: Ceramic waveguide components play a critical role in modern microwave semiconductor systems. For the first time, this work reports experimental results obtained when dielectric ceramics are abrasive-machined into waveguide components. This process will cause subsurface damage (SSD), resulting in a deviation in their working frequency which can degrade the performance of the system. For a substrate-integrated waveguide (SIW) resonator working at 10.1 GHz, SSD with a depth of 89 um can cause a maximum frequency offset of 20.2%. For a mm wave component working at 70 GHz, the corresponding frequency offset could increase to 169%. Three resonator SIW filters with SSD are studied, and the results demonstrate that the frequency offset induced by SSD can reduce the pass rate of the filters from 95.4% to 0%. A theoretical analysis is performed to reveal the mechanism and to offer a quantitative estimation of the limiting range of the offset caused by SSD. Feasible methods for reducing the offset caused by SSD, such as structure design, processing optimization, and material reinforcement, are discussed.
Citation: Machines
PubDate: 2023-11-28
DOI: 10.3390/machines11121057
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1058: Reinforcement Learning-Based Dynamic Zone
Positions for Mixed Traffic Flow Variable Speed Limit Control with
Congestion Detection
Authors: Filip Vrbanić, Martin Gregurić, Mladen Miletić, Edouard Ivanjko
First page: 1058
Abstract: Existing transportation infrastructure and traffic control systems face increasing strain as a result of rising demand, resulting in frequent congestion. Expanding infrastructure is not a feasible solution for enhancing the capacity of the road. Hence, Intelligent Transportation Systems are often employed to enhance the Level of Service (LoS). One such approach is Variable Speed Limit (VSL) control. VSL increases the LoS and safety on motorways by optimizing the speed limit according to the traffic conditions. The proliferation of Connected and Autonomous Vehicles (CAVs) presents fresh prospects for improving the operation and measurement of traffic states for the operation of the VSL control system. This paper introduces a method for the detection of multiple congested areas that is used for state estimation for a dynamically positioned VSL control system for urban motorways. The method utilizes Q-Learning (QL) and CAVs as mobile sensors and actuators. The proposed control approach, named Congestion Detection QL Dynamic Position VSL (CD-QL-DPVSL), dynamically detects all of the congested areas and applies two sets of actions, involving the dynamic positioning of speed limit zones and imposed speed limits for all detected congested areas simultaneously. The proposed CD-QL-DPVSL control approach underwent an evaluation across six distinct traffic scenarios, encompassing CAV penetration rates spanning from 10% to 100% and demonstrated a significantly better performance compared to other control approaches, including no control, rule-based VSL, two Speed-Transition-Matrices-based QL-VSL configurations with fixed speed limit zone positions, and a Speed-Transition-Matrices-based QL-DVSL with a dynamic speed limit zone position. It achieved enhancements in macroscopic traffic parameters such as the Mean Travel Time and Total Time Spent by adapting its control policy to every simulated scenario.
Citation: Machines
PubDate: 2023-11-28
DOI: 10.3390/machines11121058
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1059: Efficient Autonomous Path Planning for
Ultrasonic Non-Destructive Testing: A Graph Theory and K-Dimensional Tree
Optimisation Approach
Authors: Mengyuan Zhang, Mark Sutcliffe, P. Ian Nicholson, Qingping Yang
First page: 1059
Abstract: Within the domain of robotic non-destructive testing (NDT) of complex structures, the existing methods typically utilise an offline robot-path-planning strategy. Commonly, for robotic inspection, this will involve full coverage of the component. An NDT probe oriented normal to the component surface is deployed in a raster scan pattern. Here, digital models are used, with the user decomposing complex structures into manageable scan path segments, while carefully avoiding obstacles and other geometric features. This is a manual process that requires a highly skilled robotic operator, often taking several hours or days to refine. This introduces several challenges to NDT, including the need for an accurate model of the component (which, for NDT inspection, is often not available), the requirement of skilled personnel, and careful consideration of both the NDT inspection method and the geometric structure of the component. This paper addresses the specific challenge of scanning complex surfaces by using an automated approach. An algorithm is presented, which is able to learn an efficient scan path by taking into account the dimensional constraints of the footprint of an ultrasonic phased-array probe (a common inspection method for NDT) and the surface geometry. The proposed solution harnesses a digital model of the component, which is decomposed into a series of connected nodes representing the NDT inspection points within the NDT process—this step utilises graph theory. The connections to other nodes are determined using nearest neighbour with KD-Tree optimisation to improve the efficiency of node traversal. This enables a trade-off between simplicity and efficiency. Next, movement restrictions are introduced to allow the robot to navigate the surface of a component in a three-dimensional space, defining obstacles as prohibited areas, explicitly. Our solution entails a two-stage planning process, as follows: a modified three-dimensional flood fill is combined with Dijkstra’s shortest path algorithm. The process is repeated iteratively until the entire surface is covered. The efficiency of this proposed approach is evaluated through simulations. The technique presented in this paper provides an improved and automated method for NDT robotic inspection, reducing the requirement of skilled robotic path-planning personnel while ensuring full component coverage.
Citation: Machines
PubDate: 2023-11-29
DOI: 10.3390/machines11121059
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1060: Experimental Validation of a Driver
Monitoring System
Authors: María Garrosa, Marco Ceccarelli, Vicente Díaz, Matteo Russo
First page: 1060
Abstract: This paper presents an analysis of the risk of neck injury in vehicle occupants as a consequence of an impact. A review of the formulation of indexes that are used in the assessment and investigation of neck injury risk is discussed with the aim of providing a new, more appropriate index using suitable sensorized equipment. An experimental analysis is proposed with a new driver monitoring device using low-cost sensors. The system consists of wearable units for the head, neck, and torso where inertial measurement sensors (IMU) are installed to record data concerning the occupant’s head, neck, and torso accelerations while the vehicle moves. Two laser infrared distance sensors are also installed on the vehicle’s steering wheel to record the position data of the head and neck, as well as an additional IMU for vehicle acceleration values. To validate both the device and the new index, experiments are designed in which different sensorized volunteers reproduce an emergency braking maneuver with an instrumented vehicle at speeds of 10, 20, and 30 km/h before the beginning of any braking action. The neck is particularly sensitive to sudden changes in acceleration, so a sudden braking maneuver is enough to constitute a potential risk of cervical spine injury. During the experiments, large accelerations and displacements were recorded as the test speed increased. The largest accelerations were obtained in the experimental test at a speed of 30 km/h with values of 19.17, 9.57, 9.28, and 5.09 m/s2 in the head, torso, neck, and vehicle, respectively. In the same experiment, the largest displacement of the head was 0.33 m and that of the neck was 0.27 m. Experimental results have verified that the designed device can be effectively used to characterize the biomechanical response of the neck in car impacts. The new index is also able to quantify a neck injury risk by taking into account the dynamics of a vehicle and the kinematics of the occupant’s head, neck, and torso. The numerical value of the new index is inversely proportional to the acceleration experienced by the vehicle occupant, so that small values indicate risky conditions.
Citation: Machines
PubDate: 2023-11-29
DOI: 10.3390/machines11121060
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1061: Vibration Analysis for Fault Diagnosis in
Induction Motors Using One-Dimensional Dilated Convolutional Neural
Networks
Authors: Xiaopeng Liu, Jianfeng Hong, Kang Zhao, Bingxiang Sun, Weige Zhang, Jiuchun Jiang
First page: 1061
Abstract: Motor faults not only damage the motor body but also affect the entire production system. When the motor runs in a steady state, the characteristic frequency of the fault current is close to the fundamental frequency, so it is difficult to effectively extract the fault current components, such as the broken rotor bar. In this paper, according to the characteristics of electromagnetic force and vibration, when the rotor eccentricity and the broken bar occur, the vibration signal is used to analyze and diagnose the fault. Firstly, the frequency, order, and amplitude characteristics of electromagnetic force under rotor eccentricity and broken bar fault are analyzed. Then, the fault vibration acceleration value collected by a one-dimensional dilated convolution pair is extracted, and the SeLU activation function and residual connection are introduced to solve the problem of gradient disappearance and network degradation, and the fault motor model is established by combining average ensemble learning and SoftMax multi-classifier. Finally, experiments of normal rotor eccentricity and broken bar faults are carried out on 4-pole asynchronous motors. The experimental results show that the accuracy of the proposed method for motor fault detection can reach 99%, which meets the requirements of fault motor detection and is helpful for further application.
Citation: Machines
PubDate: 2023-11-29
DOI: 10.3390/machines11121061
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1062: An Experimental Investigation into the
Performance and Emission Characteristics of a Gasoline Direct Injection
Engine Fueled with Isopropanol Gasoline Blends
Authors: Simeon Iliev, Zdravko Ivanov, Radostin Dimitrov, Veselin Mihaylov, Daniel Ivanov, Stoyan Stoyanov, Slavena Atanasova
First page: 1062
Abstract: Propanol isomers, which are oxygen-rich fuels, possess superior octane ratings and energy density in comparison to methanol and ethanol. Recently, due to advancements in fermentation techniques, these propanol isomers have garnered increased interest as additives for engines. They are being explored to decrease emissions and reduce the usage of conventional fossil fuels. This study delves into this emerging field. One of the alternatives is the use of alcohol fuels in their pure state or as additives to traditional fuels. Alcohols, due to their higher volumetric energy density, are better fuels for spark ignition engines than hydrogen and biogas. Alcohol-blended fuels or alcohol fuels in their pure state may be used in gasoline engines to reduce exhaust emissions. The current research emphasizes the effect of isopropanol gasoline blends on the performance and emissions characteristics of a gasoline direct injection (GDI) engine. This investigation was conducted with different blends of isopropanol and gasoline (by volume: 10% isopropanol [IP10], 20% isopropanol [IP10], 30% isopropanol [IP30], 40% isopropanol [IP40], and 50% isopropanol [IP50]). The reviewed results showed that with increasing isopropanol in the fuel blends, engine brake power increased while BSFC decreased. In terms of emissions, with the increase in isopropanol in the fuel blends, CO and HC emissions decreased while CO2 and NOx emissions increased.
Citation: Machines
PubDate: 2023-11-29
DOI: 10.3390/machines11121062
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1063: Torque Ripple and Mass Comparison between
20 MW Rare-Earth and Ferrite Permanent Magnet Wind Generators
Authors: Trung-Kien Hoang, Lionel Vido, Celia Tchuanlong
First page: 1063
Abstract: This article investigates the comparison between two configurations of 20 MW offshore synchronous wind generators using ferrite and rare-earth permanent magnets. The optimization-based comparison concerns the torque ripple and active mass, which are two crucial criteria for offshore wind generators. Both generators adopt surface-mounted permanent magnet type with direct-drive technology to avoid problems associated with the gearboxes. The result shows that at the full-load condition, the ferrite permanent magnet generator can reduce the torque ripple to as much as 0.12%, while the rare-earth counterpart can be about 2.5 times lighter than the former one.
Citation: Machines
PubDate: 2023-11-30
DOI: 10.3390/machines11121063
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1064: 5G on the Farm: Evaluating Wireless Network
Capabilities and Needs for Agricultural Robotics
Authors: Tsvetan Zhivkov, Elizabeth I. Sklar, Duncan Botting, Simon Pearson
First page: 1064
Abstract: Global food security is a critical issue today, strained by a wide range of factors including global warming, carbon emissions, sociopolitical and economic challenges, traditional workforce decline and population growth. Technical innovations that address food security, like agricultural robotics, are gaining traction in industry settings, moving from controlled labs and experimental test facilities to real-world environments. Such technologies require sufficient network infrastructure to support in-field operations; thus, there is increased urgency to establish reliable, high-speed wireless communication networking solutions that enable deployment of autonomous agri-robots. The work presented here includes two contributions at the intersection of network infrastructure and in-field agricultural robotics. First, the physical performance of a private 5G-SA system in an agri-robotics application is evaluated and in-field experimental results are presented. These results are compared (using the same experimental setup) against public 4G and private WiFi6 (a newly emerging wireless communication standard). Second, a simulated experiment was performed to assess the “real-time” operational delay in critical tasks that may require quick turnaround between in-field robot and off-board processing. The results demonstrate that public 4G cannot be used in the agricultural domain for applications that require high throughput and reliable communication; that private 5G-SA greatly outperforms public 4G in all performance metrics (as expected); and that private WiFi6, though limited in range, is a fast and very reliable alternative in specific settings. While a single wireless solution does not currently exist for the agricultural domain, multiple technologies can be combined in a hybrid solution that meets the communications requirements.
Citation: Machines
PubDate: 2023-11-30
DOI: 10.3390/machines11121064
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1065: Load Torque Observer for BLDC Motors Based
on a HOSM Differentiator
Authors: Axel Coronado-Andrade, Alejandra de la Guerra, Luis Alvarez-Icaza
First page: 1065
Abstract: An observer is proposed for a trapezoidal brushless DC motor composed of a cascade connection of a reduced-order Luenberger observer and a high-order sliding mode (HOSM) differentiator. This configuration can estimate the angular velocity and reconstruct the load torque, key elements for the control of this type of motor, under the mild assumption that the variable load torque and its k-th time derivatives are bounded. The proposed observer was tested on an experimental test bench based on Texas Instruments (TI) High Voltage Digital Motor Control (HVMTR Kit) using a Delfino F28379D micro controller. The results show that the velocity and load torque can be properly estimated, despite the presence of noise in the current measurements.
