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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. 12, Pages 588: Influence of Stator/Rotor Torque Ratio on
Torque Performance in External-Rotor Dual-Armature Flux-Switching PM
Machines
Authors: Zijie Zuo, Yidong Du, Lei Yu
First page: 588
Abstract: External-rotor dual-armature flux-switching PM (ER-DA-FSPM) machines have high torque density and decent fault tolerance, making them promising candidates for in-wheel machine applications in electric vehicles. The torque output and optimal design parameters of ER-DA-FSPM machines are affected by the stator/rotor torque ratio, which is the focus of this paper. Firstly, this paper analyzes airgap flux density harmonics of ER-DA-FSPM to provide a clear insight into the torque-generation mechanism. Then, this paper investigates the influence of torque ratio on average torque under the same copper loss. It is found that the average torque decreases with torque ratio increasing due to the reduction of the positive torque component generated by the sixth airgap field harmonics and the rise in the negative torque component from the eighth harmonics. Moreover, this paper also provides the optimal parameter recommendation to guide the machine design. The split ratio should increase, and the arc of PMs should decrease for a larger torque ratio, whilst the other parameters are hardly influenced. Next, this paper makes a comparison among the ER-DA-FSPM machine, external rotor flux-switching PM (ER-FSPM) machine, and surface-mounted PM (ER-SPM) machines. It shows that the ER-DA-FSPM machine, with the torque ratio being 2, can lead to a much larger total torque. In addition, in the event of rotor winding failure, which is more possible due to the existence of slip rings than stator winding failure, the stator can still provide an average torque larger than that of ER-SPM machine and 92.0% that of the ER-FSPM machine, respectively. Finally, the theoretical analysis is verified by the experiments.
Citation: Machines
PubDate: 2024-08-23
DOI: 10.3390/machines12090588
Issue No: Vol. 12, No. 9 (2024)
- Machines, Vol. 12, Pages 589: Structural Health Monitoring of Laminated
Composites Using Lightweight Transfer Learning
Authors: Muhammad Muzammil Azad, Izaz Raouf, Muhammad Sohail, Heung Soo Kim
First page: 589
Abstract: Due to their excellent strength-to-weight ratio, composite laminates are gradually being substituted for traditional metallic materials in a variety of industries. However, due to their orthotropic nature, composite laminates are prone to several different types of damage, with delamination being the most prevalent and serious. Therefore, deep learning-based methods that use sensor data to conduct autonomous health monitoring have drawn much interest in structural health monitoring (SHM). However, the direct application of these models is restricted by a lack of training data, necessitating the use of transfer learning. The commonly used transfer learning models are computationally expensive; therefore, the present research proposes lightweight transfer learning (LTL) models for the SHM of composites. The use of an EfficientNet–based LTL model only requires the fine-tuning of target vibration data rather than training from scratch. Wavelet-transformed vibrational data from various classes of composite laminates are utilized to confirm the effectiveness of the proposed method. Moreover, various assessment measures are applied to assess model performance on unseen test datasets. The outcomes of the validation show that the pre-trained EfficientNet–based LTL model could successfully perform the SHM of composite laminates, achieving high values regarding accuracy, precision, recall, and F1-score.
Citation: Machines
PubDate: 2024-08-25
DOI: 10.3390/machines12090589
Issue No: Vol. 12, No. 9 (2024)
- Machines, Vol. 12, Pages 590: Research on Multi-Objective Low-Carbon
Flexible Job Shop Scheduling Based on Improved NSGA-II
Authors: Zheyu Mei, Yujun Lu, Liye Lv
First page: 590
Abstract: To optimize the production scheduling of a flexible job shop, this paper, based on the NSGA-II algorithm, proposes an adaptive simulated annealing non-dominated sorting genetic algorithm II with enhanced elitism (ASA-NSGA-EE) that establishes a multi-objective flexible job shop scheduling model with the objective functions of minimizing the maximum completion time, processing cost, and carbon emissions generated from processing. The ASA-NSGA-EE algorithm adopts an adaptive crossover and mutation genetic strategy, which dynamically adjusts the crossover and mutation rates based on the evolutionary stage of the population, aiming to reduce the loss of optimal solutions. Additionally, it incorporates the simulated annealing algorithm to optimize the selection strategy by leveraging its cooling characteristics. Furthermore, it improves the elite strategy through incorporating elite selection criteria. Finally, by simulation experiments, the effectiveness of the improved NSGA-II algorithm is validated by comparing it with other algorithms.
Citation: Machines
PubDate: 2024-08-26
DOI: 10.3390/machines12090590
Issue No: Vol. 12, No. 9 (2024)
- Machines, Vol. 12, Pages 591: Agricultural UAV Path Planning Based on a
Differentiated Creative Search Algorithm with Multi-Strategy Improvement
Authors: Jin Liu, Yong Lin, Xiang Zhang, Jibin Yin, Xiaoli Zhang, Yong Feng, Qian Qian
First page: 591
Abstract: A differentiated creative search algorithm with multi-strategy improvement (MSDCS) is proposed for the path planning problem for agricultural UAVs under different complicated situations. First, the good point set and oppositional learning strategies are used to effectively improve the quality of population diversity; the adaptive fitness–distance balance reset strategy is proposed to motivate the low performers to move closer to the region near the optimal solution and find the potential optimal solution; and the vertical and horizontal crossover strategy with random dimensions is proposed to improve the computational accuracy of the algorithm and the ability to jump out of the local optimum. Second, the MSDCS is compared to different algorithms using the IEEE_CEC2017 test set, which consists of 29 test functions. The results demonstrate that the MSDCS achieves the optimal value in 23 test functions, surpassing the comparison algorithms in terms of convergence accuracy, speed, and stability by at least one order of magnitude difference, and it is ranked No. 1 in terms of comprehensive performance. Finally, the enhanced algorithm was employed to address the issue of path planning for agricultural UAVs. The experimental results demonstrate that the MSDCS outperforms comparison algorithms in path planning across various contexts. Consequently, the MSDCS can generate optimal pathways that are both rational and safe for agricultural UAV operations.
Citation: Machines
PubDate: 2024-08-26
DOI: 10.3390/machines12090591
Issue No: Vol. 12, No. 9 (2024)
- Machines, Vol. 12, Pages 592: Research on Suppressing Commutation Torque
Ripple of BLDCM Based on Zeta Converter
Authors: Tao Yin, Wanli Yang, Wenxian Zhang, Meng Wu, Xiugang Yu, Xingchang Han
First page: 592
Abstract: Torque ripple in a brushless DC motor (BLDCM) seriously restricts its application in high-performance fields. This paper proposes a commutation torque ripple suppression strategy based on a Zeta converter. The expected output voltage of a Zeta converter that suppresses the commutation torque ripple is obtained, according to the effect of the duty ratio of the Zeta converter on the turn-off phase freewheeling duration and the turn-on phase rising duration, during commutation. Based on the analysis of the dynamic response of the Zeta converter, the Zeta converter is adjusted to ensure that the Zeta converter reaches stability in sufficient time. During the commutation, the output voltage of the Zeta converter is connected to the main circuit to reduce the torque ripple during commutation, and the expected regulated duty cycle of the Zeta converter during the next commutation is calculated to adjust the output voltage of the Zeta converter. Based on this analysis, the experimental results verify the effectiveness of the proposed method.
Citation: Machines
PubDate: 2024-08-26
DOI: 10.3390/machines12090592
Issue No: Vol. 12, No. 9 (2024)
- Machines, Vol. 12, Pages 593: Pre-Compensation Strategy for Tracking Error
and Contour Error by Using Friction and Cross-Coupled Control
Authors: Minghao Liu, Yongmin Zhu, Hongliang Xu, Weirui Liu, Hui Yang, Xingjun Gao
First page: 593
Abstract: This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila swarm algorithm based on cross-mutation. The friction tracking error can be deduced from the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.
Citation: Machines
PubDate: 2024-08-26
DOI: 10.3390/machines12090593
Issue No: Vol. 12, No. 9 (2024)
- Machines, Vol. 12, Pages 496: The Grinding and Correction of Face Gears
Based on an Internal Gear Grinding Machine
Authors: Zhengyang Han, Chuang Jiang, Xiaozhong Deng, Congcong Zhang, Longlong Geng, Yong Feng
First page: 496
Abstract: This paper presents a method of calculating and correcting grinding face gears on an internal gear grinding machine. The generating principle of face gears is studied, and the feasibility of grinding motion on an internal gear grinding machine is analyzed. Then, the motions that need to be followed for grinding are analyzed based on the gear machine tool structure. Four main error sources causing tooth surface deviation in the grinding movements are proposed. The mathematical modeling of the grinding of face gears containing proposed error sources on an internal gear grinding machine is accurately established. The influence of the error sources on the topological deviations of the tooth surface is explored. A sensitivity matrix is established for the influence of various error factors on the tooth surface deviations. The correction values of each error factor are obtained in the case of existing tooth surface deviations. Finally, a virtual machining experiment is conducted, which proves the accuracy of the proposed method for characterizing grinding and realizing corrections.
Citation: Machines
PubDate: 2024-07-23
DOI: 10.3390/machines12080496
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 497: Fault Diagnosis in Induction Motors through
Infrared Thermal Images Using Convolutional Neural Network Feature
Extraction
Authors: Uriel Calderon-Uribe, Rocio A. Lizarraga-Morales, Igor V. Guryev
First page: 497
Abstract: The development of diagnostic systems for rotating machines such as induction motors (IMs) is a task of utmost importance for the industrial sector. Reliable diagnostic systems allow for the accurate detection of different faults. Different methods based on the acquisition of thermal images (TIs) have emerged as diagnosis systems for the detection of IM faults to prevent the further generation of faults. However, these methods are based on artisanal feature selection, so obtaining high accuracy rates is usually challenging. For this reason, in this work, a new system for fault detection in IMs based on convolutional neural networks (CNNs) and thermal images (TIs) is presented. The system is based on the training of a CNN using TIs to select and extract the most salient features of each fault present in the IM. Subsequently, a classifier based on a decision tree (DT) algorithm is trained using the features learned by the CNN to infer the motor conditions. The results of this methodology show an improvement in the accuracy, precision, recall, and F1-score metrics for 11 different conditions.
Citation: Machines
PubDate: 2024-07-23
DOI: 10.3390/machines12080497
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 498: Research on Sintering Machine Axle Fault
Detection Based on Wheel Swing Characteristics
Authors: Bo Chen, Husheng Yang, Jiarui Mei, Yueming Wang, Hao Zhang
First page: 498
Abstract: During the sintering process in iron production, wheel swing is a sign of sintering machine trolley axle faults, which may lead to the wheel falling off and affect the production operation of the sintering machine system in serious cases. To solve this problem, this paper proposes a fault detection and localization method based on the You Only Look Once version 9 (YOLOv9) object detection algorithm and frame difference method for detecting sintering machine trolley wheel swing. The wheel images transmitted from the camera were sent to a trolley wheel and side panel number detection model that was trained on YOLOv9 for recognition. The wheel recognition boxes of the previous and subsequent frames were fused into the wheel region of interest. In the wheel region of interest, the difference operation was carried out. The result of the difference operation was compared with the preset threshold to determine whether the trolley wheel swings. When a wheel swing fault occurs, the image of the side plate at the time of the fault is collected, and the number on the side plate is identified so as to accurately locate the faulty trolley and to assist the field personnel in troubleshooting the fault. The experimental results show that this method can detect wheel swing faults in the industrial field, and the detection accuracy of wheel swing faults was 93.33%. The trolley side plate numbers’ average precision was 99.2% in fault localization. Utilizing the aforementioned method to construct a system for detecting wheel swing can provide technical support for fault detection of the trolley axle on the sintering machine.
Citation: Machines
PubDate: 2024-07-23
DOI: 10.3390/machines12080498
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 499: Experimental Evaluation of Acoustical
Materials for Noise Reduction in an Induction Motor Drive
Authors: Ashish Kumar Sahu, Abeka Selliah, Alaa Hassan, Moien Masoumi, Berker Bilgin
First page: 499
Abstract: Electric propulsion motors are more efficient than internal combustion engines, but they generate high-frequency tonal noise, which can be perceived as annoying. Acoustical materials are typically suitable for high-frequency noise, making them ideal for acoustic noise mitigation. This paper investigates the effectiveness of three acoustical materials, namely, 2″ Polyurethane foam, 2″ Vinyl-faced quilted glass fiber, and 2″ Studiofoam, in mitigating the acoustic noise from an induction motor and a variable frequency inverter. Acoustic noise rates at multiple motor speeds, with and without the application of acoustical materials, are compared to determine the effectiveness of acoustical materials in mitigating acoustic noise at the transmission stage. Acoustical materials reduce acoustic noise from the induction motor by 5–14 dB(A) at around 500 Hz and by 22–31 dB(A) at around 10,000 Hz. Among the tested materials, Studiofoam demonstrates superior noise absorption capacity across the entire frequency range. Polyurethane foam is a cost-effective and lightweight alternative, and it is equally as effective as Studifoam in mitigating high-frequency acoustic noise above 5000 Hz.