Citation: Machines
PubDate: 2023-12-01
DOI: 10.3390/machines11121065
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 1066: Exponential Local Fisher Discriminant
Analysis with Sparse Variables Selection: A Novel Fault Diagnosis Scheme
for Industry Application
Authors: Zhengping Ding, Yingcheng Xu, Kai Zhong
First page: 1066
Abstract: Local Fisher discriminant analysis (LFDA) has been widely applied to dimensionality reduction and fault classification fields. However, it often suffers from small sample size (SSS) problem and incorporates all process variables without emphasizing the key faulty ones, thus leading to degraded fault diagnosis performance and poor model interpretability. To this end, this paper develops the sparse variables selection based exponential local Fisher discriminant analysis (SELFDA) model, which can overcome the two limitations of basic LFDA concurrently. First, the responsible faulty variables are identified automatically through the least absolute shrinkage and selection operator, and the current optimization problem are subsequently recast as an iterative convex optimization problem and solved by the minimization-maximization method. After that, the matrix exponential strategy is implemented on LFDA, it can essentially overcome the SSS problem by ensuring that the within-class scatter matrix is always full-rank, thus more practical in real industrial practices, and the margin between different categories is enlarged due to the distance diffusion mapping, which is benefit for the enhancement of classification accuracy. Finally, the Tennessee Eastman process and a real-world diesel working process are employed to validate the proposed SELFDA method, experimental results prove that the SELFDA framework is more excellent than the other approaches.
Citation: Machines
PubDate: 2023-12-01
DOI: 10.3390/machines11121066
Issue No: Vol. 11, No. 12 (2023)
- Machines, Vol. 11, Pages 982: CHBS-Net: 3D Point Cloud Segmentation
Network with Key Feature Guidance for Circular Hole Boundaries
Authors: Jiawei Zhang, Xueqi Wang, Yanzheng Li, Yinhua Liu
First page: 982
Abstract: In laser scanning inspection systems for sheet metal parts, the rapid and accurate inspection of the high-precision holes is not only crucial but difficult. The accuracy of the circular holes, especially the locating datum holes on the parts, plays an important role in the assembly quality. However, accurately segmenting the circular hole boundary points required for circular hole fitting from large-scale scanning point cloud data remains one of the most difficult tasks for inspection accuracy improvement. To address this problem, a segmentation network called the circular hole boundary segmentation network (CHBS-Net) is proposed for boundary point cloud extraction. Firstly, an encoding–decoding–attention (EDA) fusion guidance mechanism is used to address the imbalance in data distribution due to the small proportion of boundary points in the overall point cloud. Secondly, a long short-term memory (LSTM) network parallel structure is used to capture the contour continuity and temporal relationships among boundary points. Finally, the interference of neighboring points and noise is reduced by extracting features in the multi-scale neighborhood. Experiments were performed using real cases from a sheet metal parts dataset to illustrate the procedures. The results showed that the proposed method achieves better performance than the benchmark state-of-the-art methods. The circular hole inspection accuracy is effectively improved by enhancing the segmentation accuracy of the scanning boundary points.
Citation: Machines
PubDate: 2023-10-24
DOI: 10.3390/machines11110982
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 983: A Conceptual Framework for Economic Analysis
of Different Law Enforcement Drones
Authors: Nikolaos Tsiamis, Loukia Efthymiou, Konstantinos P. Tsagarakis
First page: 983
Abstract: The widespread use of drones in various fields has initiated a discussion on their cost-effectiveness and economic impact. This article analyzes in detail a methodological evaluation framework for the levelized cost of drone services for law enforcement purposes. Based on the data availability, we compared two vehicles: Phantom 4 Pro and Thunder-B. Moreover, we calculated their levelized costs per surveillance time and trip distance. Our approach helps users calculate the real costs of their vehicles’ services and produce equations for rapid estimations. We observed economies of scale for time and distance and showed differentiations per aircraft capacity. Furthermore, using the produced equations, we formulated a case study and compared the costs in a 4 km area constantly monitored by the two types of drones to support the best vehicle selection. We found that the Phantom 4 Pro costs less than the Thunder-B drone, for example. Thus, we demonstrate how, by applying this methodology beforehand, decision makers can select the most appropriate vehicle for their needs based on cost. Cost research estimations will improve UAV use and will help policymakers include UAV technology in crime prevention programs, especially when more data are available.
Citation: Machines
PubDate: 2023-10-24
DOI: 10.3390/machines11110983
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 984: A Reference Governor with Adaptive
Performance for Quadrotors under Safety Constraints
Authors: Panagiotis S. Trakas, Andreas Tantoulas, Charalampos P. Bechlioulis
First page: 984
Abstract: This paper presents a novel robust reference governor (RG) for trajectory tracking of quadrotors. The proposed scheme is characterized by low computational complexity and straightforward gain selection. Moreover, it considers safety constraints regarding speed limits and ensures the stability and the proper operation of the closed-loop system. The proposed scheme imposes user-specified performance attributes on the evolution of the tracking error when the safety constraints allow it. When these constraints are at risk of violation, the proposed RG provides a relaxation of the predefined performance specifications to ensure the stability of the plant. Lyapunov analysis proves the boundedness of the closed-loop signals, while its efficacy is further clarified and verified via extensive comparative experimental results against a well-established PI regulator.
Citation: Machines
PubDate: 2023-10-24
DOI: 10.3390/machines11110984
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 985: An Overview of Grounding Design and
Grounding Fault Detection and Location Methods for a Multiphase Rectifier
Generator Power Supply System
Authors: He Huang, Fan Ma, Lijun Fu, Wei Zhu, Chun Li
First page: 985
Abstract: A multiphase rectifier generator is important power generation equipment in DC power systems in transportation fields such as ships and aviation. Grounding design and grounding fault detection and positioning are key technologies for the safe operation of the power system. This article aims to systematically elaborate on the current research status of its related content. Firstly, the topological structure characteristics of the multiphase rectifier generator power supply system are introduced, the advantages and disadvantages of different grounding methods, position selection, and other design schemes are analyzed, and reasonable suggestions are given for the selection of grounding resistance. Secondly, a brief introduction is given to the research progress on ground fault detection and location in the medium voltage DC integrated power systems of ships, and the characteristics of typical methods for ground fault detection and location in DC power grid systems are summarized. Finally, the future research directions are outlined.
Citation: Machines
PubDate: 2023-10-24
DOI: 10.3390/machines11110985
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 986: Study on the Coupling Relationship between
Wear and Dynamics in Planetary Gear Systems
Authors: Jun Chen, Ning Dong, Jiahua Min
First page: 986
Abstract: The occurrence of wear is hard to avoid in gear systems because of their transmission principle. Wear will lead to a deviation of the system’s performance from the design objectives or even failure. In this paper, a dynamic wear prediction model considering the friction and wear of all meshing gears is proposed for planetary gear systems. The differences between different wear prediction methods are compared. The interactions among the wear, the dynamic response, and the uniform load performance of the planetary gears are investigated. The results show that considering friction and wear on all tooth surfaces can significantly reduce errors in the simulation. Wear mainly affects meshing stiffness in the double tooth contact region. The degree of fluctuation of stiffness and meshing force increases significantly with wear. The load-sharing factor in the dedendum and addendum regions decreases. Accordingly, the position of maximum wear on the tooth surface moves slowly towards the pitch line. Early wear improves the dynamic performance of the system. As the wear deteriorates, the higher harmonics of the meshing frequency increase significantly. The uniform load performance of planet gears exhibits the same trend of dynamic response as the others during the wear process.
Citation: Machines
PubDate: 2023-10-25
DOI: 10.3390/machines11110986
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 987: Influence of Piston Mass and Working
Pressure on the Impact Performance of a Hydraulic Rock Drill Using the
Stress Wave Method
Authors: Zhenyi Yang, Jun Li, Guoyan Yu
First page: 987
Abstract: To optimize and improve the impact performance of a hydraulic rock drill, it is helpful to test the stress waves of the drill and analyze the impact energy, impact frequency, and energy utilization rate. For this study, a stress wave test bench was designed and built, according to international standards, in order to study the impact process of a hydraulic rock drill under the working pressures of 18 MPa and 23 MPa. The impact energy, impact frequency, and energy utilization rate of two different hydraulic rock drill pistons in low, middle, and high gear were analyzed using a control variable method. The results demonstrate that the impact stress waves of the rock drill periodically occur in the drill rod, and then decay exponentially until they become close to zero. Moreover, the amplitude of the incident stress wave determines the rock-breaking ability of the drill. The impact energy of the short piston is greater than that of the long piston, with a maximum average value of 346.1 J; the impact frequency of the long piston is higher than that of the short piston, with a maximum average value of 62 Hz; and the energy utilization rate of the short piston is higher than that of the long piston, with a maximum average value of 56.92%, which is close to the theoretical ideal efficiency. Therefore, it can be concluded that the impact performance of a hydraulic rock drill can be effectively tested using the proposed horizontal bench, and that piston characteristics and the working pressure are the main factors affecting impact performance. Accordingly, when developing a hydraulic rock drill, it is advisable to select a shorter piston and a higher working pressure, thus allowing the drill to provide good impact performance.
Citation: Machines
PubDate: 2023-10-25
DOI: 10.3390/machines11110987
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 988: Design and Analysis of an Input–Output
Linearization-Based Trajectory Tracking Controller for Skid-Steering
Mobile Robots
Authors: Javier Moreno, Emanuel Slawiñski, Fernando A. Chicaiza, Francisco G. Rossomando, Vicente Mut, Marco A. Morán
First page: 988
Abstract: This manuscript presents a control law based on the kinematic control concept and the input–output linearization approach. More specifically, the given approach has the structure of a two-loop controller. A rigorous closed-loop system analysis is presented by using known theory on perturbed systems. By assuming that the desired velocity in the body frame is persistently exciting, the uniform bound of the tracking error in earth coordinates is ensured. A simulation study using practical mobile robot parameters shows the viability of the introduced approach. In addition, two known trajectory tracking controllers are simulated in order to compare the performance of the proposed technique. Better tracking accuracy is obtained with the proposed control approach, even if uncertainties in the knowledge of the friction coefficients are presented.
Citation: Machines
PubDate: 2023-10-25
DOI: 10.3390/machines11110988
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 989: Evaluation of Counter-Rotating Dual-Rotor
Permanent-Magnet Flux-Switching Machine with Series and Parallel Stator
Teeth
Authors: Wasiq Ullah, Faisal Khan, Udochukwu Bola Akuru, Bakhtiar Khan, Salar Ahmad Khalil
First page: 989
Abstract: In this study, the focus is on the magnetic path formation and its effects on the performance of a counter-rotating dual-rotor permanent-magnet flux-switching machine (CR-DRPMFSM) for direct-drive counter-rotating wind power generation, based on different stator slot and rotor pole combinations. To fully exploit rotor-shaft bore and improve fault-tolerant design, as well as increase torque density, dual-rotor topologies with the capability for dual electrical and dual mechanical ports are investigated. Moreover, the direct-drive counter-rotating wind power generation technique offers a brushless topology, thus reducing maintenance cost and improving energy conversion efficiency compared to single-blade wind turbine systems. Using finite element analysis (FEA), the inherent magnetic coupling of the series and parallel paths shows varied impacts on the electromagnetic performance of four different CR-DRPMFSMs based on the slot/pole combinations (MI to MIV) considered in this study. The key electromagnetic performance indices, such as torque, cogging torque, torque ripple, power factor, and efficiency, show proportionate variation to the coupling level. A comparative analysis shows that MI exhibits higher average torque, lower torque ripples, and high efficiency, reaching 90% with a power factor of 0.6. As an optimal design, an MI test prototype is developed. The experimental test prototype validates the FEA results under no-load and on-load conditions.
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110989
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 990: Estimation Technique for IGBT Module
Junction Temperature in a High-Power Density Inverter
Authors: Ahmed H. Okilly, Seungdeog Choi, Sangshin Kwak, Namhun Kim, Jonghyuk Lee, Hyoungjun Moon, Jeihoon Baek
First page: 990
Abstract: During the last few decades, insulated-gate bipolar transistor (IGBT) power modules have evolved as reliable and useful electronic parts due to the increasing relevance of power inverters in power infrastructure, reliability enhancement, and long-life operation. Excessive temperature stresses caused by excessive power losses frequently cause high-power-density IGBT modules to fail. As a result, module temperature monitoring techniques are critical in designing and selecting IGBT modules for high-power-density applications to guarantee that temperature stresses in the various module components remain within the rated values. In this paper, a module’s different losses were estimated, a heating pipe system for the thermal power cycling technique was proposed, and finite element method (FEM) thermal modeling and module temperature measurement were performed using ANSYS Icepak software version 2022 R1 to determine whether the IGBT module’s temperature rise was within acceptable bounds. To test the proposed technique, a proposed design structure of the practical railway application with a 3.3 MW traction inverter is introduced using commercialized IGBT modules from Semikron company with maximum temperature of about 150 °C. the FEM analysis results showed that the maximum junction temperature is about 109 °C which is in acceptable ranges, confirming the appropriate selection of the employed IGBT module for the target application.
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110990
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 991: High-Speed Design with Separated Tapering
for Reducing Cogging Torque and Torque Ripple of a 3 kW Dry Vacuum Pump
Motor for the ETCH Process
Authors: Do-Hyeon Choi, In-Jun Yang, Min-Ki Hong, Dong-Hoon Jung, Won-Ho Kim
First page: 991
Abstract: This paper proposes a design method to reduce cogging torque and torque ripple in the concentrated winding of IPMSMs (Interior Permanent Magnet Synchronous Motors) used in motors for the semiconductor ETCH process. IPMSMs can utilize reluctance torque through the difference in inductance between the d axis and q axis, but they are at a disadvantage in terms of reducing cogging torque while tapering the rotor and stator to reduce torque ripple. In addition, the existing single tapering can push the permanent magnets into the rotor. If the rotor’s permanent magnets are embedded, the magnetic reluctance will increase, and the overall performance of the motor will decrease. However, an optimum design method was derived in which the magnets do not move during rotor tapering. This geometric design is an optimum design method that reduces cogging torque and torque ripple. This paper compares and analyzes four models, the concentrated winding model, distributed winding model, conventional tapering model, and separated tapering model, using 2D and 3D finite element analysis (FEA).
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110991
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 992: A Novel Fully Automatic Concept to Produce
First Subset of Bowden Cables, Improving Productivity, Flexibility, and
Safety
Authors: Eduardo Eiras, Francisco J. G. Silva, Raul D. S. G. Campilho, Rita C. M. Sales-Contini, André F. V. Pedroso, Naiara P. V. Sebbe
First page: 992
Abstract: With a view to maintaining the competitiveness required by the market, the automotive industry strongly encourages its suppliers to develop new production methods and technologies capable of reducing the costs of produced products, ensuring the necessary quality, and increasing flexibility, with a view to responding more easily to the customization of the products that the market increasingly demands. The main goal of this work was to increase the flexibility and productivity of equipment capable of producing the first subset that constitutes the product commonly known as the Bowden cable. To this end, the design science research methodology was used, which was understood as the most effective in describing scientific work related to the improvement of existing systems. Bowden cables are cables that activate various devices in the car, such as opening doors, moving window glasses, and adjusting some car seats, among others. The work consisted of integrating several operations usually carried out for the manufacture of the referred subset, reducing logistics operations and manual work, increasing operator safety, and increasing the production rate and flexibility of the equipment, by reducing the setup time. For this purpose, new mechanical concepts were developed, and automation was applied, which resulted in a completely new concept, able to fulfill all the objectives initially set. It should be noted here that the new equipment allowed a production rate of 1140 p/h, when the initial objective was 1100 p/h; it requires an investment of only around EUR 55,000 (easy return on investment), occupies only 11.6 m2, and has reinforced safety systems to avoid workers’ injuries, an aspect that is very important in this type of equipment, where operators deal with cutting systems and high temperatures. The dissemination of this concept could help other researchers to easily find solutions to certain problems that they face in the development of modern equipment. The main contributions of this paper are the novel concepts created to overcome some process difficulties, which can be used for a wide range of other processing situations with similar difficulties. The solutions proposed allow a decrease in the cycle time, present high flexibility, save workshop space, and are affordable in terms of global cost.
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110992
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 993: A Novel Tribometer and a Comprehensive
Testing Method for Rolling-Sliding Conditions
Authors: Pedro Amoroso, Aleks Vrček, Matthijn de Rooij
First page: 993
Abstract: This study introduces a method based on fine torque control to evaluate traction in rolling—liding line contacts under small slide-to-roll ratios (SRRs). To accomplish this, we engineered an innovative testing machine—a two-roller tribometer capable of precisely applying resisting torques to one of the rollers. Two types of tests were designed and conducted to validate our method and showcase the capabilities of the novel test setup. The first type, named the “Traction Decay Test”, proved to be effective in evaluating changes in the SRR over time. The second, named the “Torque-Mode Traction Test”, demonstrated its effectiveness in achieving ultra-low SRRs, in the order of 0.01%. As a result, traction curves with high resolution in the low SRR domain were constructed. This advancement provides the means for gaining a deeper understanding of traction coefficients, wear behavior, and tribological performance at ultra-low SRRs across diverse applications.
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110993
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 994: A Thermomechanical Model for Time-Varying
Deformations of Spigot Interference Connection under Shrink-Fitting
Assembly
Authors: Junjiang Zhu, Bo Yuan, Yibai Mao, Ping Zhou, Qingchao Sun, Guozhen Fang
First page: 994
Abstract: The interference fit connection structure is widely employed in various industries. Different from the conventional connection structure, the aero-engine rotor connection has a spigot-bolt structure. The quality of the shrink-fitting assembly process directly affects the final assembly performance of the rotor. The complexity of the connection structure and the time-varying thermal deformation bring great challenges in analyzing the formation patterns of connection performance. However, existing methods of interference connection analysis are mainly used in the wide range of interference connection structures, which makes them difficult to apply in low height interference connection for aero-engine rotors. This paper introduces a thermomechanical interference fit pressure model. First, a theoretical model for interference fit pressure considering temperature-variable loads is established to obtain the time-varying pressure during the assembly process. Second, a finite element model is established to explore the influence of interference and temperature loads on the spigot pressure and the mounting edge deformation. Finally, the experiments validate the accuracy of both the theoretical model and the finite element analysis. The results indicate that during the shrink-fitting assembly process, the interference fit pressure exhibits a nonlinear evolution trend, and the warping deformation of the mounting edge is a result of the combined influence of temperature and interference fit pressure. The law found in this paper has an application prospect in the process parameter setting of shrink-fitting assembly for special structures.
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110994
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 995: Development of a Neuroergonomic Assessment
for the Evaluation of Mental Workload in an Industrial Human–Robot
Interaction Assembly Task: A Comparative Case Study
Authors: Carlo Caiazzo, Marija Savkovic, Milos Pusica, Djordje Milojevic, Maria Chiara Leva, Marko Djapan
First page: 995
Abstract: The disruptive deployment of collaborative robots, named cobots, in Industry 5.0 has brought attention to the safety and ergonomic aspects of industrial human–robot interaction (HRI) tasks. In particular, the study of the operator’s mental workload in HRI activities has been the research object of a new branch of ergonomics, called neuroergonomics, to improve the operator’s wellbeing and the efficiency of the system. This study shows the development of a combinative assessment for the evaluation of mental workload in a comparative analysis of two assembly task scenarios, without and with robot interaction. The evaluation of mental workload is achieved through a combination of subjective (NASA TLX) and real-time objective measurements. This latter measurement is found using an innovative electroencephalogram (EEG) device and the characterization of the cognitive workload through the brainwave power ratio β/α, defined after the pre-processing phase of EEG data. Finally, observational analyses are considered regarding the task performance of the two scenarios. The statistical analyses show how significantly the mental workload diminution and a higher level of performance, as the number of components assembled correctly by the participants, are achieved in the scenario with the robot.
Citation: Machines
PubDate: 2023-10-26
DOI: 10.3390/machines11110995
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 996: Design and Implementation of
Hardware-in-the-Loop Simulation Environment Using System Identification
Method for Independent Rear Wheel Steering System
Authors: Chulwoo Moon
First page: 996
Abstract: In the automotive field, with the advancement of electronic and signal processing technologies, active control-based chassis systems have been developed to enhance vehicle stability. In this study, a Hardware-in-the-Loop (HiL) simulation environment was developed to effectively improve time and cost during the development process of an independent rear-wheel steering system. The HiL Simulation Environment was developed—a specific test bench capable of simulating driving loads on the prototype. Based on the system identification method, a reaction force modeling technique for the target driving loads was proposed. The full vehicle dynamics simulation model was developed with a lateral maximum error of 4.5% and a correlation coefficient of 0.98, as well as a longitudinal maximum error of 0.1% and a correlation coefficient of 0.99. The reaction force generation system had a maximum error of 2.9%. Using the developed HiL simulation environment, performance verification and analysis of the independent rear-wheel steering system were conducted, showing reductions of 5.1% in lateral acceleration and 5.2% in yaw rate.
Citation: Machines
PubDate: 2023-10-27
DOI: 10.3390/machines11110996
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 997: Towards Robust and Effective Passive
Compliance Design of End-Effectors for Robotic Train Fluid Servicing
Authors: Kourosh Eshraghi, Mingfeng Wang, Cristinel Mares
First page: 997
Abstract: Without mechanical compliance robots rely on controlled environments and precision equipment to avoid clashes and large contact forces when interacting with an external workpiece, e.g., a peg-in-hole (PiH) task. In such cases, passive compliance devices are used to reduce the insertion force (and in turn the robot payload) while guiding corrective motions. Previous studies in this field are limited to small misalignments and basic PiH geometries inapplicable to prevalent robotic and autonomous systems (RASs). In addition to these issues, our work argues that there is a lack of a unified approach to the development of passive compliance systems. To this end, we propose a higher-level design approach using robust engineering design (RED) methods. In a case study, we demonstrated this general approach with a Taguchi design framework, developing a remote centre compliant (RCC) end-effector for robotic train fluid servicing. For this specific problem, a pseudo-rigid-body model (PRBM) is suggested in order to save enormous computation time in design, modelling, and optimisation. Our results show that the compliant end-effector is capable of significantly reducing the insertion force for large misalignments up to 15 mm and 6 degrees.
Citation: Machines
PubDate: 2023-10-27
DOI: 10.3390/machines11110997
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 998: Power Generation Enhancement of Horizontal
Axis Wind Turbines Using Bioinspired Airfoils: A CFD Study
Authors: Hamid R. Kaviani, Mohammad Moshfeghi
First page: 998
Abstract: This research investigates the performance implications of employing a bioinspired airfoil (seagull’s wing cross-section) in horizontal-axis wind turbines. Specifically, we replaced the S809 airfoil from NREL Phase VI with an airfoil modeled after seagull wings. Initially, we calibrated four coefficients of the GEKO turbulence model for both the S809 and the bioinspired airfoil, utilizing experimental data. Subsequently, using the calibrated generalized k-ω (GEKO) model, we conducted a comparative analysis between the S809 and the seagull airfoils, revealing the considerable superiority of the seagull airfoil in terms of lift and drag coefficients. Furthermore, we numerically simulated the original NREL Phase VI turbine and a modified version where the S809 airfoil was replaced with the seagull airfoil using 3D computational fluid dynamics (CFD) with the airfoil-based-calibrated GEKO turbulence model. This investigation spanned a wide range of air speeds, including 7 m/s, 13 m/s, and 25 m/s. At these wind speeds, we observed a substantial increase in turbine power generation, with enhancements of 47.2%, 204.4%, and 103.9%, respectively. This study underscores the significant influence of nature’s designs in advancing energy extraction within industries, particularly within the wind energy sector.
Citation: Machines
PubDate: 2023-10-30
DOI: 10.3390/machines11110998
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 999: Feature-Based Bearing Fault Classification
Using Taylor–Fourier Transform
Authors: Gerardo Avalos-Almazan, Sarahi Aguayo-Tapia, Jose de Jesus Rangel-Magdaleno, Mario R. Arrieta-Paternina
First page: 999
Abstract: This paper proposes a feature-based methodology for early bearing fault detection and classification in induction motors through current signals using the digital Taylor–Fourier transform (DTFT) and statistical methods. The DTFT allows the application of narrow bandwidth digital filters located in the spurious current signal components, wherewith it is possible to gain information to detect bearing issues and classify them using statistical methods. The methodology was implemented in MATLAB using the digital Taylor–Fourier transform for three fault types (bearing ball damage, outer-race damage, and corrosion damage) at different powering conditions: power grid source at 60 Hz and adjustable speed drive applied (60 Hz, 50 Hz, 40 Hz, 30 Hz, 20 Hz, and 10 Hz) in loading and unloading conditions. Results demonstrate a classification accuracy between 93–99% for bearing ball damage, 91–99% for outer-race damage, and 94–99% for corrosion damage.
Citation: Machines
PubDate: 2023-10-29
DOI: 10.3390/machines11110999
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1000: A New Exoskeleton Prototype for Lower Limb
Rehabilitation
Authors: Ionut Geonea, Cristian Copilusi, Sorin Dumitru, Alexandru Margine, Adrian Rosca, Daniela Tarnita
First page: 1000
Abstract: This paper presents a new solution for an exoskeleton robotic system that is used for locomotor assistance in people with locomotor disabilities. As novel features of the present research, a novel structural solution of a plane-parallel kinematic chain, intended to be used as the leg of an exoskeleton robot, is proposed. A virtual prototype is made, on the basis of which kinematic and dynamic studies are carried out using ADAMS software for the dynamic analysis of multibody systems. The dynamic simulation of the exoskeleton is performed in two simulation situations: walking on a horizontal plane, as well as the simulation of motion assistance when climbing stairs. Following this analysis, it is noted that the robotic system achieves angular variations in the hip and knee joints similar to that of a human subject. As a result, the constructive solution is feasible, and the next stage of the study is to realize an experimental prototype by the rapid prototyping technique. The kinematic elements of the exoskeleton are designed to provide structural strength, to be easily manufactured by 3D printing and to be easy to assemble. For this purpose, the structural optimization is performed with the finite element method to eliminate stress concentrators. Finally, an experimental prototype of the exoskeleton robot is manufactured and assembled, whose motion is analyzed using ultrafast-camera-based video analysis.