Citation: Machines
PubDate: 2024-07-23
DOI: 10.3390/machines12080499
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 500: Safety-Centric Precision Control of a
Modified Duodenoscope Designed for Surgical Robotics
Authors: Yuxuan Cheng, Ruyan Yan, Bingyi Liu, Chun Yang, Tianyu Xie
First page: 500
Abstract: There is limited research on robotic systems designed for Endoscopic Retrograde Cholangiopancreatography (ERCP) procedures using a side-view duodenoscope. The unique structure of the duodenoscope presents challenges to safely and precisely control the distal end pose. Control methods applied can reduce potential medical risks. We have redesigned the control section of the duodenoscope to facilitate its manipulation by a robotic system. An orthogonal compensator is employed to rectify the motion planes to standard planes. A hysteresis compensator based on the Prandtl-Ishlinskii model enables precise control of the distal pose of the duodenoscope. Furthermore, we utilize a contact force prediction model to prevent excessive contact force at the distal end. The performance of the modified duodenoscope is comparable to that of the standard duodenoscope. Following orthogonal compensation, the deviation angles of the motion planes is reduced by 32% to 98%. Post-hysteresis compensation, the root mean square error (RMSE) of the output angle of the distal end is decreased from 8.347° to 4.826°. The accuracy of distal end contact force prediction was approximately ±25% under conditions of high contact force. In conclusion, the modification and control strategy we proposed can achieve relatively safe and precise control of bending section, laying the foundation for the subsequent roboticization of duodenoscope systems for ERCP procedures.
Citation: Machines
PubDate: 2024-07-23
DOI: 10.3390/machines12080500
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 501: Pose Selection Based on a Hybrid Observation
Index for Robotic Accuracy Improvement
Authors: Tiewu Xiang, Chunhui Gao, Baoan Du, Guifang Qiao, Hongfu Zuo
First page: 501
Abstract: The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration.
Citation: Machines
PubDate: 2024-07-24
DOI: 10.3390/machines12080501
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 502: Designing an Experimental Platform to Assess
Ergonomic Factors and Distraction Index in Law Enforcement Vehicles during
Mission-Based Routes
Authors: Marvin H. Cheng, Jinhua Guan, Hemal K. Dave, Robert S. White, Richard L. Whisler, Joyce V. Zwiener, Hugo E. Camargo, Richard S. Current
First page: 502
Abstract: Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations.
Citation: Machines
PubDate: 2024-07-24
DOI: 10.3390/machines12080502
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 503: A Prediction Model of Two-Sided Unbalance in
the Multi-Stage Assembled Rotor of an Aero Engine
Authors: Lingling Song, Yue Chen
First page: 503
Abstract: In rotating machinery with a multi-stage assembled rotor, such as is found in aero engines, any unbalance present will undergo unknown changes at each stage when rotating the assembly phases of the rotor. Repeated disassembly and adjustments are often required to meet the rotor’s residual unbalance specifications. Therefore, developing a prediction model of this two-sided unbalance for a multi-stage assembled rotor is crucial for improving the first-time assembly pass rate and assembly efficiency. In this paper, we propose a prediction model of the two-sided unbalance seen in the multi-stage assembled rotor of an aero engine. Firstly, a method was proposed to unify the mass feature parameters of each stage’s rotor into a geometric measurement coordinate system, achieving the synchronous transmission of geometric and mass feature parameters during the assembly process of the multi-stage rotor. Building upon this, a linear parameter equation of the actual rotation axis of the multi-stage rotor was established. Based on this axis, the mass eccentricity errors of the rotor were calculated at each stage, further enabling the accurate prediction of two-sided unbalance and its action phase in a multi-stage rotor. The experimental results indicate that the maximum prediction errors of the two-sided unbalance and its action phase for a four-stage rotor are 9.6% and 2.5%, respectively, when using this model, which is a reduction of 53.0% and 38.1% compared to the existing model.
Citation: Machines
PubDate: 2024-07-24
DOI: 10.3390/machines12080503
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 504: Fault Diagnosis in Drones via Multiverse
Augmented Extreme Recurrent Expansion of Acoustic Emissions with
Uncertainty Bayesian Optimisation
Authors: Tarek Berghout, Mohamed Benbouzid
First page: 504
Abstract: Drones are a promising technology performing various functions, ranging from aerial photography to emergency response, requiring swift fault diagnosis methods to sustain operational continuity and minimise downtime. This optimises resources, reduces maintenance costs, and boosts mission success rates. Among these methods, traditional approaches such as visual inspection or manual testing have long been utilised. However, in recent years, data representation methods, such as deep learning systems, have achieved significant success. These methods learn patterns and relationships, enhancing fault diagnosis, but also face challenges with data complexity, uncertainties, and modelling complexities. This paper tackles these specific challenges by introducing an efficient representation learning method denoted Multiverse Augmented Recurrent Expansion (MVA-REX), allowing for an iterative understanding of both learning representations and model behaviours and gaining a better understanding of data dependencies. Additionally, this approach involves Uncertainty Bayesian Optimisation (UBO) under Extreme Learning Machine (ELM), a lighter neural network training tool, to tackle both uncertainties in data and reduce modelling complexities. Three main realistic datasets recorded based on acoustic emissions are involved in tackling propeller and motor failures in drones under realistic conditions. The UBO-MVA Extreme REX (UBO-MVA-EREX) is evaluated under many, error metrics, confusion matrix metrics, computational cost metrics, and uncertainty quantification based on both confidence and prediction interval features. Application compared to the well-known long-short term memory (LSTM), under Bayesian optimisation of the approximation error, demonstrates performances, certainty, and cost efficiency of the proposed scheme. More specifically, the accuracy obtained by UBO-MVA-EREX, ~0.9960, exceeds the accuracy of LSTM, ~0.9158, by ~8.75%. Besides, the search time for UBO-MVA-EREX is ~0.0912 s, which is ~98.15% faster than LSTM, ~4.9287 s, making it highly applicable for such challenging tasks of fault diagnosis-based acoustic emission signals of drones.
Citation: Machines
PubDate: 2024-07-26
DOI: 10.3390/machines12080504
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 505: Detection of Contamination and Failure in
the Outer Race on Ceramic, Metallic, and Hybrid Bearings through AI Using
Magnetic Flux and Current
Authors: Jonathan Cureño-Osornio, Geovanni Díaz-Saldaña, Roque A. Osornio-Rios, Larisa Dunai, Lilia Sava, Jose A. Antonino-Daviu, Israel Zamudio-Ramírez
First page: 505
Abstract: Bearings are one of the most essential elements in an induction motor, and they are built with different materials and constructions according to their application. These components are usually one of the most failure-prone parts of an electric motor, so correct and accurate measurements, instrumentation, and processing methods are required to prevent and detect the presence of different failures. This work develops a methodology based on the fusion of current and magnetic stray flux signals, calculation of statistical and non-statistical indicators, genetic algorithms (GAs), linear discriminant analysis (LDA), and neural networks. The proposed approach achieves a diagnostic effectiveness of 99.8% for detecting various damages in the outer race at 50 Hz frequency and 96.6% at 60 Hz. It also demonstrates 99.8% effectiveness for detecting damages in the presence of contaminants in lubrication at 50 Hz and 97% at 60 Hz. These results apply across metallic, ceramic, and hybrid bearings.
Citation: Machines
PubDate: 2024-07-27
DOI: 10.3390/machines12080505
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 506: A Novel Grasp Detection Algorithm with
Multi-Target Semantic Segmentation for a Robot to Manipulate Cluttered
Objects
Authors: Xungao Zhong, Yijun Chen, Jiaguo Luo, Chaoquan Shi, Huosheng Hu
First page: 506
Abstract: Objects in cluttered environments may have similar sizes and shapes, which remains a huge challenge for robot grasping manipulation. The existing segmentation methods, such as Mask R-CNN and Yolo-v8, tend to lose the shape details of objects when dealing with messy scenes, and this loss of detail limits the grasp performance of robots in complex environments. This paper proposes a high-performance grasp detection algorithm with a multi-target semantic segmentation model, which can effectively improve a robot’s grasp success rate in cluttered environments. The algorithm consists of two cascades: Semantic Segmentation and Grasp Detection modules (SS-GD), in which the backbone network of the semantic segmentation module is developed by using the state-of-the-art Swin Transformer structure. It can extract the detailed features of objects in cluttered environments and enable a robot to understand the position and shape of the candidate object. To construct the grasp schema SS-GD focused on important vision features, a grasp detection module is designed based on the Squeeze-and-Excitation (SE) attention mechanism, to predict the corresponding grasp configuration accurately. The grasp detection experiments were conducted on an actual UR5 robot platform to verify the robustness and generalization of the proposed SS-GD method in cluttered environments. A best grasp success rate of 91.7% was achieved for cluttered multi-target workspaces.
Citation: Machines
PubDate: 2024-07-27
DOI: 10.3390/machines12080506
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 507: Punching Accuracy in the Case of
Square-Shaped Holes
Authors: Gheorghe Nagîț, Andrei Marius Mihalache, Irina Beșliu-Băncescu, Oana Dodun, Liviu Andrușcă, Adelina Hrițuc, Sergiu Constantin Olaru, Laurențiu Slatineanu
First page: 507
Abstract: Currently, some parts made from workpieces in the form of sheets or strips are obtained by applying piercing processes. In the research whose results are presented in the article, square-shaped holes were made to evaluate the piercing accuracy of the surfaces generated by piercing in sheets of three different metal materials (carbon steel 1.033, aluminum alloy AlMg3, and stainless steel X5CrNi18-10) with different thicknesses. Apart from the nature of the material, the thickness of the sheet, the size of the clearance between the puncher and the die, and the average speed of the puncher were considered as input factors in the piercing process. The output parameters tracked were the size of the side of the square-shaped hole and the height of the burr generated by the cut. The experimental results were mathematically processed using special software, obtaining empirical mathematical models that highlight the influence of the input factors considered on the deviation of the side size of the square-shaped hole and the burr height. The analysis of the results showed that the intended size of the square hole side exerts the strongest influence on the piercing accuracy. The factor with the most significant influence on the height of the burr is the clearance between the puncher and the die.
Citation: Machines
PubDate: 2024-07-27
DOI: 10.3390/machines12080507
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 508: The Development of Simulation and
Optimisation Tools with an Intuitive User Interface to Improve the
Operation of Electric Arc Furnaces
Authors: Simon Tomažič, Igor Škrjanc, Goran Andonovski, Vito Logar
First page: 508
Abstract: The paper presents a novel decision support system designed to improve the efficiency and effectiveness of decision-making for electric arc furnace (EAF) operators. The system integrates two primary tools: the EAF Simulator, which is based on advanced mechanistic models, and the EAF Optimiser, which uses data-driven models trained on historical data. These tools enable the simulation and optimisation of furnace settings in real time and provide operators with important insights. A key objective was to develop a user-friendly interface with the Siemens Insights Hub Cloud Service and Node-RED that enables interactive management and support. The interface allows operators to analyse and compare past and simulated batches by adjusting the input data and parameters, resulting in improved optimisation and reduced costs. In addition, the system focuses on the collection and pre-processing of input data for the simulator and optimiser and uses Message Queuing Telemetry Transport (MQTT)communication between the user interfaces and models to ensure seamless data exchange. The EAF Simulator uses a comprehensive mathematical model to simulate the complex dynamics of heat and mass transfer, while the EAF Optimiser uses a fuzzy logic-based approach to predict optimal energy consumption. The integration with Siemens Edge Streaming Analytics ensures robust data collection and real-time responsiveness. The dual-interface design improves user accessibility and operational flexibility. This system has significant potential to reduce energy consumption by up to 10% and melting times by up to 15%, improving the efficiency and sustainability of the entire process.
Citation: Machines
PubDate: 2024-07-28
DOI: 10.3390/machines12080508
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 509: Thermal Error Prediction for Vertical
Machining Centers Using Decision-Level Fusion of Multi-Source
Heterogeneous Information
Authors: Yue Han, Xiaolei Deng, Junjian Zheng, Xiaoliang Lin, Xuanyi Wang, Yong Chen
First page: 509
Abstract: To address the limitations in predictive capabilities of thermal error models built from single-source, single-structure data, this paper proposes a thermal error prediction model based on decision-level fusion of multi-source heterogeneous information to enhance prediction accuracy. First, an experimental platform for multi-source heterogeneous information acquisition was constructed to collect thermal error data from different signal sources (multi-source) and different structures (heterogeneous). Next, based on the characteristics of the multi-source and heterogeneous data, relevant features were extracted to construct the feature set. Then, using the feature information set of the multi-source and heterogeneous data, thermal error prediction sub-models were established using Nonlinear Autoregressive models with exogenous inputs (NARX) and Gated Recurrent Units (GRUs) for a vertical machining center spindle. Finally, the entropy weight method was employed to assign the weights for the linear-weighted fusion rule, achieving decision-level fusion of multi-source heterogeneous information to obtain the final prediction result. This result was then compared with experimental results and the prediction results of single-source models. The findings indicate that the proposed thermal error prediction model closely matches the actual results and outperforms the single-source and single-structure data models in terms of Root-Mean-Square Error (RMSE), Coefficient of Determination (R2), and Mean Absolute Error (MAE).
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080509
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 510: Current Status of Research on Hybrid Ceramic
Ball Bearings
Authors: Bing Su, Chunhao Lu, Chenghui Li
First page: 510
Abstract: Rolling element bearings are essential components in modern mechanical equipment, providing crucial support for rotating parts. Hybrid ceramic ball bearings, consisting of steel rings and ceramic balls, have gained popularity in high-speed machinery to enhance performance. These bearings offer advantages such as longer fatigue life, improved performance, and higher speeds. Extensive research by scholars has been conducted to promote the wider adoption of hybrid ceramic ball bearings. This paper compiles relevant studies on hybrid ceramic bearings, organizing literature related to their lifetime, arranging literature pertaining to their performance analysis from the perspective of analytical methods, and collating literature on their lubrication techniques from the angle of lubrication methods. This paper covers research on lifetime modeling, fatigue spalling, wear, mechanical and tribological properties, dynamic performance, thermal analysis, temperature considerations, and lubrication techniques of hybrid ceramic ball bearings. The aim is to provide readers and researchers with a comprehensive overview of these innovative bearings.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080510
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 511: The Effect of Cycloid Gear Wear on the
Transmission Accuracy of the RV Reducer
Authors: Yourui Tao, Huishan Liu, Miaojie Wu, Nanxian Zheng, Jiaxing Pei
First page: 511
Abstract: The cycloid gear wear of RV reducers leads to the degradation of the industrial robots’ transmission accuracy, but the degradation law with respect to the wear volume is still unclear. In this paper, a method for determining transmission error (TE) through a combination of numerical and simulation analysis is proposed. The wear model of cycloid gear was ascertained based on the theory of Archard. Then, the full rigid body and rigid–flexible coupling model of RV reducers were established using the multibody dynamics theory. Finally, the static transmission error (STE) and dynamic transmission error (DTE) were investigated. The results show that as working hours increase, the cycloid gear wear volume increases, and transmission accuracy deteriorates, but the rate tends to slow down.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080511
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 512: Optimization of CNC Working Time Depending
on the Positioning of the Tools in the Magazine
Authors: Arbnor Pajaziti, Orlat Tafilaj, Enise Hasanaj, Afrim Gjelaj
First page: 512
Abstract: This paper addresses the optimization of CNC (Computer Numerical Control) working time through the strategic positioning and grouping of cutting tools. Our analysis primarily focused on evaluating the previous arrangement of tools in the magazine and exploring the potential for repositioning them to enhance CNC operational efficiency. The work methodology is based on the collection of direct data from CNC as well as through CAD/CAM simulations. The collected data are analyzed with the Python programming language, which is one of the most used and efficient languages now. By grouping the tools according to the same function, tools were repositioned next to each other, which we obtained. After collecting the data and processing them in the Python programming language, we made a visualization of the data. Working time is reduced from 27.44 s to 26.46 s and results are analyzed in the Python Programming language. The working time is reduced by 0.98 s, which is approximately a 3.57% reduction in the working time. This indicates a significant improvement in efficiency due to the optimization of the cutting tool positions in the magazine.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080512
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 513: Computational Fluid Dynamics-Based
Optimisation of High-Speed and High-Performance Bearingless Cross-Flow Fan
Designs
Authors: Ivana Bagaric, Daniel Steinert, Thomas Nussbaumer, Johann Walter Kolar
First page: 513
Abstract: To enhance the fluid dynamic performance of bearingless cross-flow fans (CFFs), this paper presents a CFD-based optimisation of both rotor and static casing wall modifications. High-performance CFFs are essential in industrial applications such as highly specialised laser modules in the semiconductor industry. The goal for the investigated rotor modifications is to enhance the CFF’s mechanical stiffness by integrating reinforcing shafts, which is expected to increase the limiting bending resonance frequency, thereby permitting higher rotational speeds. Additionally, the effects of these rotor modifications on the fluid dynamic performance are evaluated. For the casing wall modifications, the goal is to optimise design parameters to reduce losses. Optimised bearingless CFFs benefit semiconductor manufacturing by improving the gas circulation system within the laser module. Higher CFF performance is a key enabler for enhancing laser performance, increasing the scanning speed of lithography machines, and ultimately improving chip throughput. Several numerical simulations are conducted and validated using various commissioned prototypes, each measuring 600mm in length and 60mm in outer diameter. The results reveal that integrating a central shaft increases the rotational speed by up to 42%, from 5000rpm to 7100rpm, due to enhanced CFF stiffness. However, the loss in fluid flow amounts to 61% and outweighs the gain in rotational speed. Optimising the casing walls results in a 22% increase in maximum fluid flow reaching 1800m3/h at 5000rpm. It is demonstrated that the performance of bearingless CFFs can be enhanced by modifying the geometry of the casing walls, without requiring changes to the CFF rotor or bearingless motors.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080513
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 514: Simulation Study on Arc Temperature of Urban
Rail DC Pantograph-Catenary and Arc Ablation of Contact Line
Authors: Xiaoying Yu, Ze Wang, Mengjie Song, Liying Song, Junrui Yang, Yang Su
First page: 514
Abstract: The high temperature generated by the DC pantograph-catenary arc of urban rail systems will aggravate the wear of the pantograph-catenary system. When the ablation intensifies, it will lead to disconnection accidents on the contact line. In this paper, through the establishment of a pantograph-catenary arc model and contact line arc ablation model, considering the flow of the molten pool, it is reported that the temperature distribution of the pantograph-catenary arc is axisymmetric. With the increase in the arcing time, the maximum temperature of the arc increases. The heat flux density of the arc injection contact line presents a Gaussian distribution and is positively correlated with the arcing time. The high-temperature area of the contact line and the distribution of the molten pool show an approximate arc shape. The velocity of the molten pool shows a symmetrical distribution about the center of the electrode. The area, depth, and radius of the molten pool of the contact line increase with an increase in the arcing time, and the radius of the molten pool is always greater than the depth of the molten pool. The work presented in this paper is helpful to further our understanding of the basic physical process of pantograph-catenary arc ablation of contact lines.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080514
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 515: Sensitivity Analysis of Bogie Wheelbase and
Axle Load for Low-Floor Freight Wagons, Based on Wheel Wear
Authors: David S. Pellicer, Emilio Larrodé
First page: 515
Abstract: This paper shows the usage of a numerical analysis model that enables the calculation of the life of railway wheels used for low-floor freight wagons as a function of its primary operating factors, which allows for carrying out sensitivity analyses. Low-floor wagons are being increasingly used for combined transport applications, and many types of bogies have been proposed to constitute the wagons. Due to the uniqueness of this type of wagon, the bogie configurations in terms of wheelbase and axle load have hardly been analyzed so far. The numerical analysis model used addresses the primary challenges that arise in the vehicle–track interaction and establishes the relations among them. The main aspects of this model have been described in this paper, which has been later used to calculate the life of an ordinary-diameter wheel for several wheelbase and axle load values. This study has been replicated with reduced-diameter wheels, which are commonly used for low-floor wagons. In this way, it is possible to know the evolution of the life depending on the wheelbase and the axle load. The observed behaviors are not so dissimilar for the different types of wheels, and they point out huge increases in wear as the axle load and the wheelbase rise, especially with axle load. The root causes can be explained by the entire understanding of the rolling phenomenon provided by the full analytical work.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080515
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 516: A Method to Obtain Frequency Response
Functions of Operating Mechanical Systems Based on Experimental Modal
Analysis and Operational Modal Analysis
Authors: Cunrui Shen, Chihua Lu
First page: 516
Abstract: The characteristics of a mechanical structure under operating conditions may differ from those in a static state. It is often more desirable to obtain the frequency response function (FRF) of the operating structure in engineering applications. While operational modal analysis (OMA) can estimate modal parameters during operation, it fails to provide mass-normalized mode shapes for FRF synthesis. This paper presents a new method using experimental modal analysis (EMA) to compensate for the absent information in OMA. It categorizes operational mode shapes into changed ones and those that remain the same compared to the static state, applying different scaling techniques accordingly. This method adapts to changes in dynamic characteristics without altering the operating conditions. Stability is emphasized throughout the process. Two examples are provided to verify the method, considering noise and incompleteness in measurement, and disturbances in dynamic properties. The proposed method is proven to be feasible and reliable to capture the changes in operational FRFs.
Citation: Machines
PubDate: 2024-07-29
DOI: 10.3390/machines12080516
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 517: Optimization Design of Cogging Torque for
Electric Power Steering Motors
Authors: Guoguang Zhang, Peng Hou
First page: 517
Abstract: Excessive cogging torque can cause torque fluctuations, noise, and vibration in electric power steering (EPS) motors, which is a key factor in the high-precision and high-performance optimization design of EPS motors for electric vehicles. This article takes a 12-slot 10-pole electric power steering motor for a certain car as an example. By establishing the corresponding electromagnetic field model and theoretical analysis of the motor, the influence of the pole arc coefficient and eccentricity parameters of the permanent magnet on the cogging torque of the electric power steering motor is explored. A comprehensive optimization scheme for reducing the cogging torque of the motor structure is proposed. The effectiveness of the designed scheme was verified through finite-element simulation and experimental testing of motor electromagnetism. Compared with the original design, the optimized structure of the EPS motor resulted in an 86.62% reduction in cogging torque during experimental testing.
Citation: Machines
PubDate: 2024-07-30
DOI: 10.3390/machines12080517
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 518: Extrusion-Based Bioprinting in a
Cost-Effective Bioprinter
Authors: Jones Joseph Jebaraj Dharmaraj, Rajesh Jesudoss Hynes Navasingh, Grzegorz Krolczyk, Shenbaga Velu Pitchumani
First page: 518
Abstract: Three-dimensional (3D) bioprinting has emerged as a revolutionary approach in the life sciences, combining multiple disciplines such as computer engineering, materials science, robotics, and biomedical engineering. This innovative technology enables the production of cellular constructs using bio-inks, and differs from conventional 3D printing by incorporating living cells. The present work addresses the conversion of a commercial thermoplastic 3D printer into a low-cost bioprinter. The modification addresses the challenges of the high cost of commercial bioprinters, limited adaptability, and specialized personnel requirements. This modification uses an extrusion-based bioprinting method that is particularly popular in research due to its viscosity tolerance and versatility. The individual steps, including replacing the extruder with a syringe pump, rebuilding the electronic motherboard, and configuring the firmware, are explained in detail. The work aims at providing access to bioprinting technology so that laboratories with modest resources can take advantage of the immense potential of this technology. This modification resulted in improved resolution, allowing submicron movements, which is comparable to some of the commercially available bioprinters. The accuracy of the modified printer was validated using hydrogel bioprinting tests, suggesting that it is suitable for broader applications in regenerative medicine.
Citation: Machines
PubDate: 2024-07-30
DOI: 10.3390/machines12080518
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 519: Design and Development of a Flexible
Manufacturing Cell Controller Using an Open-Source Communication Protocol
for Interoperability
Authors: Evangelos Tzimas, George Papazetis, Panorios Benardos, George-Christopher Vosniakos
First page: 519
Abstract: Flexible manufacturing cells provide significant advantages in low-volume mass-customization production but also induce added complexity and technical challenges in terms of integration, control, and extensibility. The variety of closed-source industrial protocols, the heterogeneous equipment, and the product’s manufacturing specifications are main points of consideration in the development of such a system. This study aims to describe the approach, from concept to implementation, for the development of the controller for a flexible manufacturing cell consisting of heterogeneous equipment in terms of functions and communication interfaces. Emphasis is put on the considerations and challenges for effective integration, extensibility, and interoperability. Scheduling and monitoring performed by the developed controller are demonstrated for a manufacturing cell producing microfluidic devices (bioMEMS) that consists of six workstations and a robot-based handling system. Communication between the system controller and the workstations was based on open-source technologies instead of proprietary software and protocols, to support interoperability and, to a considerable extent, code reusability.