Citation: Machines
PubDate: 2023-10-30
DOI: 10.3390/machines11111000
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1001: Precision Face Milling of Maraging Steel
350: An Experimental Investigation and Optimization Using Different
Machine Learning Techniques
Authors: Adel T. Abbas, Mohamed O. Helmy, Abdulhamid A. Al-Abduljabbar, Mahmoud S. Soliman, Ali S. Hasan, Ahmed Elkaseer
First page: 1001
Abstract: Maraging steel, characterized by its superior strength-to-weight ratio, wear resistance, and pressure tolerance, is a material of choice in critical applications, including aerospace and automotive components. However, the machining of this material presents significant challenges due to its inherent properties. This study comprehensively examines the impacts of face milling variables on maraging steel’s surface quality, cutting temperature, energy consumption, and material removal rate (MRR). An experimental analysis was conducted, and the gathered data were utilized for training and testing five machine learning (ML) models: support vector machine (SVM), K-nearest neighbor (KNN), artificial neural network (ANN), random forest, and XGBoost. Each model aimed to predict the outcomes of different machining parameters efficiently. XGBoost emerged as the most effective, delivering an impressive 98% prediction accuracy across small datasets. The study extended into applying a genetic algorithm (GA) for optimizing XGBoost’s hyperparameters, further enhancing the model’s predictive accuracy. The GA was instrumental in multi-objective optimization, considering various responses, including surface roughness and energy consumption. The optimization process evaluated different weighting methods, including equal weights and weights derived from the analytic hierarchy process (AHP) based on expert insights. The findings indicate that the refined XGBoost model, augmented by GA-optimized hyperparameters, provides highly accurate predictions for machining parameters. This outcome holds significant implications for industries engaged in the machining of maraging steel, offering a pathway to optimized operational efficiency, reduced costs, and enhanced product quality amid the material’s machining challenges.
Citation: Machines
PubDate: 2023-10-30
DOI: 10.3390/machines11111001
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1002: EHB Gear-Drive Symmetric Dead-Zone
Finite-Time Adaptive Control
Authors: Shuai Wang, Qinghua Cao, Fukuo Ma, Jian Wu
First page: 1002
Abstract: Intelligent driving vehicles require more accurate and stable braking control. Electrohydraulic braking (EHB) systems can better adapt to the development of autonomous driving technology. The gear transmission system plays a crucial role in EHB deceleration and torque increase mechanisms. However, its dead-zone nonlinearity poses challenges for EHB control. To address the position-control problem in the EHB gear transmission system, we propose a finite-time adaptive control method for the symmetrical dead zone. This approach combines adaptive control theory with finite-time control theory and designs parameter-updating laws for the unknown parameters in the system. Boundary estimates are introduced into the parameter-update laws and control laws to compensate for unknown disturbances. By adjusting the relevant parameters, the convergence rate can be improved, ensuring that errors converge within a specified range within a limited time. After modifying the parameter-updating laws and control laws, all closed-loop signals remain bounded. Finally, we validate the proposed control strategy through simulation and hardware-in-the-loop (HIL) testing. The results demonstrate that the control strategy developed in this study achieves high tracking accuracy and stability even in the presence of dead zones, unknown parameters, and unknown interferences in the EHB gear-drive servo system.
Citation: Machines
PubDate: 2023-10-30
DOI: 10.3390/machines11111002
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1003: Study on Single-Phase BLDC Motor Design
through Drive IC Integration Analysis
Authors: Ye-Seo Lee, Na-Rim Jo, Hyun-Jo Pyo, Dong-Hoon Jung, Won-Ho Kim
First page: 1003
Abstract: In this paper, a single-phase BLDC motor is applied to a cooling fan motor and a Drive IC integration analysis method of the single-phase BLDC motor is proposed. Single-phase BLDC motors have a simple structure, are easy to manufacture, and are low cost, so they are used in applications where low outputs and low costs are advantageous. Single-phase BLDC motors use a full-bridge inverter (Drive IC), and this inverter (Drive IC) has dead time due to switching. Therefore, in order to consider dead time when analyzing a single-phase BLDC motor, analysis through integration with Drive IC is necessary. This paper compares the types of single-phase BLDC motors, designs a model that satisfies target performance, and conducts research on Drive IC integration analysis through FEA. A prototype motor was manufactured and tested, and the validity of the Drive IC integration analysis was verified.
Citation: Machines
PubDate: 2023-11-01
DOI: 10.3390/machines11111003
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1004: An Improved Denoising Method for Fault
Vibration Signals of Wind Turbine Gearbox Bearings
Authors: Chaohai Zhang, Xu Zhang, Zufeng Xu, Wei Dai, Jie Lu
First page: 1004
Abstract: Vibration monitoring (VM) is an important tool for fault diagnosis in key components of wind turbine gearboxes (WTGs). However, due to the influence of white noise and random interference, it is difficult to realize high-quality denoising of WTG-VM signals. To overcome this limitation, a novel joint denoising method for fault WTG-VM signals is proposed in this article, which we have named EWTKC-SVD. First, the empirical wavelet transform (EWT) boundary exploration method is used to optimize frequency band allocation and obtain the multiple intrinsic mode functions (IMFs). Second, the sensitive IMFs are selected according to the calculated correlation coefficient and kurtosis index, avoiding IMF redundancy. Finally, the fault WTG-VM signals are obtained using SVD denoising. Using this approach, the proposed method realizes high-quality denoising of WTG-VM signals. Furthermore, it also effectively solves the existing problems of conventional methods, namely, inefficient IMF selection, high noise, false frequencies, mode mixing, and end effect. Finally, the effectiveness, superiority, and reliability of the proposed method are proved using simulation and practical case results.
Citation: Machines
PubDate: 2023-11-01
DOI: 10.3390/machines11111004
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1005: Knowledge Graph-Embedded
Time-Serial-Data-Driven Bottleneck Analysis of Textile and Apparel
Production Processes
Authors: Guodong Wang, Guohua Liu, Qianqian Li
First page: 1005
Abstract: There is a lack of high correlation and reuse potential among multiple manufacturing data for textiles and apparel. Moreover, the material flow traceability between production workstations is not clear, making it difficult to detect potential production bottlenecks. This paper proposes a knowledge graph embedded time serial data-driven bottleneck analysis of textile and apparel production processes. Firstly, a dynamic information association model is established to organize global manufacturing information, including the static data and time-series data features. Also, a textile-corpus-oriented knowledge extraction model is designed to construct a time-series knowledge graph for textile and apparel production (TKG4TA). Then, a temporal knowledge-driven production process bottleneck prediction model is presented based on manufacturing knowledge in the textile and apparel industry. Of these, textile knowledge is transformed into embeddings using a graph convolutional network (GCN). In turn, the context-associated information features are learned by the long short-term memory (LSTM) to predict the bottlenecks in the textile and apparel production process. Finally, a typical process flow in a shirt manufacturing workshop is used as a case study. It shows that the F1 value of the proposed method for named entity recognition and relationship extraction is up to 80.3%, and 50.6%, respectively. The performance of the proposed model for bottleneck prediction is improved by 8.2% and 14.92% compared to only the use of GCN or LSTM in the mean absolute error. This model may provide a solid foundation for the temporal knowledge-graph-driven bottleneck analysis of shirt manufacturing.
Citation: Machines
PubDate: 2023-11-02
DOI: 10.3390/machines11111005
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1006: Enhanced Output Tracking Control for Direct
Current Electric Motor Systems Using Bio-Inspired Optimization
Authors: Hugo Yañez-Badillo, Francisco Beltran-Carbajal, Ivan Rivas-Cambero, Antonio Favela-Contreras, Jose Humberto Arroyo-Nuñez, Juan Nabor Balderas-Gutierrez
First page: 1006
Abstract: In this paper, an efficient output reference trajectory tracking control scheme for direct current electric motor systems based on bio-inspired optimization is proposed. The differential flatness structural property of the electric motor along with dynamic tracking error compensation is suitably exploited for the backstepping control design. Off-line optimal selection of control parameters, implementing bio-inspired ant colony and particle swarm optimization algorithms, is addressed by minimizing an objective function where the decision variables are the tracking error and control input effort. A novel adaptive version of the control approach based on B-spline artificial neural networks is provided as well. The introduced flat output feedback tracking control design approach can be further extended for other differentially flat dynamic systems. Considerably perturbed, diverse velocity and position reference trajectory tracking scenarios are developed for demonstrating the acceptable closed-loop system performance. The results prove the efficient and robust tracking of the position and velocity reference profiles planned for the operation of the controlled electric motor system under variable torque disturbances using bio-inspired optimization.
Citation: Machines
PubDate: 2023-11-02
DOI: 10.3390/machines11111006
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1007: Numerical Study on Aerodynamic and Noise
Responses of Rotor with Ramp Increase in Collective Pitch Based on
Time-Accurate Free-Wake Method
Authors: Zhiyuan Hu, Runze Xia, Yongjie Shi, Guohua Xu
First page: 1007
Abstract: Research on helicopter transient maneuvering flight noise is a hotspot and challenging topic in the fields of helicopter design and application. A new time-accurate free-wake (TAFW) method and the Fowcs Williams-Hawkings (FW–H) equations are applied to analyze the aerodynamic and noise responses of a rotor subjected to a ramp increase in collective pitch, in hover, and in forward flight. First, a TAFW algorithm suitable for rotor aerodynamic simulation in steady-state flight and transient maneuvers is developed using modified third-order upwind backward differentiation formulas. Then, to verify the effectiveness and accuracy of the proposed method, various parameters are calculated for two scenarios and compared with corresponding results from experiments by the University of Maryland: the Langley 2MRTS rotor and the NACA rotor with ramp increases in collective pitch. Finally, the influence of collective pitch increase rate, the total increase of collective pitch, and the start and stop azimuth of ramp increase on the aerodynamic and loading noise responses of the rotor are analyzed in hover and forward flight conditions. The results show the ramp increase in collective pitch will affect the loading noise in three timescales: short-term, medium-term, and long-term. The change of the loading noise is greater when the collective pitch increase rate is greater, and the start and stop azimuth angles of the ramp increase are also important factors affecting the aerodynamic load distribution and directionality of the noise.
Citation: Machines
PubDate: 2023-11-03
DOI: 10.3390/machines11111007
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1008: Parametric Analysis of Tool Wear, Surface
Roughness and Energy Consumption during Turning of Inconel 718 under Dry,
Wet and MQL Conditions
Authors: M. Zeeshan Siddique, Muhammad Iftikhar Faraz, Shahid Ikramullah Butt, Rehan Khan, Jana Petru, Syed Husain Imran Jaffery, Muhammad Ali Khan, Abdul Malik Tahir
First page: 1008
Abstract: Economy and productivity are the two most important elements of modern manufacturing systems. Economy is associated with energy-efficient operations, which results in an overall high input-to-output ratio, while productivity is related to quality and quantity. This specific work presents experimental investigations of the use of cooling conditions (dry, MQL and wet) as input variables alongside other input parameters, including depth of cut, feed and cutting speed. This research aimed to investigate the variation in output responses including tool wear, specific cutting energy, and surface roughness while machining Inconel 718, a nickel-based super alloy. For experimentation, three levels of depth of cut, feed, and cutting speed were chosen. The Taguchi method was used for the experimental design. The contribution ratio of each input parameter was ascertained through analysis of variance (ANOVA). Use of coolant showed a positive effect on process parameters, particularly MQL. By adapting the optimum machining conditions, specific cutting energy was improved by 27%, whereas surface roughness and tool wear were improved by 15% and 30%, respectively.
Citation: Machines
PubDate: 2023-11-03
DOI: 10.3390/machines11111008
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1009: Analysis of the Electrical Impedance of
Graphite and Silver Graphite Carbon Brushes for Use in the Impedance
Measurement of Sensory Utilizable Machine Elements
Authors: Maximilian Hausmann, Tobias Schirra, Eckhard Kirchner
First page: 1009
Abstract: The ongoing digitalization of processes and products in mechanical engineering is accompanied by an increasing demand for data. In order to provide this data, technical systems are being extended with sensory functions. To supply those sensory functions on rotating elements—such as shafts—with electrical energy, and to be able to transmit signals out of the system, sliding contacts can be used as a cost-effective and established solution. However, if electrical properties of machine elements are utilized for sensing purposes, such as condition monitoring of rolling element bearings by means of impedance measurement, sliding contacts are directly in the measurement path and can thus influence the measured impedance. The aim of this paper is to analyze the impedance of graphite and silver graphite carbon brushes under different rotational speeds, in different positions, and with different carrier frequencies. The material of the carbon brushes as well as the position have significant effects on the impedance behavior. Furthermore, carbon brushes show a significant running-in behavior. The results are discussed, and indications for use in impedance measurements are given. Silver graphite carbon brushes in axial positioning are particularly suitable for impedance measurements of sensory utilizable machine elements. Sufficient running-in time must be considered.