Citation: Machines
PubDate: 2024-07-30
DOI: 10.3390/machines12080519
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 520: Multidisciplinary Collaborative Design
Optimization ofElectric Shovel Working Devices
Authors: Juan Wu, Junkang Zhao, Xin Wang, Baoguo Lin
First page: 520
Abstract: The development of the open-pit mining industry has set higher performance standards for mining electric shovels. Addressing issues such as low efficiency, high energy consumption, and high failure rates in working mining electric shovel devices, this paper comprehensively considers bulk mechanics, structural mechanics, and dynamics to conduct a multidisciplinary, collaborative design optimization for electric shovels by introducing the BLISCO method, which is based on an approximated model, into the structural-optimization design process of working electric shovel devices, aiming to enhance the overall performance of electric shovels. Firstly, a dynamic model of an electric shovel is established to analyze the hoist force and crowd force during the excavation process, and an accurate load input for the dynamic analysis is provided through the bulk material mechanics model. Additionally, to ensure that the stiffness of the boom meets the requirements, the maximum stress at the most critical position of the optimized boom is considered. Subsequently, the design variables are screened through experimental design, and an approximate model is established. Focusing on the hoist force, crowd force, maximum stress at the critical position of the boom, and the angle between the dipper arm and the wire rope, a mathematical model is constructed and optimized using a two-level integrated system co-optimization framework based on an approximate model (BLISCO-AM), followed by a simulation. Finally, a test bench for the electric shovel working device is constructed to compare pre- and post-optimization performance. Experimental results show that through the optimized design, the hoist force and crowd force required in a single excavation process are reduced by 6% and 8.48%, respectively, and the maximum angle between the wire rope and the dipper arm is increased by 4%, significantly improving excavation efficiency while ensuring the safety and reliability of the equipment.
Citation: Machines
PubDate: 2024-07-30
DOI: 10.3390/machines12080520
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 521: On the Optimization of Robot Machining: A
Simulation-Based Process Planning Approach
Authors: Thanassis Souflas, Christos Gerontas, Harry Bikas, Panagiotis Stavropoulos
First page: 521
Abstract: The use of industrial robots for machining operations is pursued by industry lately, since they can increase the flexibility of the production system and reduce production costs. However, their industrial adoption is still limited, mainly due to their insufficient structural stiffness and posture-dependent dynamic behavior, leading to limited machining process accuracy. For this purpose, the Digital-Model of a machining robot has been developed, providing a tool for virtual commissioning of the process that can be used during the process planning stage. The Multi-Body Simulation method combined with a Component Mode Synthesis have been adopted, considering flexibility of both the joints and links. On top of that, and motivated from robotic-based machining systems’ flexibility and versatility, two optimization algorithms have been developed, attempting to increase the process accuracy. A workpiece placement optimization algorithm, attempting to maximize the robot stiffness during the process acquiring knowledge from the robot stiffness maps, and a feed-rate scheduling algorithm, attempting to constrain the contour error by regulating the generated cutting forces. The capabilities and functionality of the developed model and optimization algorithms are showcased in two different case studies, with the results proving the improvements on the process accuracy after the application of the optimization algorithms. Finally, an experimental validation of the Digital-Model has been performed, to confirm the consistency between model outputs and real experimental data.
Citation: Machines
PubDate: 2024-07-31
DOI: 10.3390/machines12080521
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 522: The Formation of a Low-Carbon Steel/Ni-Cr-W
Alloy Bimetallic Material via Liquid–Solid Compound Casting with a
Laser Assisted Solid Surface
Authors: Serhii Salii, Leonid Golovko, Oleksii Kaglyak, Oleksandr Kapustynskyi, Nikolaj Višniakov
First page: 522
Abstract: The aim of this study was to develop a new manufacturing process for bimetallic materials by combining laser treatment with traditional casting methods. This process involves laser-treating nickel alloy-grade UNS 6230 plates to create a regular macro-relief on their surface. These treated plates are then placed in a sand mold, and molten non-alloy steel (S235JRG2) is poured into the mold to create bimetallic layered castings. The experimental procedure focuses on optimizing the melt-to-solid phase ratios and pouring temperatures to achieve a uniform microstructure and strong mechanical properties in the bimetals. The produced bimetallic castings are suitable for applications in the oil refining and chemical industries and heavy machinery sector. The quantitative results indicate that the optimized process parameters lead to a high-quality transition zone with minimal defects, characterized by the diffusion of alloying elements from the nickel alloy to the steel. The microstructure, chemical, and phase compositions were evaluated using XRD and SEM with EDS, confirming the formation of a robust metallurgical bond. Key findings include a significant improvement in the hardness and strength of the transition layer, with the optimal pouring temperature being 1600 °C. The resulting bimetallic materials demonstrate an improved performance in demanding industrial environments.
Citation: Machines
PubDate: 2024-07-31
DOI: 10.3390/machines12080522
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 523: Adaptive Neuro-Fuzzy Inference System-Based
Predictive Modeling of Mechanical Properties in Additive Manufacturing
Authors: Vasileios D. Sagias, Paraskevi Zacharia, Athanasios Tempeloudis, Constantinos Stergiou
First page: 523
Abstract: Predicting the mechanical properties of Additive Manufacturing (AM) parts is a complex task due to the intricate nature of the manufacturing processes. This study presents a novel application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the mechanical properties of PLA specimens produced using Fused Filament Fabrication (FFF). The ANFIS model integrates the strengths of neural networks and fuzzy logic to establish a mapping between the inputs and the output mechanical properties, specifically maximum stress, strain, and Young’s modulus. Experimental data were collected from three-point bending tests conducted on FFF samples fabricated from PLA material with different manufacturing parameters, such as infill pattern, infill, layer thickness, printing speed, extruder and bed temperature, printing orientation (along each axis and twist angle), and raster angle. These data were used to train, check, and validate the ANFIS model. The results reveal that the proposed predictive model can effectively predict the mechanical properties of FFF-printed PLA samples, demonstrating its potential for broader applications across various AM technologies and materials, ultimately enhancing the efficiency and effectiveness of the AM fabrication process.
Citation: Machines
PubDate: 2024-07-31
DOI: 10.3390/machines12080523
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 524: Human Reliability Assessment of Space
Teleoperation Based on ISM-BN
Authors: Hongrui Zhang, Shanguang Chen, Rongji Dai
First page: 524
Abstract: Space teleoperation systems, as complex giant systems, feature performance-influencing factors that are interrelated. Accurately describing the dependence between these factors is crucial for constructing a human factor reliability assessment (HRA) model. Moreover, data scarcity has consistently been a challenge in space HRA. There are primarily two types of data in this domain: expert judgment data and empirical data (simulation data, actual reports), each with complementary effects. The expert judgment data, although subjective, are readily accessible, while empirical data provide robust objectivity but are difficult to obtain. Addressing these challenges, this paper constructs an HRA model for space teleoperation that combines Interpretive Structural Modeling (ISM) with a two-stage Bayesian update method. This model reflects the dependencies between factors and accommodates multisource data (expert judgment and experimental data). With more empirical data, the model can be continuously updated and refined to yield increasingly accurate evaluations of human error probability (HEP). The validity of the model was verified through the analysis of 52 space incidents using the N-K model. The study provides a methodological foundation for HRA in other space missions.
Citation: Machines
PubDate: 2024-07-31
DOI: 10.3390/machines12080524
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 525: Improvement and Fusion of D*Lite Algorithm
and Dynamic Window Approach for Path Planning in Complex Environments
Authors: Yang Gao, Qidong Han, Shuo Feng, Zhen Wang, Teng Meng, Jingshuai Yang
First page: 525
Abstract: Effective path planning is crucial for autonomous mobile robots navigating complex environments. The “global–local” coupled path planning algorithm exhibits superior global planning capabilities and local adaptability. However, these algorithms often fail to fully realize their potential due to low efficiency and excessive constraints. To address these issues, this study introduces a simpler and more effective integration strategy. Specifically, this paper proposes using a bi-layer map and a feasible domain strategy to organically combine the D*Lite algorithm with the Dynamic Window Approach (DWA). The bi-layer map effectively reduces the number of nodes in global planning, enhancing the efficiency of the D*Lite algorithm. The feasible domain strategy decreases constraints, allowing the local algorithm DWA to utilize its local planning capabilities fully. Moreover, the cost functions of both the D*Lite algorithm and DWA have been refined, enabling the fused algorithm to cope with more complex environments. This paper conducts simulation experiments across various settings and compares our method with A_DWA, another “global–local” coupled approach, which combines A* and DWA. D_DWA significantly outperforms A_DWA in complex environments, despite a 7.43% increase in path length. It reduces the traversal of risk areas by 71.95%, accumulative risk by 80.34%, global planning time by 26.98%, and time cost by 35.61%. Additionally, D_DWA outperforms the A_Q algorithm, a coupled approach validated in real-world environments, which combines A* and Q-learning, achieving reductions of 1.34% in path length, 67.14% in traversal risk area, 78.70% in cumulative risk, 34.85% in global planning time, and 37.63% in total time cost. The results demonstrate the superiority of our proposed algorithm in complex scenarios.
Citation: Machines
PubDate: 2024-08-01
DOI: 10.3390/machines12080525
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 526: Kinetostatics of a Snake Robot with
Redundant Degrees of Freedom
Authors: Dong-Jie Zhao, Han-Lin Sun, Zhao-Cai Du, Yan-Bin Yao, Jing-Shan Zhao
First page: 526
Abstract: This paper proposes a kinetostatic approach for analyzing the joint torques of a redundant snake robot. The method is suitable for weightless space environments. With the high degree of freedom and flexible cable actuation, the redundant snake robot is well-suited for utilization in space-weightless environments. This method reduces computational cost by using the multiplication of matrices and vectors instead of inverse matrices. Taking advantage of the velocity screw (twist) and force screw (wrench), this strategy provides an idea for redundant serial robots to achieve the calculation of joint torques. This methodology is straightforward for programming and has good computational efficiency. The instantaneous work performed by the actuation is expressed with the force screw. According to the principle of virtual work, the kinetostatic equation of the robot can be obtained and the torque required for each joint can be determined. Meanwhile, to solve the inertia force generated by joint acceleration, D’Alembert’s principle is adopted to transform the dynamic problem into a static problem. Through kinetostatic analysis of a redundant snake robot, this paper shows the approach of establishing the kinetostatic model to calculate the torque in screw form. At the same time, the actuation distribution of the redundant snake robot is also cracked effectively for practical purposes. Due to the difficulty of achieving weightless space environments, this paper validates the method by using ADAMS simulation without gravity in the simulation.
Citation: Machines
PubDate: 2024-08-01
DOI: 10.3390/machines12080526
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 527: Design of Soft Robots: A Review of Methods
and Future Opportunities for Research
Authors: Behzad Hasanshahi, Lin Cao, Ki-Young Song, Wenjun Zhang
First page: 527
Abstract: Soft robots present resilient and adaptable systems characterized by deformable bodies inspired by biological systems. In this paper, we comprehensively review existing design methods for soft robots. One unique feature of our review is that we first formulate criteria, which enables us to derive knowledge gaps and suggest future research directions to close these gaps and go further. Another distinctive feature of our review is that we pivot on the general engineering design process for soft robots. As such, we consider three criteria: (1) the availability of design requirements to start with the design of soft robots, (2) the availability of the so-called concept design or architecture design for soft robots, and (3) the systematic process that leads to the final design of soft robots. The review is conducted systematically, especially when searching for and selecting relevant publications in the literature. The main contribution of this review includes (i) identifying knowledge gaps and (ii) suggesting future research directions to close these gaps and go further.
Citation: Machines
PubDate: 2024-08-01
DOI: 10.3390/machines12080527
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 528: Virtual Reality for Training in Assembly and
Disassembly Tasks: A Systematic Literature Review
Authors: Valentina Di Pasquale, Paolo Cutolo, Carmen Esposito, Benedetta Franco, Raffaele Iannone, Salvatore Miranda
First page: 528
Abstract: The evolving landscape of industrial manufacturing is increasingly embracing automation within smart factories. However, the critical role of human operators, particularly in manual assembly and disassembly tasks, remains undiminished. This paper explores the complexities arising from mass customization and remanufacturing, which significantly enhance the intricacy of these manual tasks. Human involvement is essential in these tasks due to their complexity, necessitating a structured learning process to enhance efficiency and mitigate the learning–forgetting cycle. This study focuses on the utilization of virtual reality (VR) as an innovative training tool to address these challenges. By conducting a systematic literature review (SLR) on the impact of VR on training operators for assembly and disassembly tasks, this paper evaluates the current level of VR application, the used technologies, the operator performance, and the VR benefits and limitations. The analysis reveals a limited but promising application of VR in training, highlighting its potential to improve learning outcomes, productivity, and safety while reducing costs. However, the research also identifies gaps in the practical application of VR for training purposes suggesting a future research agenda to explore its full potential.
Citation: Machines
PubDate: 2024-08-02
DOI: 10.3390/machines12080528
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 529: Exhaustive Enumeration of Spatial Prime
Structures
Authors: Takahiro Aruga, Nobuyuki Iwatsuki
First page: 529
Abstract: Prime structures are link chains with 0 DoF (degrees of freedom), not including subchains with 0 or fewer DoF, which are expected to be used in systematic kinematic and dynamic analyses of link mechanisms. This paper describes the exhaustive enumeration of spatial prime structures with three–five links. There will be more types of spatial prime structures than planar prime structures due to the variety in the DoF of kinematic pairs and the existence of prime structures with idle DoF. In the enumeration, the graphs which represent the connection of links and pairs are used. The vertices of the graphs represent the links of structures, and the edges represent the kinematic pairs. To consider the pair DoF, weights are set on edges. First, the numbers of pairs are calculated using Grübler’s equation. Second, the graphs corresponding to structures are enumerated, without considering pair DoF, and the isomorphic graphs and other inappropriate graphs are eliminated. Then, the combinations of pair DoF arrangement are enumerated, and the isomorphic graphs and graphs which correspond to non-prime structures are eliminated. Finally, prime structures with idle DoF are considered. As a result, 3, 13, and 97 kinds of spatial prime structures for 3, 4, and 5 links, respectively, are obtained.
Citation: Machines
PubDate: 2024-08-02
DOI: 10.3390/machines12080529
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 530: Semiactive Car-Seat System for Rear-End
Collisions
Authors: Ali Gunes Kaya, Selcuk Himmetoglu
First page: 530
Abstract: This study proposes and simulates a smart system that can be used in production car seats to decrease whiplash risk in rear-end crashes. A sliding seat incorporating a semiactively controlled magnetorheological (MR) damper model positioned under the seat-pan is simulated with a validated biofidelic human body model. Since this is the first study that demonstrates a computer controlled anti-whiplash car seat system to the best of the authors’ knowledge, a benchmark analysis is carried out to compare the proposed semiactive seat with a state-of-the-art passive anti-whiplash car seat using 23 different crash pulses, including the moderate and high severity crash pulses within the European New Car Assessment Program (EuroNCAP) whiplash risk assessment framework. The proposed semiactive design outperforms the passive seat design by further reducing the values of the critical EuroNCAP whiplash criteria, such as NIC and Nkm, together with the loads acting on the upper neck. The semiactive seat lowers the upper-neck shear force by an amount of 4 kg and 7 kg while lessening the NIC by 10% and 21% and Nkm by 9% and 56% for the EuroNCAP crash pulses, having a delta-V of 16 km/h and 24 km/h, respectively. The findings presented in this paper can aid in the design of car seats to further mitigate whiplash risk in rear-end crashes.
Citation: Machines
PubDate: 2024-08-03
DOI: 10.3390/machines12080530
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 531: Experimental Study on Vibration and Noise
Reduction of Gear Transmission System Based on ISFD
Authors: Gang Zhu, Lidong He, Xingyun Jia, Zhifu Tan, Qingwang Qin
First page: 531
Abstract: Gear transmission systems are widely used in ship propulsion systems, but, during operations, they produce serious vibration and noise problems, reducing the fatigue life of gears and affecting the performance of ships and the comfort of operators. Taking into account the complex frequencies and vibration components of gear transmission systems, this study conducted wide-band vibration suppression and noise reduction research on a gear transmission system using an integral squeeze film damper (ISFD), providing a novel approach for reducing vibration and noise in gear transmission systems. By conducting a simulation analysis and numerical calculations, the ISFD was analyzed via a static analysis and a dynamic analysis. We developed an experimental platform for reducing vibration and noise in gear transmission systems using sliding bearings, and the experiments were conducted under different speed and load conditions to study the vibration suppression and noise reduction of gears based on the ISFD. The experimental results show that the ISFD has good vibration suppression capabilities for gears at different speeds, with a horizontal vibration reduction of 75.44% and a vertical vibration reduction of 68.48% at 2400 r/min. The ISFD has wide-band vibration suppression capabilities, especially for mesh frequency and twice-mesh frequency, with a vibration reduction of 86.96% or more. Moreover, the ISFD has good vibration suppression capabilities for gears at different load torques, with a reduction of more than 35% in all directions. In addition, the ISFD also has noise reduction capabilities, reducing the gears’ noise by 1.92 dB at different speeds.
Citation: Machines
PubDate: 2024-08-05
DOI: 10.3390/machines12080531
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 532: Effect of Roundness Error of the Grooves on
the Inner Ring Runout of Angular Contact Ball Bearings
Authors: Di Cui, Yongjian Yu, Yujun Xue, Pengge Guo, Hongbiao Han, Haichao Cai
First page: 532
Abstract: In this paper, a prediction model of the inner ring runout of angular contact ball bearings is established according to the geometric and kinematic relationships of the bearing, considering factors such as the roundness error of the inner and outer grooves, the dimensional error of the balls, and the change of the contact angle between the balls and the grooves. The correctness of the model is verified through experiments. The effects of the order and amplitude of the roundness error of the inner groove and the order and amplitude of the roundness error of the outer groove on the inner ring runout are analyzed. The coupling effect of the roundness error of the inner and outer grooves on the inner ring runout is further analyzed. The results show that the inner ring runout changes periodically with a change to the roundness error order of the grooves, which increases with an increase in the roundness error amplitude. Under the coupling of the roundness error of the inner and outer grooves, the magnification of the inner ring runout increases as a whole. When there are specific relationships between the roundness error orders of the grooves and the number of balls, the magnification of the axial or radial runout changes significantly.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080532
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 533: The Optimization and Control of the
Engagement Pressure for a Helicopter Dry Clutch
Authors: Yangyang Xiao, Qunming Li, Huisi Liu
First page: 533
Abstract: The engagement quality of a helicopter dry clutch has a significant impact on the service life and overall flight performance of the helicopter. The engagement oil pressure is an important factor affecting the clutch engagement quality. Firstly, a nonlinear input–output dynamic model for the dry clutch is developed to investigate the optimization and control of dry clutch engagement pressure in this paper. Secondly, to efficiently obtain the optimal pressure curve, an optimal method combining the developed dynamic model with the state feedback gain of a linear quadratic optimization regulator (LQR) solver is proposed. Thirdly, considering that hydraulic actuators may struggle with tracking certain pressure curves, a hydraulic actuator for accurately tracking pressure curves based on fuzzy PID is proposed. The simulation results indicate that the developed hydraulic actuator exhibits an excellent tracking performance. Moreover, compared with linear and segmented pressure curves, the optimal pressure curve derived from the proposed method significantly reduces jerk, friction work, and engagement duration, resulting in improved helicopter dry clutch engagement quality.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080533
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 534: Modelling and Design Optimization of a Novel
Compliant XY Positioner for Vibration-Assisted CNC Milling
Authors: Minh Phung Dang, Chi Thien Tran, Hieu Giang Le, Vo Quoc Anh Tran, Hong Van Tran
First page: 534
Abstract: Vibration-assisted machining, known as hybrid processing technology, offers several benefits over conventional machining methods. However, developing mechanical structure designs to generate a non-resonant frequency source remains challenging. The objective of this study is to propose a novel design for an XY flexure positioner by combining the pseudo-rigid-body model with the Lagrange technique, finite element analysis and Crayfish optimization algorithm. Firstly, the mechanism was designed by combining a hybrid amplifier and parallel driving mechanism integrated with right circular hinges to increase the natural frequency and precision for potential application to VAM CNC milling. Then, the analytical model was established by the pseudo-rigid-body and Lagrange method. Next, the theoretical result was verified by finite element analysis. The first natural frequency results of theory and FEM methods were found at 990.74 Hz and 1058.5 Hz, respectively. The error between the two methods was 6.4%, demonstrating a reliable modeling approach. Based on the analytical equations, the Crayfish optimization algorithm was utilized for optimizing the main design variables of the mechanism. Next, the prototype was fabricated. The results showed that the experimental and simulated frequencies were 1127.62 Hz and 1216.6 Hz, with an error between the two methods of 7.31%. Finally, the workpiece was installed on the prototype and a real vibration-assisted CNC milling process was carried out in the frequency range [700 Hz, 1000 Hz]. The best surface roughness of the specimen was achieved at a frequency of 900 Hz with a Ra of 0.287 µm. This demonstrates that the proposed XY mechanism is an effective structure for generating a non-resonant frequency source for vibration-assisted machining.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080534
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 535: Design of Dual Winding Flux Modulation
Machine for Performance Improvement in Variable Speed Application
Authors: Min-Gu Lyeo, Kyu-Yun Hwang, Sung-Hyun Lee
First page: 535
Abstract: In this paper, a Dual Winding Flux Modulation Machine (DWFMM) is proposed for variable speed application. The DWFMM is configured by adding windings to the Single Winding Flux Modulation Machine (SWFMM), consisting of a master winding that drives the motor and a slave winding that enables pole changing and performance enhancement. Through pole changing, the DWFMM can operate as two different machines: a Vernier Machine (VM) for varying speeds and torque operations and a Permanent Magnet Synchronous Machine (PMSM). In the VM mode, flux enhancement is applied to improve torque, and in the PMSM mode, Flux Weakening is applied to increase speed. The characteristics of the two different operating modes were analyzed using the Finite Element Method (FEM) to validate the machine’s performance. Finally, the DWFMM and SWFMM were designed and compared as variable speed application machines to confirm their suitability and superiority.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080535
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 536: An Asymmetric Independently Steerable Wheel
for Climbing Robots and Its Motion Control Method
Authors: Meifeng Lv, Xiaoshun Liu, Lei Xue, Ke Tan, Junhui Huang, Zeyu Gong
First page: 536
Abstract: Climbing robots, with their expansive workspace and flexible deployment modes, have the potential to revolutionize the manufacturing processes of large and complex components. Given that the surfaces to be machined typically exhibit variable curvature, good surface adaptability, load capacity, and motion accuracy are essential prerequisites for climbing robots in manufacturing tasks. This paper addresses the manufacturing requirements of climbing robots by proposing an asymmetric independently steerable wheel (AISW) for climbing robots, along with the motion control method. Firstly, for the adaptability issue of the locomotion mechanism on curved surfaces under heavy load, an asymmetric independently steerable wheel motion module is proposed, which improves the steering difficulty of the traditional independently steerable wheel (ISW) based on the principle of steering assisted by wheels. Secondly, a kinematic model of the AISW chassis is established and, on this basis, a trajectory tracking method based on feedforward and proportional–integral feedback is proposed. Comparative experimental results on large, curved surface components show that the asymmetric independently steerable wheel has lower steering resistance and higher motion accuracy, significantly enhancing the reachability of climbing robots and facilitating their application in the manufacturing of large and complex components.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080536
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 537: A Terminal Residual Vibration Suppression
Method of a Robot Based on Joint Trajectory Optimization
Authors: Liang Liang, Chengdong Wu, Shichang Liu
First page: 537
Abstract: Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080537
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 538: Game-Theoretic Adversarial Interaction-Based
Critical Scenario Generation for Autonomous Vehicles
Authors: Xiaokun Zheng, Huawei Liang, Jian Wang, Hanqi Wang
First page: 538
Abstract: Ensuring safety and efficiency in the rapidly advancing autonomous vehicle (AV) industry presents a significant engineering challenge. Comprehensive performance evaluations and critical scenario testing are essential for identifying situations that AVs cannot handle. Thus, generating critical scenarios is a key problem in AV testing system design. This paper proposes a game-theoretic adversarial interaction method to efficiently generate critical scenarios that challenge AV systems. Initial motion prediction for adversarial and surrounding vehicles is based on kinematic models and road constraints, establishing interaction action spaces to determine possible driving domains. A novel evaluation approach combines reachability sets with adversarial intensity to assess collision risks and adversarial strength for any state, used to solve behavior values for each interaction action state. Further, equilibrium action strategies for the vehicles are derived using Stackelberg game theory, yielding optimal actions considering adversarial interactions in the current traffic environment. Simulation results show that the adversarial scenarios generated by this method significantly increase incident rates by 158% to 1313% compared to natural driving scenarios, while ride comfort and driving efficiency decrease, and risk significantly increases. These findings provide critical insights for model improvement and demonstrate the proposed method’s suitability for assessing AV performance in dynamic traffic environments.
Citation: Machines
PubDate: 2024-08-06
DOI: 10.3390/machines12080538
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 539: Soft Robots: Computational Design,
Fabrication, and Position Control of a Novel 3-DOF Soft Robot
Authors: Martin Garcia, Andrea-Contreras Esquen, Mark Sabbagh, Devin Grace, Ethan Schneider, Turaj Ashuri, Razvan Cristian Voicu, Ayse Tekes, Amir Ali Amiri Moghadam
First page: 539
Abstract: This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications.
Citation: Machines
PubDate: 2024-08-07
DOI: 10.3390/machines12080539
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 540: A Survey of Augmented Reality for
Human–Robot Collaboration
Authors: Christine T. Chang, Bradley Hayes
First page: 540
Abstract: For nearly three decades, researchers have explored the use of augmented reality for facilitating collaboration between humans and robots. In this survey paper, we review the prominent, relevant literature published since 2008, the last date that a similar review article was published. We begin with a look at the various forms of the augmented reality (AR) technology itself, as utilized for human–robot collaboration (HRC). We then highlight specific application areas of AR for HRC, as well as the main technological contributions of the literature. Next, we present commonly used methods of evaluation with suggestions for implementation. We end with a look towards future research directions for this burgeoning field. This review serves as a primer and comprehensive reference for those whose work involves the combination of augmented reality with any kind of human–robot collaboration.