Citation: Machines
PubDate: 2023-11-03
DOI: 10.3390/machines11111009
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1010: The Influence of Internally Cooled Drill
Bits on Cutting Dynamics and Workpiece Hardness Monitoring in Stone
Machining
Authors: Miho Klaić, Danko Brezak, Matej Šlankovič, Tomislav Staroveški
First page: 1010
Abstract: Drill bits with internal cooling capabilities are still not employed in stone machining practices within shop floor environments. Therefore, a conventional industrial drill bit used in stone machining was subject to a redesign wherein an axial cooling channel was machined throughout its body. A comparison was drawn between the standard drill bit without cooling capabilities and the redesigned drill bit, which used compressed air as a cooling medium. The experiment was performed by drilling three types of stone samples varying in hardness with nine combinations of cutting speed and feed rate. During the machining process, two types of process signals were continuously measured—namely, cutting forces and vibrations. Additionally, the cutting edges of the drill bits were inspected after a specific number of drilling cycles using a vision system. The primary objective of this study was to compare the cutting forces and tool wear dynamics achieved by those two drill bits. Furthermore, the usage of vibration signals in the classification of stone hardness during machining with an internally cooled drill bit was additionally analyzed. The results of this study unveiled improvement in minimizing cutting forces, vibrations, and the intensity of tool wear when utilizing an internally cooled drill bit. Even though the machining system generally exhibited lower vibrations, vibration signals again demonstrated commendable efficacy in classifying stone hardness.
Citation: Machines
PubDate: 2023-11-05
DOI: 10.3390/machines11111010
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1011: Industrial Process Improvement by
Automation and Robotics
Authors: Raul D. S. G. Campilho, Francisco J. G. Silva
First page: 1011
Abstract: Automation and robotics have revolutionized industrial processes, making them more efficient, precise, and flexible [...]
Citation: Machines
PubDate: 2023-11-06
DOI: 10.3390/machines11111011
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1012: Multi-Stage Approach Using Convolutional
Triplet Network and Ensemble Model for Fault Diagnosis in Oil Plant Rotary
Machines
Authors: Seungjoo Lee, YoungSeok Kim, Hyun-Jun Choi, Bongjun Ji
First page: 1012
Abstract: Ensuring the operational safety and reliability of rotary machinery systems, especially in oil plants, has become a focal point in both academic and industry arenas. Specifically, in terms of key rotary machinery components such as shafts, the diagnosis of these systems is paramount for achieving enhanced generalization capabilities in fault diagnosis, encompassing multiple sensor-derived variables with their respective fault patterns. This study introduces a multi-stage approach to generalize capabilities for fault diagnosis that considers multiple sensor-derived variables and their fault patterns. This method combines the Convolutional Triplet Network for feature extraction with an ensemble model for fault classification. Initially, vibration signals are processed to yield the most representative temporal and spatial features. Then, an ensemble approach is used to maximize both diversity and accuracy by balancing the contributions of the individual classifiers. The approach can detect three representative types of shaft faults more accurately than traditional single-stage machine learning models. Comprehensive experiments, detailed within, showcase the method’s efficacy in diagnosing rotary machine faults across diverse operational scenarios.
Citation: Machines
PubDate: 2023-11-06
DOI: 10.3390/machines11111012
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1013: Deformable Object Manipulation in
Caregiving Scenarios: A Review
Authors: Liman Wang, Jihong Zhu
First page: 1013
Abstract: This paper reviews the robotic manipulation of deformable objects in caregiving scenarios. Deformable objects like clothing, food, and medical supplies are ubiquitous in care tasks, yet pose modeling, control, and sensing challenges. This paper categorises caregiving deformable objects and analyses their distinct properties influencing manipulation. Key sections examine progress in simulation, perception, planning, control, and system designs for deformable object manipulation, along with end-to-end deep learning’s potential. Hybrid analytical data-driven modeling shows promise. While laboratory successes have been achieved, real-world caregiving applications lag behind. Enhancing safety, speed, generalisation, and human compatibility is crucial for adoption. The review synthesises critical technologies, capabilities, and limitations, while also pointing to open challenges in deformable object manipulation for robotic caregiving. It provides a comprehensive reference for researchers tackling this socially valuable domain. In conclusion, multi-disciplinary innovations combining analytical and data-driven methods are needed to advance real-world robot performance and safety in deformable object manipulation for patient care.
Citation: Machines
PubDate: 2023-11-07
DOI: 10.3390/machines11111013
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1014: Impact of Grid-Connected Inverter
Parameters on the Supraharmonic Emissions in Distributed Power Generation
Systems
Authors: Abdellatif M. Aboutaleb, Jan Desmet, Jos Knockaert
First page: 1014
Abstract: In this paper, a mathematical analysis is presented to show the effect of grid-connected inverter (GCI) parameters on its emissions in the supraharmonic range. This analysis is extended to explain the effect of asymmetry on the emissions of parallel-connected GCIs on distributed power generation systems. The switching harmonics of a GCI appear as bands around the switching frequency and its multiples. A MATLAB/Simulink model is built to perform two studies. In the first study, we use one GCI to examine the effect of the parameters on the emissions, while in the second study, we examine the effect of the asymmetry of two parallel-connected GCIs on the total emission toward the grid. An actual setup is built to verify the results of the mathematical analysis and the simulation study. It is found that the SHs of single-phase GCI amplitude are strongly dependent on the DC-link voltage and the coupling inductor, while the phases of the sideband harmonics only change with changing the injected power. The variation of the injected power does not have any tangible effect on the carrier harmonics.
Citation: Machines
PubDate: 2023-11-07
DOI: 10.3390/machines11111014
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1015: Time Series Prediction for Energy
Consumption of Computer Numerical Control Axes Using Hybrid Machine
Learning Models
Authors: Robin Ströbel, Yannik Probst, Samuel Deucker, Jürgen Fleischer
First page: 1015
Abstract: The prediction of energy-related time series for computer numerical control (CNC) machine tool axes is an essential enabler for the shift towards autonomous and intelligent production. In particular, a precise prediction of energy consumption is needed to determine the environmental impact of a product and the optimization of its production. For this purpose, a novel approach for predicting high-frequency time series of numerically controlled axes based on the program code to be executed is presented. The method involves simulative preprocessing of the input NC code to determine each axis’s acceleration, velocity, and process force. Combined with the material removal rate, these variables are input for a machine learning (ML) model that delivers axis-specific high-frequency time series predictions. Compared to common approaches, it is thus possible to make predictions for the variable energy consumption of machine tools for any tool path or target resolution in the time domain. Experiments show that this approach achieves a high precision when a robust learning data basis is available. For the X-, Y-, and Z-axis, errors of 0.2%, −1.09%, and 0.09% for aircut and of 0.15%, −3.55%, and 0.08% for material removal can be achieved. The potentials for further improvement are identified systematically.
Citation: Machines
PubDate: 2023-11-08
DOI: 10.3390/machines11111015
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1016: A Novel Individual Aircraft Life Monitoring
Method Based on Reliable Life Consumption Assessment
Authors: Yueshuai Fu, Huimin Fu
First page: 1016
Abstract: Individual life monitoring is crucial for ensuring aircraft flight safety. Conventional life-consumption-based monitoring methods ignore reliability, thus disjoining them from the aircraft’s reliable life determination and extension, where high confidence and reliability are required. Therefore, this paper proposes a reliable life consumption and individual life monitoring method for aircraft structure fatigue. In the paper, the P-S-N curve, i.e., the relationship between the aircraft structure’s life (N) and fatigue load (S) under a certain probability (P), is established, by which the lower confidence limit of the aircraft structure’s reliable life can be evaluated under any fatigue loads. Based on that and the aircraft’s monitored fatigue loads, the indexes of reliable life consumption and remaining reliable life percentages are proposed and assessed in real time for individual aircraft life monitoring and online life management. Case studies indicate that the proposed method can guarantee high confidence and reliability requirements in individual life monitoring, consistent with the aircraft’s life determination and extension, which are widely accepted nowadays in engineering practice.
Citation: Machines
PubDate: 2023-11-08
DOI: 10.3390/machines11111016
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1017: A Toolpath Planning Method for Optical
Authors: Xuchu Wang, Qingshun Bai, Siyu Gao, Liang Zhao, Kai Cheng
First page: 1017
Abstract: As the applications for freeform optical surfaces continue to grow, the need for high-precision machining methods is becoming more and more of a necessity. Different toolpath strategies for the ultra-high precision turning of freeform surfaces can have a significant impact on the quality of the machined surfaces. This paper presents a novel toolpath planning method for ultra-precision slow tool servo diamond turning based on the curvature of freeform surfaces. The method analyzes the differential geometric properties of freeform surfaces by reconstructing NURBS freeform surfaces. A mathematical model is constructed based on the parameters of different positions of the freeform surface, toolpath parameters, and tool residual height. Appropriate toolpath parameters can be calculated to generate the optical freeform ultra-precision slow tool servo diamond turning toolpath. Compared with the toolpaths generated by the traditional Archimedes spiral method, the ultra-precision slow tool servo diamond turning toolpath planning method proposed in this paper can generate more uniform toolpaths on the freeform surfaces and keep the residual tool height within a small range.
Citation: Machines
PubDate: 2023-11-09
DOI: 10.3390/machines11111017
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1018: Application of Multiple Deep Neural
Networks to Multi-Solution Synthesis of Linkage Mechanisms
Authors: Chiu-Hung Chen
First page: 1018
Abstract: This paper studies the problem of linkage-bar synthesis by means of multiple deep neural networks (DNNs), which requires the inverse solution of linkage parameters based on a desired trajectory curve. This problem is highly complex due to the fact that the solution space is nonlinear and may contain multiple solutions, while a good quality of learning cannot be obtained by a single neural network approach. Therefore, this paper proposes employing Fourier descriptors to represent trajectory curves in a systematic and normalized form, developing a multi-solution distribution evaluation by random restart local searches (MDE-RRLS) to examine a better solution-space partitioning scheme, utilizing multiple DNNs to learn subspace regions separately, and creating a multi-facet query (MFQuery) to cooperatively predict multiple solutions. The experiments demonstrate that the proposed approach can obtain better or at least competitive outcomes compared to previous work in the literature. Furthermore, to verify the effectiveness and applicability, this paper investigates the design problem of an industrial six-linkage-bar ladle mechanism used in a die-casting system, and the proposed method can obtain several superior design solutions and offer alternatives in a short period of time when faced with redesign requirements.
Citation: Machines
PubDate: 2023-11-11
DOI: 10.3390/machines11111018
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1019: Quantitative Fault Diagnostics of Hydraulic
Cylinder Using Particle Filter
Authors: Yakun Zhang, Andrea Vacca, Guofang Gong, Huayong Yang
First page: 1019
Abstract: Condition-based hydraulic cylinder maintenance necessitates quantitative fault diagnostics. However, existing methods are characterized by either qualitative or limited quantitative capabilities. In this paper, a quantitative fault diagnostic method using a particle filter for hydraulic cylinders is proposed. The problem of quantitative fault diagnostics is formally formulated in a stochastic framework to assess the health/fault state, and an architecture based on joint state-parameter estimation is proposed. Through the establishment and analysis of a nonlinear dynamic model of the hydraulic cylinder, the impact of time-varying parameters on the state variables is revealed. Three fault modes of the cylinder, including friction, internal leakage, and external leakage, are theoretically identified. The proposed method allows for a simultaneous quantitative diagnosis of these three fault modes. The performance of the proposed method is evaluated using meticulously designed experiments. The results demonstrate that the mean absolute percentage errors in the parameter estimations are below 9% (accuracy exceeding 91%), thus validating its feasibility and effectiveness.
Citation: Machines
PubDate: 2023-11-12
DOI: 10.3390/machines11111019
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1020: Enhancing Dimensional Accuracy in
Budget-Friendly 3D Printing through Solid Model Geometry Tuning and Its
Use in Rapid Casting
Authors: Barun Haldar
First page: 1020
Abstract: Achieving precise dimensional accuracy and improving surface quality are the primary research and development objectives in the engineering and industrial applications of 3D printing (3DP) technologies. This experimental study investigates the pivotal role of solid model geometry tuning in enhancing the dimensional accuracy of affordable 3D printing technologies, with a specific focus on economical engineering applications. This experiment utilises low-cost Material Extrusion/Fused Filament Fabrication (FFF) and Stereolithography (SLA)/Digital Light Processing (DLP) 3D-printed patterns for the meticulous measurement of errors in the X, Y, and Z directions. These errors are then used to refine subsequent solid models, resulting in a marked improvement in dimensional accuracy (i.e., 0.15%, 0.33%, and 2.16% in the X, Y, and Z directions, respectively) in the final DLP 3D-printed parts. The study also derives and experimentally validates a novel and simple mathematical model for tuning the solid model based on the calculated linear directional errors (ei, ej, and ek). The developed mathematical model offers a versatile approach for achieving superior dimensional accuracy in other 3D printing processes. Medium-sized (4 to 10 cm) wax-made DLP- and PLA-made patterns are used to test the ceramic mould-building capacity for rapid casting (RC), where the FFF-based 3D-printed (hollow inside) pattern favours successful RC. This work comprehensively addresses the critical challenges encountered in low-cost DLP and FFF processes and their scopes in engineering applications. It provides novel suggestions and answers to improve the effectiveness, quality, and accuracy of the FFF 3D printing process for future applications in RC.