Citation: Machines
PubDate: 2024-08-07
DOI: 10.3390/machines12080540
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 541: Numerical Study of Solid–Gas Two-Phase
Flow and Erosion Distribution in Glass Fiber-Reinforced Polymer Ball
Valves
Authors: Qi Chen, Yazhong Xia, Jiuyang Yu, Yaonan Dai, Kang Peng, Tianyi Zhang, Bowen Liu
First page: 541
Abstract: The use of glass fiber-reinforced polymer (GFRP) composites in fluid transport systems can effectively reduce corrosion damage caused by corrosive media. However, collisions between solid particles and the surfaces of ball valve flow passages can cause erosion damage and lead to safety issues. The two-phase flow and erosion characteristics of ball valves manufactured from resin-based fiber-reinforced composite materials were studied under different openings and particle sizes using the CFD-DPM method. The results indicate that both smaller and larger relative openings are prone to erosion damage at the thin edges of the valve ball. As the relative opening increases, the average erosion amount in the flow passage first increases and then decreases. The maximum average erosion amount is 0.0051 kg/m2·s when the relative opening is Cv = 40. At Cv = 40, erosion damage in the flow channel mainly occurs at the bottom of the inlet flow channel and the valve seat position. With increasing particle size, both the average and maximum erosion amounts in the flow channel increase. Larger particle sizes in the inlet flow channel significantly raise the erosion rate nearby, while at other locations, larger particle sizes mainly increase the erosion rate in the same area. During the use of GFRP valves, it is important to avoid introducing large-sized particles into the medium. Keeping the valve’s relative opening greater than 40 and using more erosion-resistant materials for the valve seat can effectively reduce the erosion of the composite ball valve and extend its service life.
Citation: Machines
PubDate: 2024-08-07
DOI: 10.3390/machines12080541
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 542: A Study on Service-Oriented Digital Twin
Modeling Methods for Weaving Workshops
Authors: Bo Yu, Liaoliao Fang, Laibing Luo, Xudong Hu, Chunya Shen
First page: 542
Abstract: With the rapid development of intelligent manufacturing, Digital Twin technology, as an advanced tool for the intelligentization of weaving workshops, has endowed weaving services with real-time simulation and dynamic optimization capabilities while also placing higher demands on the digital capabilities of workshops. The diverse and multi-manufacturer equipment in weaving workshops exacerbates the complexity of multi-source heterogeneous data. Moreover, traditional data collection methods, which are mostly based on fixed frequencies, increase the network load during real-time high-frequency data reception, making stable, long-term operation difficult. Conversely, low-frequency collection might miss important state changes, thus affecting the quality of weaving big data. To address these issues, this paper proposes a service-oriented Digital Twin modeling method for weaving workshops. This method combines OPC Unified Architecture (OPC UA) with a state change-based data collection approach, utilizing a sliding time window (STW) to identify anomalous data and employing median interpolation to correct these anomalies. The goal is to enhance the representation capability of the Digital Twin in the weaving workshop by improving the data quality. For a specific service of predicting the warp-out time of 288 air-jet looms in a workshop, the average error of the predicted warp-out time using the dynamic data set proposed in this study was reduced from 0.85 h to 0.78 h compared to the static data set based on fixed frequency, an improvement of 8.2%, thereby validating the effectiveness of the proposed method.
Citation: Machines
PubDate: 2024-08-07
DOI: 10.3390/machines12080542
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 543: Model Predictive Control for Formation
Placement and Recovery of Traffic Cone Robots
Authors: Zhiyong Li, Siyuan Chang, Min Ye, Shengjie Jiao
First page: 543
Abstract: The challenge of effectively managing the formation and recovery of traffic cone robots (TCRs) is addressed by proposing a linear time-varying model predictive control (MPC) strategy. This problem involves coordinating multiple TCR formations within a work area to reach a target location, which is a huge challenge due to the complexity of dynamic coordination. Unlike conventional approaches, our method decomposes the formation control problem into two main components: leader TCR motion planning and follower formation tracking control. The motion planning component involves path and velocity planning to achieve leader trajectory control, which serves as a reference trajectory for the follower. The formation tracking task extends to formation control among multiple robots to achieve the traffic cone robot formation placement and recovery task. To address the TCR input limitation problem, input constraints are considered during the design process of the MPC controllers. The effectiveness and practicality of the proposed control strategy are validated through a series of numerical simulations and physical experiments with TCRs.
Citation: Machines
PubDate: 2024-08-08
DOI: 10.3390/machines12080543
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 544: Design, Analysis and Experiment of a Modular
Deployable Continuum Robot
Authors: Aihu Jia, Xinyu Liu, Yuntao Guan, Yongxi Liu, Qianze Helian, Chenshuo Liu, Zheming Zhuang, Rongjie Kang
First page: 544
Abstract: Continuum robots, possessing great flexibility, can accomplish tasks in complex work scenes, regarded as an important direction in robotics. However, the current continuum robots are not satisfying enough in terms of fabrication and maintenance, and their workspace is limited by structure and other aspects. In this paper, to address the above problems, a modular deployable robot, which adopts an origami structure instead of a flexible hinge, is proposed. A fabrication method is innovated, the Spherical Linkage Parallel Mechanism (SLPM) unit is optimized, and the installation and fabrication process of the robot is simplified through modularization. The forward kinematics and inverse kinematics of the robot and its workspace are analyzed by using the screw theory. The prototype of the robot is constructed, and its folding performance, bending performance, and motion accuracy are tested, and the error analysis and compensation optimization are carried out. After the optimization, the position error of the robot is reduced by about 65%, and the standard deviation is greatly lowered, which effectively improves the motion accuracy and stability of the robot.
Citation: Machines
PubDate: 2024-08-10
DOI: 10.3390/machines12080544
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 545: The Impact of an Electric Machine Body on EM
Wave Propagation in RTMS
Authors: Sonia Ben Brahim, Samia Dardouri, Amor Hammami, Ridha Bouallegue, Jacques David, Tan-Hoa Vuong
First page: 545
Abstract: The metallic components of an electric machine exert a notable impact on the electromagnetic transmission between an external recipient and an internal shaft sensor. The RTMS (rotor temperature monitoring system) is aimed at boosting power transfer in the machine, regardless of the considerable impact of its components. This research assesses the influence of the components of an electric machine on shaft sensor transmission and specifically addresses the packaging needs of the receiver antenna. The study includes a comparative analysis of two antennas. HFSS (high frequency structure simulator) simulation was utilized to identify the antenna with a superior propagation factor. The principal contribution of this paper is the assessment of how the electrical machine body affects the transmission and propagation of rotor sensor signals, establishing a connection between these effects and the packaging criteria for the receiver antenna. Moreover, the paper presents an antenna design that capitalizes on the electrical machine body to enhance the power transmission effectiveness. The final section of this paper encompasses the experimental results obtained from the implementation of the RN171 antenna on the electrical machine, provides valuable insights into the propagation characteristics, and contributes to a comprehensive understanding of the electromagnetic dynamics within the system.
Citation: Machines
PubDate: 2024-08-11
DOI: 10.3390/machines12080545
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 546: Personalizing Human–Robot Workplace
Parameters in Human-Centered Manufacturing
Authors: Robert Ojsteršek, Borut Buchmeister, Aljaž Javernik
First page: 546
Abstract: This study investigates the relationship between collaborative robot (CR) parameters and worker utilization and system performance in human–robot collaboration (HRC) environments. We investigated whether optimized parameters increase workplace efficiency and whether adapting these parameters to the individual worker improves workplace outcomes. Three experimental scenarios with different CR parameters were analyzed in terms of the setup time, assembly time, finished products, work in process, and worker utilization. The main results show that personalized CR parameters significantly improve efficiency and productivity. The scenario in which CR parameters were tailored to individual workers, balanced the workload, and minimized worker stress, resulting in higher productivity compared to non-people-centric settings. The study shows that personalization reduces cognitive and physical stress, promotes worker well-being, and is consistent with the principles of human-centered manufacturing. Overall, our research supports the adoption of personalized, collaborative workplace parameters, supported by the mathematical model, to optimize employee efficiency and health, contributing to human-centered and efficient HRC environments.
Citation: Machines
PubDate: 2024-08-11
DOI: 10.3390/machines12080546
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 547: PolyDexFrame: Deep Reinforcement
Learning-Based Pick-and-Place of Objects in Clutter
Authors: Muhammad Babar Imtiaz, Yuansong Qiao, Brian Lee
First page: 547
Abstract: This research study represents a polydexterous deep reinforcement learning-based pick-and-place framework for industrial clutter scenarios. In the proposed framework, the agent tends to learn the pick-and-place of regularly and irregularly shaped objects in clutter by using the sequential combination of prehensile and non-prehensile robotic manipulations involving different robotic grippers in a completely self-supervised manner. The problem was tackled as a reinforcement learning problem; after the Markov decision process (MDP) was designed, the off-policy model-free Q-learning algorithm was deployed using deep Q-networks as a Q-function approximator. Four distinct robotic manipulations, i.e., grasp from the prehensile manipulation category and inward slide, outward slide, and suction grip from the non-prehensile manipulation category were considered as actions. The Q-function comprised four fully convolutional networks (FCN) corresponding to each action based on memory-efficient DenseNet-121 variants outputting pixel-wise maps of action-values jointly trained via the pixel-wise parametrization technique. Rewards were awarded according to the status of the action performed, and backpropagation was conducted accordingly for the FCN generating the maximum Q-value. The results showed that the agent learned the sequential combination of the polydexterous prehensile and non-prehensile manipulations, where the non-prehensile manipulations increased the possibility of prehensile manipulations. We achieved promising results in comparison to the baselines, differently designed variants, and density-based testing clutter.
Citation: Machines
PubDate: 2024-08-11
DOI: 10.3390/machines12080547
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 548: Research on Pressure Characteristics of
Two-Speed Buffer Valve Pushing System
Authors: Ziming Kou, Lin Zhang, Buwen Zhang
First page: 548
Abstract: The positioning control of the hydraulic support pushing system in the fully mechanized mining face is the key technical support to realize intelligent mining. The opening and closing of the existing support switch reversing valve will cause a sudden change in the system pressure and flow under the conditions of high pressure and large flow, which will affect the life of the components, the precision, and stability of the actuator movement. To solve the problem, the structure of a two-speed buffer valve for the hydraulic support pushing circuit is designed. Firstly, the pushing system is analyzed theoretically, and the characteristics of the flow field in the valve and the applicable working conditions are simulated. Then, an experimental platform was built to test the improvement effect of the two-speed buffer valve on the characteristics of the pushing system. Finally, the pressure, flow, and positioning characteristics of the two-valve series pushing system under different flow rates are studied by the test results. The research results show that when the two-speed buffer valve is used, the pressure and velocity change thresholds of the system are reduced, which reduces the pressure fluctuation in front of the valve and its effect on the system pressure. At the same time, under a different system flow, the downstream pressure characteristics of the valve are improved, and the steady-state pressure anti-interference is enhanced. The positioning error of the system is reduced under different flow rates. The effectiveness of the scheme is verified by the test, which provides a basis for the optimization of the downhole valve control cylinder scheme and the subsequent valve.
Citation: Machines
PubDate: 2024-08-12
DOI: 10.3390/machines12080548
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 549: Pad Alignment Methods and Their Impact on
Large Hydrostatic Bearing Precision
Authors: Jan Foltýn, Jakub Hurník, Michal Michalec, Petr Svoboda, Ivan Křupka, Martin Hartl
First page: 549
Abstract: Hydrostatic bearings are frequently used for moving large structures smoothly and precisely. In such applications, difficulties with manufacturing, transportation, and assembly arise. The safety and precision of the entire supported structure depend on the accuracy of the hydrostatic bearing alignment. There are several suitable methods for its alignment, yet it is not clear which method can achieve the highest bearing precision. This study provides a comparative experimental assessment of the three methods. The measurements were performed on a hydrostatic bearing test rig with independent positioning of the pads. Conventional measuring devices, a pressure distribution alignment method, and an optical method, OCMM (optical coordinate measuring machine), were compared. The accuracy of the selected methods and the difficulty of the aligning process were included in the comparison. The OCMM method was able to achieve an accuracy 1.6 times higher relative to the pressure method and 6 times higher compared to conventional measuring devices. This method is versatile and can be applied for a wide range of bearing sizes.
Citation: Machines
PubDate: 2024-08-13
DOI: 10.3390/machines12080549
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 550: Network-Centric Formation Control and Ad Hoc
Communication with Localisation Analysis in Multi-UAV Systems
Authors: Jack Devey, Palvir Singh Gill, George Allen, Essa Shahra, Moad Idrissi
First page: 550
Abstract: In recent years, the cost-effectiveness and versatility of Unmanned Aerial Vehicles (UAVs) have led to their widespread adoption in both military and civilian applications, particularly for operations in remote or hazardous environments where human intervention is impractical. The use of multi-agent UAV systems has notably increased for complex tasks such as surveying and monitoring, driving extensive research and development in control, communication, and coordination technologies. Evaluating and analysing these systems under dynamic flight conditions present significant challenges. This paper introduces a mathematical model for leader–follower structured Quadrotor UAVs that encapsulates their dynamic behaviour, incorporating a novel multi-agent ad hoc coordination network simulated via COOJA. Simulation results with a pipeline surveillance case study demonstrate the efficacy of the coordination network and show that the system offers various improvements over contemporary pipeline surveillance approaches.
Citation: Machines
PubDate: 2024-08-13
DOI: 10.3390/machines12080550
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 551: Artificial Intelligence Enabling Denoising
in Passive Electronic Filtering Circuits for Industry 5.0 Machines
Authors: Alessandro Massaro
First page: 551
Abstract: The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes.
Citation: Machines
PubDate: 2024-08-13
DOI: 10.3390/machines12080551
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 552: Time-Delay Estimation Improves Active
Disturbance Rejection Control for Time-Delay Nonlinear Systems
Authors: Syeda Nadiah Fatima Nahri, Shengzhi Du, Barend J. van Wyk, Tawanda Denzel Nyasulu
First page: 552
Abstract: Lately, active disturbance rejection control (ADRC), a model-independent controller, has become popular for combating various forms of uncertain disturbances incurred in industrial applications. ADRC was validated for external disturbances, internal disturbances, and nonlinearities incurred under realistic scenarios. Time delay challenges all controllers, especially when it coexists with nonlinearities. This paper investigates the impacts of time delay and backlash-like hysteresis nonlinearity in ADRC-controlled systems. These impacts are analyzed, as in the case study, in two ADRC-based methods, namely the ADRC with delayed input method and the predictive extended state observer (PESO)-based ADRC (PESO-ADRC) method. To improve the system response and to attain a decent attenuation of uncertainties, the time-delay estimation (TDE) mechanism is introduced to the concerned ADRC-based methods. Experimental studies are conducted to verify the effectiveness and stability of the proposed TDE-ADRC methods. The results demonstrate the robustness and decent recovery of the transient response after the adverse impact of the backlash-like hysteresis on both concerned ADRC-controlled systems.
Citation: Machines
PubDate: 2024-08-13
DOI: 10.3390/machines12080552
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 553: Analysis of the Influence of Nozzle
Structure of Dry Powder Fire Extinguishing System on Supersonic Jet
Characteristics
Authors: Hongen Ge, Peng Zhao, Cong Zhu, Xin Zhang, Yuqi Liu
First page: 553
Abstract: The nozzle, as a critical jet component in dry powder fire extinguishing systems, significantly affects jet characteristics through its geometric configuration. To explore the influence of structural parameters on ultrafine dry powder gas-solid two-phase jet characteristics, a bidirectional coupled numerical model based on the SST k-ω turbulence model and the Discrete Phase Model is employed. This study examines how variations in the semi-expansion angle (α) and semi-contraction angle (β) of the nozzle affect compressible gas flow behavior and particle distribution trajectories through a combination of simulations and experiments. The results indicate that when α = 2°, the gas jet is in an under-expanded state, leading to increased particle dispersion due to the stripping effect of the surrounding high-speed airflow. Within the range of x = 0–180 mm, the dry powder exhibits a diffusion trend. When α = 4.5°, the gas jet core region is the longest, providing optimal particle acceleration. Under constant inlet pressure, reducing α enhances particle collimation. The reduction of α alters the gas jet state, with α = 2° showing better powder diffusion compared to α = 6°. However, an excessively small α is detrimental to increasing the range of dry powder. With consistent structural parameters, the diffusion and range of dry powder remain the same across different β values, and variations in β have a relatively minor impact on supersonic jet characteristics. These findings offer theoretical guidance for optimizing and improving nozzles in ultrafine dry powder fire extinguishing systems.
Citation: Machines
PubDate: 2024-08-13
DOI: 10.3390/machines12080553
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 554: Output Feedback-Based Neural Network Sliding
Mode Control for Electro-Hydrostatic Systems with Unknown Uncertainties
Authors: Tri Dung Dang, Tri Cuong Do, Hoai Vu Anh Truong
First page: 554
Abstract: This paper proposes an output feedback-based control for uncertain electro-hydrostatic systems (EHSs) to satisfy high output tracking precision under the influences of unknown mismatched and matched uncertainties and unstructured dynamical behavior. In this configuration, an extended state observer (ESO) is first employed to obtain unmeasured states and suppress the adverse effect of matched uncertainty. Meanwhile, the influence of unstructured dynamical behavior is approximated by employing a radial basis function neural network (RBFNN)-based technique. With the unmeasured states observed, matched uncertainty, and system dynamics compensated, the robust backstepping sliding mode control is accordingly established and the lumped mismatched uncertainty is then suppressed through disturbance observer-based adaptive law. Interestingly, the proposed control methodology requires only output feedback but can address the whole system dynamics. The stability of the closed-loop system is theoretically proven through a Lyapunov theorem and the effectiveness of the proposed methodology is demonstrated through comparative simulations.
Citation: Machines
PubDate: 2024-08-13
DOI: 10.3390/machines12080554
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 555: Digital Performance Management: An
Evaluation of Manufacturing Performance Management and Measurement
Strategies in an Industry 4.0 Context
Authors: Nathaniel David Smith, Yuri Hovanski, Joe Tenny, Sebastian Bergner
First page: 555
Abstract: Manufacturing management and operations place heavy emphasis on monitoring and improving production performance. This supervision is accomplished through strategies of manufacturing performance management, a set of measurements and methods used to monitor production conditions. Over the last 30 years, the most prevalent measurement of traditional performance management has been overall equipment effectiveness, a percentile summary metric of a machine’s utilization. The technologies encapsulated by Industry 4.0 have expanded the ability to gather, process, and store vast quantities of data, creating the opportunity to innovate on how performance is measured. A new method of managing manufacturing performance utilizing Industry 4.0 technologies has been proposed by McKinsey & Company (New York City, NY, USA), and software tools have been developed by PTC Inc. (Boston, MA, USA) to aid in performing what they both call digital performance management. To evaluate this new approach, the digital performance management tool was deployed on a Festo (Esslingen, Germany) Cyber-Physical Lab (FCPL), an educational mock production environment, and compared to a digitally enabled traditional performance management solution. Results from a multi-day production period displayed an increased level of detail in both the data presented to the user and the insights gained from the digital performance management solution as compared to the traditional approach. The time unit measurements presented by digital performance management paint a clear picture of what and where losses are occurring during production and the impact of those losses. This is contrasted by the single summary metric of a traditional performance management approach, which easily obfuscates the constituent data and requires further investigation to determine what and where production losses are occurring.
Citation: Machines
PubDate: 2024-08-14
DOI: 10.3390/machines12080555
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 556: Novel Workstation Module and Method for
Automatic Blanking of Surgical Forceps
Authors: Xianzhen Du, Jiapeng Li, Haochen Wang, Zhenyu Li, Yusheng Li, Zhiyuan Li
First page: 556
Abstract: During the manufacturing of surgical forceps, the flashes of the blanks need to be removed. Manual production has problems such as high labor intensity, low efficiency, and high-risk factors. To solve this problem and realize fully automatic resection, a novel modular workstation was designed and a corresponding process method was proposed. The workstation adopts robots, non-standard automation equipment, and image recognition technology instead of manual loading and blanking, but the blank storage still needs to be performed manually. The critical components were selected according to the workstation design scheme and process method, and the control system design was completed. The reliability of the separation unit was studied through a test platform, and the failure problem caused by uneven force was solved using a blank locking device, which showed that the separation success rate was stabilized at 100%. The detection speed of the image recognition system can reach 100 ms/piece, and the product qualification rate can reach 95.7%. The advantages of the workstation in terms of output and productivity were further analyzed by comparing it to manual production, where the average daily output increased by 12.5% (4500 pieces). In addition, the results of long-term test experiments and power consumption comparison tests showed that the workstations are highly stable and consume little additional power.
Citation: Machines
PubDate: 2024-08-14
DOI: 10.3390/machines12080556
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 557: KCS-YOLO: An Improved Algorithm for Traffic
Light Detection under Low Visibility Conditions
Authors: Qinghui Zhou, Diyi Zhang, Haoshi Liu, Yuping He
First page: 557
Abstract: Autonomous vehicles face challenges in small-target detection and, in particular, in accurately identifying traffic lights under low visibility conditions, e.g., fog, rain, and blurred night-time lighting. To address these issues, this paper proposes an improved algorithm, namely KCS-YOLO (you only look once), to increase the accuracy of detecting and recognizing traffic lights under low visibility conditions. First, a comparison was made to assess different YOLO algorithms. The benchmark indicates that the YOLOv5n algorithm achieves the highest mean average precision (mAP) with fewer parameters. To enhance the capability for detecting small targets, the algorithm built upon YOLOv5n, namely KCS-YOLO, was developed using the K-means++ algorithm for clustering marked multi-dimensional target frames, embedding the convolutional block attention module (CBAM) attention mechanism, and constructing a small-target detection layer. Second, an image dataset of traffic lights was generated, which was preprocessed using the dark channel prior dehazing algorithm to enhance the proposed algorithm’s recognition capability and robustness. Finally, KCS-YOLO was evaluated through comparison and ablation experiments. The experimental results showed that the mAP of KCS-YOLO reaches 98.87%, an increase of 5.03% over its counterpart of YOLOv5n. This indicates that KCS-YOLO features high accuracy in object detection and recognition, thereby enhancing the capability of traffic light detection and recognition for autonomous vehicles in low visibility conditions.
Citation: Machines
PubDate: 2024-08-15
DOI: 10.3390/machines12080557
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 558: A Velocity-Adaptive MPC-Based Path Tracking
Method for Heavy-Duty Forklift AGVs
Authors: Yajun Wang, Kezheng Sun, Wei Zhang, Xiaojun Jin
First page: 558
Abstract: In warehouses with vast quantities of heavy goods, heavy-duty forklift Automated Guided Vehicles (AGVs) play a key role in facilitating efficient warehouse automation. Due to their large load capacity and high inertia, heavy-duty forklift AGVs struggle to automatically navigate optimized routes. Additionally, rapid acceleration and deceleration can pose safety hazards. This paper proposes a velocity-adaptive model predictive control (MPC)-based path tracking method for heavy-duty forklift AGVs. The movement of heavy-duty forklift-type AGVs is categorized into straight-line and curve-turning motions, corresponding to the straight and curved sections of the reference path, respectively. These sections are segmented based on their curvature. The best driving speeds for straight and curved sections were 1.5 m/s and 0.3 m/s, respectively, while the optimal acceleration rates were 0.2 m/s2 for acceleration and −0.2 m/s2 for deceleration in straight paths and 0.3 m/s2 for acceleration with −0.15 m/s2 for deceleration in curves. Moreover, preferred sampling times, prediction domain, and control domain were determined through simulations at various speeds. Four path tracking methods, including pure tracking, Linear Quadratic Regulator (LQR), MPC, and the velocity-adaptive MPC, were simulated and evaluated under straight-line, turning, and complex double lane change conditions. Field experiments conducted in a warehouse environment demonstrated the effectiveness of the proposed path tracking method. Findings have implications for advancing path tracking control in narrow aisles.
Citation: Machines
PubDate: 2024-08-15
DOI: 10.3390/machines12080558
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 559: Chatter Detection in Thin-Wall Milling Based
on Multi-Sensor Fusion and Dual-Stream Residual Attention CNN
Authors: Danian Zhan, Dawei Lu, Wenxiang Gao, Haojie Wei, Yuwen Sun
First page: 559
Abstract: Thin-walled parts exhibit high flexibility, rendering them susceptible to chatter during milling, which can significantly impact machining accuracy, surface quality, and productivity. Therefore, chatter detection plays a crucial role in thin-wall milling. In this study, a chatter detection method based on multi-sensor fusion and a dual-stream convolutional neural network (CNN) is proposed, which can effectively identify the machining status in thin-wall milling. Specifically, the acceleration signals and cutting force signals are first collected during the milling process and transformed into the frequency domain using fast Fourier transform (FFT). Secondly, a dual-stream CNN is designed to extract the hidden features from the spectrum of multi-sensor signals, thereby avoiding confusion when learning the features of each sensor signal. Then, considering that the characteristics of each sensor are of different importance for chatter detection, a joint attention mechanism based on residual connection is designed, and the feature weight coefficients are adaptively assigned to obtain the joint features. Finally, the joint features feed into a machining status classifier to identify chatter occurrences. To validate the feasibility and effectiveness of the proposed method, a series of milling tests are conducted. The results demonstrate that the proposed method can accurately distinguish between stable and chatter under various milling scenarios, achieving a detection accuracy of up to 98.68%.