Citation: Machines
PubDate: 2023-11-12
DOI: 10.3390/machines11111020
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1021: Wear of Abrasive Tools during CMC Machining
Authors: Franck Andrés Girot Mata, Mario Alfredo Renderos Cartagena, Unai Alonso Pinillos, Borja Izquierdo Aramburu
First page: 1021
Abstract: Machining CMCs under productivity conditions while limiting tool wear and material damage is a challenge for applications such as jet aircraft engines and industrial turbines. This contribution focused on developing a method to characterize the wear of abrasive tools based on fractal dimensions. This solution allows characterization of the state of the tool after each machining and identification of the type of damage to the tool (regular wear of the diamond grains, cleavage, or breakage) and its influence on the cutting forces, in addition to damage to the machined material and the quality of the machined surface. Thus, the chipped area and the maximum chipping are directly associated with the fractal dimension of the tool surface and the metal removal rate of the process. The quality of the surface (Sa, Sz, and Sq) is associated with the fractal dimension of the surface of the tool characterizing the state of the grinding wheel and the radial depth of cut ae characterizing the engagement of the tool in the CMC material. Moreover, the results also demonstrated that the use of an abrasive tool associated with cutting conditions close to milling and not grinding is a viable solution.
Citation: Machines
PubDate: 2023-11-13
DOI: 10.3390/machines11111021
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1022: Adaptive Neuro-Fuzzy Control of Active
Vehicle Suspension Based on H2 and H∞ Synthesis
Authors: Esmaeili, Akbari, Farnam, Azad, Crevecoeur
First page: 1022
Abstract: This paper addresses the issue of a road-type-adaptive control strategy aimed at enhancing suspension performance. H2 synthesis is employed for modeling road irregularities as impulses or white noise, minimizing the root mean square (RMS) of performance outputs for these specific road types. It should be noted, however, that this approach may lead to suboptimal performance when applied to other road profiles. In contrast, the H∞ controller is employed to minimize the RMS of performance outputs under worst-case road irregularities, taking a conservative stance that ensures robustness across all road profiles. To leverage the advantages of both controllers and achieve overall improved suspension performance, automatic switching between these controllers is recommended based on the identified road type. To implement this adaptive switching mechanism, manual switching is performed, gathering input–output data from the controllers. These data are subsequently employed for training an Adaptive Neuro-Fuzzy Inference System (ANFIS) network. This elegant approach contributes significantly to the optimization of suspension performance. The simulation results employing this novel ANFIS-based controller demonstrate substantial performance enhancements compared to both the H2 and H∞ controllers. Notably, the ANFIS-based controller exhibits a remarkable 62% improvement in vehicle body comfort and a significant 57% enhancement in ride safety compared to passive suspension, highlighting its potential for superior suspension performance across diverse road conditions.
Citation: Machines
PubDate: 2023-11-14
DOI: 10.3390/machines11111022
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1023: Analysis of a Three-Phase Induction Motor
with a Double–Triple-Layer Stator Winding Configuration Operating
with Broken Rotor Bar Faults
Authors: Mbika Muteba
First page: 1023
Abstract: This paper presents the performance analysis of a three-phase squirrel cage induction motor (SCIM) with a double–triple-layer (DTL) stator winding configuration operating with broken rotor bar (BRB) faults. The effects of BRB faults on the performance of specific parameters are analyzed under a steady-state regime. The SCIM is modeled using the two-dimensional finite element method (FEM) to study electromagnetic performance under healthy and BRB faulty conditions. To validate the finite element analysis (FEA) results, a prototype of an SCIM with a DTL stator winding configuration is tested for performance evaluation under healthy and BRB faulty conditions. The FEA and experimental (EXP) results of the SCIM with a DTL stator winding arrangement are compared with the results of the SCIM with a conventional double-layer (CDL) stator winding configuration. FEA and EXP results evidenced that the SCIM with a DTL stator winding configuration mitigates some of the adverse effects introduced by the BRB faults compared to the SCIM with a CDL stator winding of the exact specifications. Under loaded conditions, the SCIM with a DTL stator winding configuration reduced the magnitudes of the twice slip frequency sidebands caused by BRB faults from ±1.2 Hz for the SCIM with a CDL stator winding arrangement down to ±0.2 Hz and ±0.36 Hz when operating with 3BRB and 6BRB faults, respectively. The results also indicate that the SCIM with a DTL stator winding configuration has reduced the decibel sideband magnitude by 7.5 dB and 8 dB for unloaded and loaded conditions, respectively. This premise has positioned the SCIM with a DTL stator winding configuration as a strong candidate in applications where BRB faults are frequent, and the motor may be required to continue operating with a BRB fault until scheduled maintenance is in effect.
Citation: Machines
PubDate: 2023-11-14
DOI: 10.3390/machines11111023
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1024: Towards the Design of a User-Friendly
Chimney-Cleaning Robot
Authors: Giuliano Arcorace, Giovanni Caruso, Pietro Cavallaro, Antonio Pantaleone Paglia, Christian Sollazzo, Manuel Tripodi, Elio Matteo Curcio, Francesco Lago, Giuseppe Carbone
First page: 1024
Abstract: Domestic chimney cleaning is still mostly a manual procedure which can be overly complex, dangerous, and expansive. This paper describes the design of a novel robotic device for chimney cleaning that aims to provide a valuable alternative solution to the traditional manual techniques with user-friendly and low-cost features. The proposed device enables a significant reduction in operator risks, including roof falling and soot dust contact. The paper’s content describes, in detail, the design process, including a definition of the main design requirements and steps towards the manufacturing of a preliminary prototype. Moreover, a preliminary validation is described through laboratory tests to demonstrate the engineering feasibility and effectiveness of the proposed design solution for the intended semi-autonomous chimney-cleaning application.
Citation: Machines
PubDate: 2023-11-15
DOI: 10.3390/machines11111024
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1025: Assembly Error Tolerance Estimation for
Large-Scale Hydrostatic Bearing Segmented Sliders under Static and
Low-Speed Conditions
Authors: Michal Michalec, Jan Foltýn, Tomáš Dryml, Lukáš Snopek, Dominik Javorský, Martin Čupr, Petr Svoboda
First page: 1025
Abstract: Hydrostatic bearings come with certain advantages over rolling bearings in moving large-scale structures. However, assembly errors are a serious matter on large scales. This study focuses on finding assembly error tolerances for the most common types in segmented errors of hydrostatic bearing sliders: tilt and offset. The experimental part was performed in the laboratory on a full diagnostic hydrostatic bearing testing rig. An investigation of the type of error on bearing performance was first conducted under static conditions. We identified the limiting error-to-film thickness ratio (e/h) for static offset error as 2.5 and the tilt angle as θ = 0.46° for the investigated case. Subsequently, two types of offset error were investigated under slow-speed conditions at 38 mm/s. The limiting error for the offset error considering the relative bi-directional movement of the slider and the pad was determined as e/h < 1. The results further indicate that the error tolerance would further decrease with increasing speed. The experimental results of error tolerances can be used to determine the required film thickness or vice versa.
Citation: Machines
PubDate: 2023-11-15
DOI: 10.3390/machines11111025
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1026: Evaluation of the Supporting Mounts of a
Three-in-One Electric Drive Unit Using a Hybrid Simulation Model
Authors: So-Hee Park, Chan-Jung Kim, Yeonjune Kang
First page: 1026
Abstract: The 3-in-1 electric drive unit (EDU) has the advantage of increasing the motor size for a larger output, and the reducer can be a compact layout designed to incorporate three key components—the drive motor, inverter, and reducer—into a single main body. This paper explores a hybrid simulation model for a 3-in-1 electromechanical drive unit (EDU) and its supporting components, consisting of the gear drive unit (GDU) mount, the motor mount, and the roll rod mounts. The synthesis of these sub-components, including the 3-in-1 EDU itself, the three supporting mount modules, and a rigid-body finite element model, is presented. The dynamics of the 3-in-1 EDU were determined through an experimental modal test. Meanwhile, the dynamic stiffness and damping coefficients of the three supporting mounts were measured using an elastomer tester across a frequency range from 10 Hz to 1000 Hz. To evaluate the sensitivity of each mount, the total spectral responses of the 3-in-1 EDU were compared under a torque input, considering rigid connections for each mount in contrast to their original dynamic stiffness. Through installing a rollrod mount, the optimal rigid connection was identified to control the dynamic response of the 3-in-1 EDU hybrid model. Furthermore, simulation results for the rigid connections in each mount were validated against experimental findings, confirming that the rigid rollrod mount configuration provided the best results.
Citation: Machines
PubDate: 2023-11-16
DOI: 10.3390/machines11111026
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1027: Fault Diagnosis of a Switch Machine to
Prevent High-Speed Railway Accidents Combining Bi-Directional Long
Short-Term Memory with the Multiple Learning Classification Based on
Associations Model
Authors: Haixiang Lin, Nana Hu, Ran Lu, Tengfei Yuan, Zhengxiang Zhao, Wansheng Bai, Qi Lin
First page: 1027
Abstract: The fault diagnosis of a switch machine is vital for high-speed railway operations because switch machines play an important role in the safe operation of high-speed railways, which often have faults because of their complicated working conditions. To improve the accuracy of turnout fault diagnosis for high-speed railways and prevent accidents from occurring, a combination of bi-directional long short-term memory (BiLSTM) with the multiple learning classification based on associations (MLCBA) model using the operation and maintenance text data of switch machines is proposed in this research. Due to the small probability of faults for a switch machine, it is difficult to form a diagnosis with the small amount of sample data, and more fault text features can be extracted with feedforward in a BiLSTM model. Then, the high-quality rules of the text data can be acquired by replacing the SoftMax classification with MLCBA in the output of the BiLSTM model. In this way, the identification of switch machine faults in a high-speed railway can be realized, and the experimental results show that the Accuracy and Recall of the fault diagnosis can reach 95.66% and 96.29%, respectively, as shown in the analysis of the ZYJ7 turnout fault text data of a Chinese railway bureau from five recent years. Therefore, the combined BiLSTM and MLCBA model can not only realize the accurate diagnosis of small-probability turnout faults but can also prevent high-speed railway accidents from occurring and ensure the safe operation of high-speed railways.
Citation: Machines
PubDate: 2023-11-17
DOI: 10.3390/machines11111027
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1028: Development and Functional Validation
Method of the Scenario-in-the-Loop Simulation Control Model Using
Co-Simulation Techniques
Authors: Balint Toth, Zsolt Szalay
First page: 1028
Abstract: With the facilitated development of highly automated driving functions and automated vehicles, the need for advanced testing techniques also arose. With a near-infinite number of potential traffic scenarios, vehicles have to drive an increased number of test kilometers during development, which would be very difficult to achieve with currently utilized conventional testing methods. State-of-the-Art testing technologies such as Vehicle-in-the-Loop (ViL) or Scenario-in-the-Loop (SciL) can provide a long-term solution; however, validation of these complex systems should also be addressed. ViL and SciL technologies provide real-time control and measurement with multiple participants; however, they require enormous computational capacity and low-latency communication to provide comparable results with real-world testing. 5G (fifth-generation wireless) communication and Edge computing can aid in fulfilling these needs, although appropriate implementation should also be tested. In the current paper, a realized control model based on the SciL architecture was presented that was developed with real-world testing data and validated utilizing co-simulation and digital twin techniques. The model was established in Simcenter Prescan© connected to MATLAB Simulink® and validated using IPG CarMaker®, which was used to feed the simulation with the necessary input data to replace the real-world testing data. The aim of the current paper was to introduce steps of the development process, to present the results of the validation procedure, and to provide an outlook of potential future implementations into the state of the art in proving ground ecosystems.
Citation: Machines
PubDate: 2023-11-17
DOI: 10.3390/machines11111028
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1029: A Study of Noise Effect in Electrical
Machines Bearing Fault Detection and Diagnosis Considering Different
Representative Feature Models
Authors: Dimitrios A. Moysidis, Georgios D. Karatzinis, Yiannis S. Boutalis, Yannis L. Karnavas
First page: 1029
Abstract: As the field of fault diagnosis in electrical machines has significantly attracted the interest of the research community in recent years, several methods have arisen in the literature. Also, raw data signals can be acquired easily nowadays, and, thus, machine learning (ML) and deep learning (DL) are candidate tools for effective diagnosis. At the same time, a challenging task is to identify the presence and type of a bearing fault under noisy conditions, especially when relevant faults are at their incipient stage. Since, in real-world applications and especially in industrial processes, electrical machines operate in constantly noisy environments, a key to an effective approach lies in the preprocessing stage adopted. In this work, an evaluation study is conducted to find the most suitable signal preprocessing techniques and the most effective model for fault diagnosis of 16 conditions/classes, from a low-workload (computational burden) perspective using a well-known dataset. More specifically, the reliability and resiliency of conventional ML and DL models is investigated here, towards rolling bearing fault detection, simulating data that correspond to noisy industrial environments. Diverse preprocessing methods are applied in order to study the performance of different training methods from the feature extraction perspective. These feature extraction methods include statistical features in time-domain analysis (TDA); wavelet packet decomposition (WPD); continuous wavelet transform (CWT); and signal-to-image conversion (SIC), utilizing raw vibration signals acquired under varying load conditions. The noise effect is examined and thoroughly commented on. Finally, the paper provides accumulated usual practices in the sense of preferred preprocessing methods and training models under different load and noise conditions.
Citation: Machines
PubDate: 2023-11-17
DOI: 10.3390/machines11111029
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1030: Fluid Film Bearings and CFD Modeling: A
Review
Authors: Demetrio Pérez-Vigueras, Jorge Colín-Ocampo, Andrés Blanco-Ortega, Rafael Campos-Amezcua, Cuauhtémoc Mazón-Valadez, Víctor I. Rodríguez-Reyes, Saulo Jesús Landa-Damas
First page: 1030
Abstract: This paper is a review of the literature about CFD modeling and analysis of journal, thrust, and aerostatic bearings; the advantages and disadvantages of each are specified, and the bearing problems that have been analyzed are discussed to improve their designs and performance. A CFD transient analysis of journal bearings was conducted using the dynamic mesh method together with movement algorithms while keeping a structured mesh of a good quality in the ANSYS Fluent software to determine the equilibrium position of the journal and calculate the dynamic coefficients. Finally, areas of opportunity for analyzing and designing fluid film bearings to improve their performance are proposed.
Citation: Machines
PubDate: 2023-11-17
DOI: 10.3390/machines11111030
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1031: Implementation of Digital Twin in Actual
Production: Intelligent Assembly Paradigm for Large-Scale Industrial
Equipment
Authors: Huaqiu Ding, Lizhong Zhao, Jihong Yan, Hsi-Yung Feng
First page: 1031
Abstract: The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved.
Citation: Machines
PubDate: 2023-11-19
DOI: 10.3390/machines11111031
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1032: Monitoring of Tool and Component Wear for
Self-Adaptive Digital Twins: A Multi-Stage Approach through Anomaly
Detection and Wear Cycle Analysis
Authors: Robin Ströbel, Alexander Bott, Andreas Wortmann, Jürgen Fleischer
First page: 1032
Abstract: In today’s manufacturing landscape, Digital Twins play a pivotal role in optimising processes and deriving actionable insights that extend beyond on-site calculations. These dynamic representations of systems demand real-time data on the actual state of machinery, rather than static images depicting idealized configurations. This paper presents a novel approach for monitoring tool and component wear in CNC milling machines by segmenting and classifying individual machining cycles. The method assumes recurring sequences, even with a batch size of 1, and considers a progressive increase in tool wear between cycles. The algorithms effectively segment and classify cycles based on path length, spindle speed and cycle duration. The tool condition index for each cycle is determined by considering all axis signals, with upper and lower thresholds established for quantifying tool conditions. The same approach is adapted to predict component wear progression in machine tools, ensuring robust condition determination. A percentage-based component state description is achieved by comparing it to the corresponding Tool Condition Codes (TCC) range. This method provides a four-class estimation of the component state. The approach has demonstrated robustness in various validation cases.
Citation: Machines
PubDate: 2023-11-19
DOI: 10.3390/machines11111032
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1033: Heuristics and Rescheduling in Prioritised
Multi-Robot Path Planning: A Literature Review
Authors: James Heselden, Gautham Das
First page: 1033
Abstract: The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and they are useful in a wide range of environments from warehouse automation, to agriculture and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from the theoretical bounds; there are many reasons for this, with the most-prominent and -impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aimed to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach, and identify key areas that are not well-explored and the scope of applying existing MRPP approaches to real-world settings.
Citation: Machines
PubDate: 2023-11-20
DOI: 10.3390/machines11111033
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1034: Anomaly Detection Using Puzzle-Based Data
Augmentation to Overcome Data Imbalances and Deficiencies
Authors: Eunkyeong Kim, Seunghwan Jung, Minseok Kim, Jinyong Kim, Baekcheon Kim, Jonggeun Kim, Sungshin Kim
First page: 1034
Abstract: Machine tools are used in a wide range of applications, and they can manufacture workpieces flexibly. Furthermore, they require maintenance; the overall costs include maintenance costs, which constitute a significant portion, and the costs involved in ensuring product quality. Therefore, anomaly detection in tool conditions is required, because these tools are essential industrial elements. However, the data related to tool conditions present some challenges: data imbalances and deficiencies. Data imbalances and deficiencies can affect the performance of anomaly detection models. A model trained using data with imbalances and deficiencies may miscalculate that abnormal data are normal data, leasing to errors. To overcome these problems, the proposed method has been designed using the wavelet transform, color space conversion, color extraction, puzzle-based data augmentation, and double transfer learning. The proposed method generated image data from time-series data, effectively extracted features, and generated new image data using puzzle-based data augmentation. The color information was processed to highlight features, and the proposed puzzle-based data augmentation was applied during processing to increase the amount of data to improve the performance of the anomaly detection model. The experimental results showed that the proposed method can classify normal and abnormal data with greater accuracy. In particular, the accuracy of abnormal data classification increased from 25.00% to 91.67%. This demonstrates that the proposed method is effective and can overcome data imbalances and deficiencies.
Citation: Machines
PubDate: 2023-11-20
DOI: 10.3390/machines11111034
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1035: 4D Printing: A Methodical Approach to
Product Development Using Smart Materials
Authors: Stefan Junk, Henning Einloth, Dirk Velten
First page: 1035
Abstract: In 4D printing, an additively manufactured component is given the ability to change its shape or function in an intended and useful manner over time. The technology of 4D printing is still in an early stage of development. Nevertheless, interesting research and initial applications exist in the literature. In this work, a novel methodical approach is presented that helps transfer existing 4D printing research results and knowledge into solving application tasks systematically. Moreover, two different smart materials are analyzed, used, and combined following the presented methodical approach to solving the given task in the form of recovering an object from a poorly accessible space. This is implemented by self-positioning, grabbing, and extracting the target object. The first smart material used to realize these tasks is a shape-memory polymer, while the second is a polymer-based magnetic composite. In addition to the presentation and detailed implementation of the methodical approach, the potentials and behavior of the two smart materials are further examined and narrowed down as a result of the investigation. The results show that the developed methodical approach contributes to moving 4D printing closer toward a viable alternative to existing technologies due to its problem-oriented nature.
Citation: Machines
PubDate: 2023-11-20
DOI: 10.3390/machines11111035
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 1036: Increased Dynamic Drivetrain Performance by
Implementing a Modular Design with Decentralized Control Architecture
Authors: Niels Divens, Théo Tuerlinckx, Bernhard Westerhof, Kurt Stockman, David van Os, Koen Laurijssen
First page: 1036
Abstract: This paper assesses the energy consumption, control performance, and application-specific functional requirements of a modular drivetrain in comparison to a benchmark drivetrain. A decentralised control architecture has been developed and validated using mechanical plant models. Simscape models have been validated with data from an experimental setup including an equivalent modular and benchmark drivetrain. In addition, the control strategy has been implemented and validated on the experimental setup. The results prove the ability of the control strategy to synchronize the motion of the different sliders, resulting in crank position tracking errors below 0.032 radians on the setup. The model and experimental data show an increased performance of the modular drivetrain compared to the benchmark drivetrain in terms of energy consumption, control performance, and functional requirements. The modular drivetrain is especially advantageous for machines running highly dynamic motion profiles due to the reduced inertia. For such motion profiles, an increased position tracking of up to 84% has been measured. In addition, it is shown that the modular drivetrain root mean square (RMS) torque is reduced with 32% compared to the benchmark drivetrain. However, these mechanical energy savings are partly counteracted by the higher motor losses seen in the modular drivetrain, resulting in potential electrical energy savings of around 29%.
Citation: Machines
PubDate: 2023-11-20
DOI: 10.3390/machines11111036
Issue No: Vol. 11, No. 11 (2023)
- Machines, Vol. 11, Pages 967: Assistive Self-Driving Car Networks to
Provide Safe Road Ecosystems for Disabled Road Users
Authors: Juan Guerrero-Ibañez, Juan Contreras-Castillo, Ismael Amezcua-Valdovinos, Angelica Reyes-Muñoz
First page: 967
Abstract: Disabled pedestrians are among the most vulnerable groups in road traffic. Using technology to assist this vulnerable group could be instrumental in reducing the mobility challenges they face daily. On the one hand, the automotive industry is focusing its efforts on car automation. On the other hand, in recent years, assistive technology has been promoted as a tool for consolidating the functional independence of people with disabilities. However, the success of these technologies depends on how well they help self-driving cars interact with disabled pedestrians. This paper proposes an architecture to facilitate interaction between disabled pedestrians and self-driving cars based on deep learning and 802.11p wireless technology. Through the application of assistive technology, we can locate the pedestrian with a disability within the road traffic ecosystem, and we define a set of functionalities for the identification of hand gestures of people with disabilities. These functions enable pedestrians with disabilities to express their intentions, improving their confidence and safety level in tasks within the road ecosystem, such as crossing the street.
Citation: Machines
PubDate: 2023-10-17
DOI: 10.3390/machines11100967
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 968: Empirical Filtering-Based Artificial
Intelligence Learning Diagnosis of Series DC Arc Faults in Time Domains
Authors: Hoang-Long Dang, Sangshin Kwak, Seungdeog Choi
First page: 968
Abstract: Direct current (DC) networks play a pivotal role in the growing integration of renewable energy sources. However, the occurrence of DC arc faults can introduce disruptions and pose fire hazards within these networks. In order to ensure both safety and optimal functionality, it becomes imperative to comprehend the characteristics of DC arc faults and implement a dependable detection system. This paper introduces an innovative arc fault detection algorithm that leverages current filtering based on the empirical rule in conjunction with intelligent machine learning techniques. The core of this approach involves the sampling and subsequent filtration of current using the empirical rule. This filtering process effectively amplifies the distinctions between normal and arcing states, thereby enhancing the overall performance of the intelligent learning techniques integrated into the system. Furthermore, this proposed diagnosis scheme requires only the signal from the current sensor, which reduces the complexity of the diagnosis scheme. The results obtained from the detection process serve to affirm the effectiveness and reliability of the proposed DC arc fault diagnosis scheme.
Citation: Machines
PubDate: 2023-10-17
DOI: 10.3390/machines11100968
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 969: Experimental Study of the Dynamic
Short-Circuit Withstand Capability of an 8400 kVA Power Transformer
Specially Designed for Photovoltaic Applications
Authors: Cristian-Eugeniu Sălceanu, Cătălin Dobrea, Daniel Ocoleanu, Marcel Nicola, Daniela Iovan, Maria-Cristina Nițu
First page: 969
Abstract: This article, besides offering data of great value for any designer of high-power short-circuits of special three-winding design, illustrates the correlation with the corresponding FRA measurements, validating this type of measurement. The frequency response measurements can provide data about the transformer’s status after it is put into service, its vulnerability in incipient states, and, particularly for this type of transformer, its insulation, which is the subject of high dielectric stress due to its invertor working regime. This article presents the behavior of a three-phase 8400 kVA medium-voltage step-up transformer (corrugated hermetic tank) specially designed for photovoltaic applications during short-circuit tests. This transformer, fed by two inverters, has two secondaries with elliptical windings (non-circular aluminum foil for LV windings and an aluminum conductor for HV windings). Various experiments were performed, including measurements of winding resistance, measurements of voltage ratio, measurements of short-circuit impedance and load loss on three tappings, measurements of no-load loss and current, a frequency response analysis, and short-circuiting. These experiments were performed to study the behavior of the transformer, which, in real life, is powered by photovoltaic inverters on the LV side that feed into the MV grid on the HV side, making it the interface between the photovoltaic inverter and the MV grid. An auxiliary supply transformer may be connected to the LV side. Given these elements, concerning both the importance and the particularities of the problem studied, we can say that this article represents a niche study on the guarantee of good functioning and safety in operation given the passing of the test to withstand the dynamic effects of short-circuiting.
Citation: Machines
PubDate: 2023-10-17
DOI: 10.3390/machines11100969
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 970: A Decoupling Algorithm-Based Technology for
Predicting and Regulating the Unbalance of Aircraft Rotor Assembly
Considering Manufacturing Errors
Authors: Yingjie Zhao, Xiaokai Mu, Jian Liu, Qingchao Sun, Ping Zhou, Guozhen Fang
First page: 970
Abstract: Rotor unbalance is the most important factor affecting the dynamic performance of aircraft engines. The existing unbalance prediction and control methods are insufficient for multi-stage rotors. The post-assembly unbalance of rotors in aircraft engines is a critical factor affecting their dynamic performance. In order to predict and reduce the unbalance of multi-stage rotors after assembly, this paper establishes a measurement model for the center-of-mass offset of aircraft engine rotors through decoupled calculations of the unbalance. Furthermore, it constructs an unbalance prediction model using the spatial transfer mechanism of combined rotor offset centers under the influence of manufacturing errors. Additionally, a method for measuring rotor unbalance during the assembly phase is proposed. The experimental results of the unbalance in multi-stage combined rotor assembly indicate that the degree of agreement between the predicted results and the experimental results is 91.3%, resulting in a reduction in the mean error of 15.3% compared to before the correction. The study also investigates the impact of manufacturing errors on unbalance. This research provides robust support for controlling the unbalance in multi-stage combined rotor assembly.
Citation: Machines
PubDate: 2023-10-18
DOI: 10.3390/machines11100970
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 971: Influence of Rotation Speed and Gas Content
on the Transient Gas–Liquid Two-Phase Flow of an Electric
Submersible Pump
Authors: Deqing Sun, Zhongmin Xiao, Ziming Feng, Heng Yuan, Wei Cui
First page: 971
Abstract: In order to study the internal flow characteristics of the electric submersible pump (ESP) when the gas–liquid two-phase flow is conveyed by the variable frequency variable speed operation and the change of the imported gas content, the impeller of the Q10# ESP is taken as the research object, based on the Eulerian-Eulerian non-homogeneous phase. The flow model, the unsteady Reynolds time-averaged N-S equation, and the standard k-ε turbulence model are used for transient simulation calculations of the gas–liquid two-phase flow in the impeller of the ESP. Calculations show that with the rotation of the impeller, the gas phase is unevenly distributed in the flow channel. The gas phase is mainly concentrated on the inlet side of the flow channel near the front cover, and the gas phase exhibits periodic aggregation and diffusion in the flow channel. When the impeller speed increases, the period of periodic accumulation and diffusion of gas in the flow channel is shortened and the gas concentration in the impeller decreases, the overall flow velocity in the flow channel increases, and the pressure difference between the inlet and outlet increases. The pressure difference between the two sides of the blade is proportional to the speed of the impeller, and the fluctuation frequency of the blade surface also increases. As the gas content increases, the maximum concentration of gas phase in the flow channel increases. The area occupied by the high concentration of gas phase in the flow channel expands toward the blade’s working surface, and periodically accumulates, diffuses, and grows. The gas-liquid splitting area shrinks toward the front cover side and the pump. The internal pressure increases slightly, the main flow velocity increases, and the vortex action range increases.
Citation: Machines
PubDate: 2023-10-18
DOI: 10.3390/machines11100971
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 972: A Sliding Mode Approach-Based Adaptive
Steering Control Algorithm for Path Tracking of Autonomous Mobility with
Weighted Injection
Authors: Sehwan Kim, Kwangseok Oh
First page: 972
Abstract: The increasing complexity of mathematical models developed as part of the recent advancements in autonomous mobility platforms has led to an escalation in uncertainty. Despite the intricate nature of such models, the detection, decision, and control methods for autonomous mobility path tracking remain critical. This study aims to achieve path tracking based on pixel-based control errors without parameters in the mathematical model. The proposed approach entails deriving control errors from a multi-particle filter based on a camera, estimating the error dynamics coefficients through a recursive least squares (RLS) approach, and using the sliding mode approach and weighted injection to formulate a cost function that leverages the estimated coefficients and control errors. The resultant adaptive steering control expedites the convergence of control errors towards zero by determining the magnitude of the injection variable based on the control errors and the finite-time convergence condition. The efficacy of the proposed approach is evaluated through an S-curved and elliptical path using autonomous mobility equipped with a single steering and driving module. The results demonstrate the capability of the approach to reasonably track target paths through driving and steering control facilitated by a multi-particle filter and a lidar-based obstacle detection system.
Citation: Machines
PubDate: 2023-10-18
DOI: 10.3390/machines11100972
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 973: Towards DevOps for Cyber-Physical Systems
(CPSs): Resilient Self-Adaptive Software for Sustainable Human-Centric
Smart CPS Facilitated by Digital Twins
Authors: Jürgen Dobaj, Andreas Riel, Georg Macher, Markus Egretzberger
First page: 973
Abstract: The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.
Citation: Machines
PubDate: 2023-10-19
DOI: 10.3390/machines11100973
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 974: Grasping Pose Estimation for Robots Based on
Convolutional Neural Networks
Authors: Tianjiao Zheng, Chengzhi Wang, Yanduo Wan, Sikai Zhao, Jie Zhao, Debin Shan, Yanhe Zhu
First page: 974
Abstract: Robots gradually have the ability to plan grasping actions in unknown scenes by learning the manipulation of typical scenes. The grasping pose estimation method, as a kind of end-to-end method, has rapidly developed in recent years because of its good generalization. In this paper, we present a grasping pose estimation method for robots based on convolutional neural networks. In this method, a convolutional neural network model was employed, which can output the grasping success rate, approach angle, and gripper opening width for the input voxel. The grasping dataset was produced, and the model was trained in the physical simulator. A position optimization of the robotic grasping was proposed according to the distribution of the object centroid to improve the grasping success rate. An experimental platform for robot grasping was established, and 11 common everyday objects were selected for the experiments. Grasping experiments involving the eleven objects individually, multiple objects, as well as a dark environment without illumination, were performed. The results show that the method has the adaptability to grasp different geometric objects, including irregular shapes, and it is not influenced by lighting conditions. The total grasping success rate was 88.2% for the individual objects and 81.1% for the cluttered scene.
Citation: Machines
PubDate: 2023-10-20
DOI: 10.3390/machines11100974
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 975: Design and Implementation of a Recursive
Feedforward-Based Virtual Reference Feedback Tuning (VRFT) Controller for
Temperature Uniformity Control Applications
Authors: Juan Gabriel Araque, Luis Angel, Jairo Viola, Yangquan Chen
First page: 975
Abstract: Data-driven controller synthesis methods use input/output information to find the coefficients of a proposed control architecture. Virtual Reference Feedback Tuning (VRFT) is one of the most popular frameworks due to its simplicity and one-shoot synthesis style based on open-loop system response for classic regulators such as PI or PID. This paper presents a recursive VRFT framework to extend VRFT into high-order controllers with more complex structures. The framework first defines a reference model and controller structure, then uses the open-loop data to compute the virtual reference and error signals, and, finally, uses these to find the controller parameters via an optimization algorithm. Likewise, the recursive VRFT controller performance is improved by adding a model-based feedforward loop to improve reference signal tracking. The recursive method is tested to design a temperature uniformity control system. The obtained results show that the recursive VRFT with a feedforward improves the system response while allowing more complex controller synthesis.
Citation: Machines
PubDate: 2023-10-20
DOI: 10.3390/machines11100975
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 976: An Adaptive Model-Based Approach to the
Diagnosis and Prognosis of Rotor-Bearing Unbalance
Authors: Banalata Bera, Shyh-Chin Huang, Mohammad Najibullah, Chun-Ling Lin
First page: 976
Abstract: Rotating machinery is the fundamental component of almost all industrial frameworks. Therefore, prognostics and health management (PHM) have emerged as crucial requirements for effectively managing and sustaining various systems in a timely manner. The unbalanced fault has been recognized as a significant contributing factor in the development of faults in rotor-bearing systems, eventually causing failure. Thus, it is essential to monitor the unbalance and maintain it within acceptable bounds in order to guarantee the system’s proper operation. Most approaches to the rotor’s unbalance monitoring are model-based instead of data-driven due to the shortage of faulted data. In a derived model-based approach, proper identification of the model’s parameters, e.g., bearing parameters, always plays a very crucial role. Nonetheless, the identified model’s parameters in their initial state would inevitably degenerate during a long-term operation because of aging or environmental changes, such that they are no longer well representative of the real system. In this context, this paper offers an adaptive model-based approach for the assessment of unbalance faults developing over days in a rotor-bearing model. The model is adaptive in the sense that it automatically adjusts its parameters so that they are more closely aligned with the real system. A particle swarm optimization (PSO) scheme is utilized in the parameter identification process. The residual serves as the index for initiating the adaptive process when it is greater than a preset percentage. Individual feature errors work as a gauge to determine which bearing parameters need to be reevaluated. A set of 16-month operational data from a local petrochemical company is used to validate the approach. The unbalanced deterioration trend is evaluated, and results from the adaptive methodology are assessed to show its superiority over the original one. It is also observed that the model’s capacity to anticipate unbalance is greatly enhanced by the adaptive strategy. Finally, future unbalances are explored to show its capacity for continuous monitoring-based maintenance solutions.
Citation: Machines
PubDate: 2023-10-21
DOI: 10.3390/machines11100976
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 977: Study on Human Motion Energy Harvesting
Devices: A Review
Authors: Wenzhou Lin, Yuchen Wei, Xupeng Wang, Kangjia Zhai, Xiaomin Ji
First page: 977
Abstract: With the increasing utilization of portable electronic devices and wearable technologies, the field of human motion energy harvesting has gained significant attention. These devices have the potential to efficiently convert the mechanical energy generated by human motion into electrical energy, enabling a continuous power supply for low-power devices. This paper provides an overview of the fundamental principles underlying various energy harvesting modes, including friction-based, electromagnetic, and piezoelectric mechanisms, and categorizes existing energy harvesting devices accordingly. Furthermore, this study conducts a comprehensive analysis of key techniques in energy harvesting, such as mode selection, efficiency enhancement, miniaturized design of devices, and evaluation of energy harvesting experiments. It also compares the distinct characteristics of different energy harvesting modes. Finally, the paper summarizes the challenges faced by these devices in terms of integrating human biomechanics, achieving higher energy harvesting efficiencies, facilitating micro-miniaturization, enabling composite designs, and exploring broader applications. Moreover, it offers insights into the future development of human motion energy harvesting technology, laying a theoretical framework and providing a reference for future research endeavors in this field.
Citation: Machines
PubDate: 2023-10-22
DOI: 10.3390/machines11100977
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 978: Development of a 6-DOF Parallel Robot for
Potential Single-Incision Laparoscopic Surgery Application
Authors: Doina Pisla, Nadim Al Hajjar, Bogdan Gherman, Corina Radu, Tiberiu Antal, Paul Tucan, Ruxanda Literat, Calin Vaida
First page: 978
Abstract: This paper presents the development of a 6-DOF (Degrees of Freedom) parallel robot for single-incision laparoscopic surgery (SILS). The concept of the robotic system is developed with respect to a medical protocol designed by the medical experts in the team targeting a SILS procedure in urology. The kinematic model of the robotic system was defined to determine the singularities that may occur during functioning. FEM analyses were performed to determine the components of the robotic structure that may compromise the rigidity of the robotic system, and these components were redesigned and integrated into the final design of the robot. To verify the kinematic model a series of numerical and graphical simulations were performed, while to test the functionality of the robotic system, a low-cost experimental model was developed. The accuracy of the experimental model was measured using an optical motion tracking system.
Citation: Machines
PubDate: 2023-10-23
DOI: 10.3390/machines11100978
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 979: Kinematic Models and the Performance Level
Index of a Picking-and-Placing Hybrid Robot
Authors: Qi Zou, Dan Zhang, Guanyu Huang
First page: 979
Abstract: The mobile platform of the parallel robot designed for picking and placing operations is usually equipped with one or two extra degree(s) of freedom to enable flexible grasping orientations. However, additional motors indicate extra loads for the moving platform, and the total payload performance shrinks. This paper proposes a spatial picking-and-placing manipulator, in which one actuator that is supposed to be installed on the mobile platform is placed far away from the mobile platform. The platform has a large workspace along one direction. The comprehensive analytical inverse and forward kinematic solutions of this robot are derived. The reachable workspace of the parallel manipulator module is then explored. The novel performance level index is designed to normalize the performance index and demonstrate the performance rank for any pose. A mathematical proof is provided for this novel index. The manipulability index is taken as an example to examine the level indicator. A multi-objective optimization is implemented to pursue optimal performance; then, the initial design and optimized results are compared in detail. A sample trajectory is provided to verify the correctness of the kinematic mathematical model of the parallel mechanism.
Citation: Machines
PubDate: 2023-10-23
DOI: 10.3390/machines11100979
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 980: Path Planning Technique for Mobile Robots: A
Review
Authors: Liwei Yang, Ping Li, Song Qian, He Quan, Jinchao Miao, Mengqi Liu, Yanpei Hu, Erexidin Memetimin
First page: 980
Abstract: Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.
Citation: Machines
PubDate: 2023-10-23
DOI: 10.3390/machines11100980
Issue No: Vol. 11, No. 10 (2023)
- Machines, Vol. 11, Pages 981: Damage Identification for Railway Tracks
Using Onboard Monitoring Systems in In-Service Vehicles and Data Science
Authors: Nelson Traquinho, Cecília Vale, Diogo Ribeiro, Andreia Meixedo, Pedro Montenegro, Araliya Mosleh, Rui Calçada
First page: 981
Abstract: Nowadays, railway track monitoring strategies are based on the use of railway inspection vehicles and wayside dynamic monitoring systems. The latter sometimes requires traffic disruption, as well as higher time and cost-consumption activities, and the use of dedicated inspection vehicles is less economical and efficient as the use of in-service vehicles. Furthermore, the use of non-automated algorithms faces challenges when it comes to early damage detection in railway infrastructure, considering operational, environmental, and big data aspects, and may lead to false alarms. To overcome these challenges, the application of artificial intelligence (AI) algorithms for early detection of track defects using accelerations, measured by dynamic monitoring systems in in-service railway vehicles is attracting the attention of railway managers. In this paper, an AI-based methodology based on axle box acceleration signals is applied for the early detection of distributed damage to track in terms of the longitudinal level and lateral alignment. The methodology relies on feature extraction using an autoregressive model, data normalization using principal component analysis, data fusion and feature discrimination using Mahalanobis distance and outlier analysis, considering eight onboard accelerometers. For the numerical simulations, 75 undamaged and 45 damaged track scenarios are considered. The alert limit state defined in the European Standard for assessing track geometry quality is also assumed as a threshold. It was found that the detection accuracy of the AI-based methodology for different sensor layouts and types of damage is greater than 94%, which is acceptable.
Citation: Machines
PubDate: 2023-10-23
DOI: 10.3390/machines11100981
Issue No: Vol. 11, No. 10 (2023)