Citation: Machines
PubDate: 2024-08-15
DOI: 10.3390/machines12080559
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 560: Design, Analysis and Application of Control
Techniques for Driving a Permanent Magnet Synchronous Motor in an Elevator
System
Authors: Vasileios I. Vlachou, Dimitrios E. Efstathiou, Theoklitos S. Karakatsanis
First page: 560
Abstract: An electrical motors, together with its appropriate drive system, is one of the most important elements of electromobility. In recent years, there has been a particular interest by academic researchers and engineers in permanent-magnet motors (PMSMs) in various applications, such as electric vehicles, Unmanned Aerial Vehicles (UAVs), elevator systems, etc., as the main source of drive transmission. Nowadays, the elevator industry, with the evolution of magnetic materials, has turned to gearless PMSMs over geared induction motors (IMs). One of the most important elements that is given special emphasis in these applications is proper motor design in consideration of the weight and speed of the chamber to be served during operation. This paper presents a design of a high-efficiency PMSM, in which finite elements analysis (FEA) and the study of the lift operating cycle provided useful conclusions on the magnetic field of the machine in different operating states. In addition, a simulated model was compared with experimental results of test operations. Furthermore, the drive system also required the use of appropriate electrical power and controls to drive the PMSM. Especially in elevator applications, the control of the motor speed by the variable voltage variable frequency technique (VVVF) is the most common technology used to avoid endangering the safety of the passengers. Thus, suitable speed and current controllers were used for this purpose. In our research, we focused on studying different control techniques using a suitable inverter to compare the system operation in each case studied.
Citation: Machines
PubDate: 2024-08-15
DOI: 10.3390/machines12080560
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 561: In-Depth Analysis of the Processing of Nomex
Honeycomb Composites: Problems, Techniques and Perspectives
Authors: Tarik Zarrouk, Mohammed Nouari, Jamal-Eddine Salhi, Hilal Essaouini, Mohammed Abbadi, Ahmed Abbadi, Mohammed Lhassane Lahlaouti
First page: 561
Abstract: Nomex honeycomb composites are widely recognized for their advanced structural applications in the aerospace, automotive and defense industries. These materials are distinguished by exceptional characteristics such as thin cell walls and a hexagonal structure, as well as layers made of phenolic resins and aramid fibers. However, complex machining and the maintenance of high quality at a large scale presents considerable challenges. This study provides a comprehensive review of the literature on the processing of Nomex composites, highlighting the design challenges related to processing technologies, the impact of conventional and ultrasonic processing methods, and the associated mechanical properties and microstructural topographies. Moreover, it reviews research advances in machining techniques, current challenges, and future perspectives, thereby providing valuable guidance to ensure the optimal cutting of Nomex honeycomb composite structures (NHCs).
Citation: Machines
PubDate: 2024-08-15
DOI: 10.3390/machines12080561
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 562: Double-Sided Surface Structures with
Undercuts on Cold-Rolled Steel Sheets for Interlocking in Hybrid
Components
Authors: Aron Ringel, Sindokht Shayan, David Bailly
First page: 562
Abstract: Weight reduction strategies are essential for the transportation sector to reduce greenhouse gas emissions or extend the range of electric vehicles. In the field of lightweight assembly strategies, multi-material design offers great potential. Joining materials typically used in the automotive sector, such as aluminum and steel, brings challenges as conventional processes such as fusion welding are unsuitable. Therefore, new technologies can extend the design options. In previous studies, a mechanical interlocking between cold-rolled surface structures with undercuts on a steel sheet and die-cast aluminum was presented. This method has now been extended to double-sided structures for more complex applications with a joint on both sheet surfaces. Numerical simulations and validation experiments were performed to investigate the manufacturing of the double-sided structures. Furthermore, the influence of the alignment of the upper and lower structures in relation to each other on the resulting structural geometry and the rolling forces were analyzed. More advantageous geometric parameters, e.g., 24% larger undercuts, and approx. 24.1% lower forming forces at 20% height reduction were observed for a shifted alignment. However, significantly higher wear of the structured rollers occurred in the corresponding experiments.
Citation: Machines
PubDate: 2024-08-16
DOI: 10.3390/machines12080562
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 563: Pipeline Landmark Classification of
Miniature Pipeline Robot π-II Based on Residual Network ResNet18
Authors: Jian Wang, Chuangeng Chen, Bingsheng Liu, Juezhe Wang, Songtao Wang
First page: 563
Abstract: A pipeline robot suitable for miniature pipeline detection, namely π-II, was proposed in this paper. It features six wheel-leg mobile mechanisms arranged in a staggered manner, with a monocular fisheye camera located at the center of the front end. The proposed robot can be used to capture images during detection in miniature pipes with an inner diameter of 120 mm. To efficiently identify the robot’s status within the pipeline, such as navigating in straight pipes, curved pipes, or T-shaped pipes, it is necessary to recognize and classify these specific pipeline landmarks accurately. For this purpose, the residual network model ResNet18 was employed to learn from the images of various pipeline landmarks captured by the fisheye camera. A detailed analysis of image characteristics of some common pipeline landmarks was provided, and a dataset of approximately 908 images was created in this paper. After modifying the outputs of the network model, the ResNet18 was trained according to the proposed datasets, and the final test results indicate that this modified network has a high accuracy rate in classifying various pipeline landmarks, demonstrating a promising application prospect of image detection technology based on deep learning in miniature pipelines.
Citation: Machines
PubDate: 2024-08-16
DOI: 10.3390/machines12080563
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 564: Abnormal Driving Area Detection Using
Multiple Vehicle Dynamic Model-Based Filter: Design and Experimental
Validation
Authors: Changmook Kang, Taehyung Lee, Jongho Shin
First page: 564
Abstract: The main concern of remote control systems for autonomous ground vehicles (AGVs) is to perform the given mission according to the purpose of the operator. Although most remote systems are composed of a screen-based architecture, they are insufficient to transfer sufficient information to the remote operator. Therefore, in this paper, we present and experimentally validate an abnormal driving area detection system using an interacting multiple model (IMM) filter for the remote control system. In the proposed IMM filter, the unknown dynamic behavior of the vehicle, which changes according to changes in the driving environment, was lumped into a parameter change of the system model. As a result, we can obtain the probability of each model representing the reliability of each model, but an index can be used to infer the current status of the AGV and the driving environment. The index can help us detect both unusual behavior of the AGV such as skidding or sliding, and areas with low-friction road conditions that are not confirmed by images from the camera sensor. Thus, the remote operator can directly decide whether to continue operating or not. The proposed method is simple but useful and meaningful for the remote operator compared to the image-only method. The overall procedure of the proposed method was experimentally validated via a multi-purpose AGV on rough unpaved proving ground. Nine abnormal driving areas were discovered on the ground. In five of these areas, vehicles consistently exhibited abnormal driving behavior. The remaining four areas were confirmed to be affected by variables such as weather conditions and vehicle tire wear.
Citation: Machines
PubDate: 2024-08-17
DOI: 10.3390/machines12080564
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 565: Advanced State Estimation for
Multi-Articulated Virtual Track Trains: A Fusion Approach
Authors: Zhenggang Lu, Zehan Wang, Xianguang Luo
First page: 565
Abstract: The Virtual Track Train (VTT) represents an innovative urban public transportation system that combines tire-based running gears with rail transit management. Effective control of such a system necessitates precise state estimation, a task rendered complex by the multi-articulated nature of the vehicles. This study addresses the challenge by focusing on state estimation for the first unit under significant interference, introducing a fusion state estimation strategy utilizing Gaussian Process Regression (GPR) and Interacting Multiple Model (IMM) techniques. First, a joint model for the first unit is established, comprising the dynamics model as the main model and a residual model constructed based on GPR to accommodate the main model’s error. The proposed fusion strategy comprises two components: a kinematic model-based method for handling transient and high-acceleration phases, and a joint-model-based method suitable for near-steady-state and low-acceleration conditions. The IMM method is employed to integrate these two approaches. Subsequent units’ states are computed from the first unit’s state, articulation angles, and yaw rates’ filtered data. Validation through hardware-in-the-loop (HIL) simulation demonstrates the strategy’s efficacy, achieving high accuracy with an average lateral speed estimation error below 0.02 m/s and a maximum error not exceeding 0.22 m/s. Additionally, the impact on VTT control performance after incorporating state estimation is minimal, with a reduction of only 3–6%.
Citation: Machines
PubDate: 2024-08-17
DOI: 10.3390/machines12080565
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 566: Uncertainty Optimization of Vibration
Characteristics of Automotive Micro-Motors Based on Pareto Elliptic
Algorithm
Authors: Hao Hu, Deping Wang, Yudong Wu, Jianjiao Deng, Xi Chen, Weiping Ding
First page: 566
Abstract: The NVH (Noise, Vibration, and Harshness) characteristics of micro-motors used in vehicles directly affect the comfort of drivers and passengers. However, various factors influence the motor’s structural parameters, leading to uncertainties in its NVH performance. To improve the motor’s NVH characteristics, we propose a method for optimizing the structural parameters of automotive micro-motors under uncertain conditions. This method uses the motor’s maximum magnetic flux as a constraint and aims to reduce vibration at the commutation frequency. Firstly, we introduce the Pareto ellipsoid parameter method, which converts the uncertainty problem into a deterministic one, enabling the use of traditional optimization methods. To increase efficiency and reduce computational cost, we employed a data-driven method that uses the one-dimensional Inception module as the foundational model, replacing both numerical models and physical experiments. Simultaneously, the module’s underlying architecture was improved, increasing the surrogate model’s accuracy. Additionally, we propose an improved NSGA-III (Non-dominated Sorting Genetic Algorithm III) method that utilizes adaptive reference point updating, dividing the optimization process into exploration and refinement phases based on population matching error. Comparative experiments with traditional models demonstrate that this method enhances the overall quality of the solution set, effectively addresses parameter uncertainties in practical engineering scenarios, and significantly improves the vibration characteristics of the motor.
Citation: Machines
PubDate: 2024-08-18
DOI: 10.3390/machines12080566
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 567: Theoretical and Experimental Investigation
of a Novel Wedge-Loading Planetary Traction Drive
Authors: Yujiang Jiang, Guangjian Wang
First page: 567
Abstract: The development of high-speed motors has stimulated the demand for high-speed reducers. In response to the lack of research on high-speed reducers and the challenge of developing high-speed transmission systems, this study proposes a novel wedge-loading planetary traction drive (WPTD). First, a more accurate theoretical analysis model is established by considering the combined effects of elastic deformation, loading state, and a elastohydrodynamic lubrication (EHL) traction mechanism. Second, the mixed thermal EHL model is introduced into the performance analysis of traction drive for the first time. The fitting formulas for predicting traction contact behavior are derived, and a performance analysis method for all line-contact traction drives is presented. Third, the loading performance, transmission characteristics, and the influence of different parameters on the transmission characteristics of WPTD are analyzed. Finally, the theoretical model is validated by prototype performance tests. The results show that the loading mechanism demonstrates a good self-adaptive loading effect, and WPTD achieves a peak efficiency of 96%. Additionally, WPTD delivers superior efficiency and vibration and noise performance because of its smooth power-transfer characteristics, thereby providing a possible solution for high-speed and low-vibration transmissions.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080567
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 568: A Study on the Running of a Joystick-Type
Six-Wheeled Electric Wheelchair When Curb Climbing
Authors: Tetsuaki Kawata, Fumihisa Sato, Shiori Tsuji, Toya Suzuki, Takato Suzuki, Takuto Kokuryu
First page: 568
Abstract: In Japan, the number of power wheelchair users is increasing as the country becomes an aging society. This trend is expected to continue in the future. Electric wheelchairs currently on the market include (1) bar-handle-type power wheelchairs for older users and (2) joystick-type power wheelchairs that change direction by operating a joystick. When such electric wheelchairs are used outdoors, the problem is curb-climbing at the boundary between the roadway and the sidewalk. It would be difficult for a wheelchair with a small front wheel diameter of 200 mm to overcome a curb height of 50 mm. Therefore, users are forced to take a detour or drive on the street to avoid the curb step. One of the most effective ways to solve this problem is to increase the wheel diameter. However, larger wheels make it more difficult for users to get in and out of the wheelchair. In addition, there are problems such as an increased footprint when turning, which makes the wheelchairs difficult to use on narrow streets. In this paper, using a joystick-type six-wheel electric wheelchair as an example, we examined the mechanism by which an electric wheelchair can overcome curb climbing and consider improvements to the chassis with a method that does not rely on increasing the wheel diameter. As a result, it became possible to overcome a curb of 96 mm in height with a front-wheel diameter of 200 mm.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080568
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 569: Optimization Techniques in the Localization
Problem: A Survey on Recent Advances
Authors: Massimo Stefanoni, Peter Sarcevic, József Sárosi, Akos Odry
First page: 569
Abstract: Optimization is a mathematical discipline or tool suitable for minimizing or maximizing a function. It has been largely used in every scientific field to solve problems where it is necessary to find a local or global optimum. In the engineering field of localization, optimization has been adopted too, and in the literature, there are several proposals and applications that have been presented. In the first part of this article, the optimization problem is presented by considering the subject from a purely theoretical point of view and both single objective (SO) optimization and multi-objective (MO) optimization problems are defined. Additionally, it is reported how local and global optimization problems can be tackled differently, and the main characteristics of the related algorithms are outlined. In the second part of the article, extensive research about local and global localization algorithms is reported and some optimization methods for local and global optimum algorithms, such as the Gauss–Newton method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and so on, are presented; for each of them, the main concept on which the algorithm is based, the mathematical model, and an example of the application proposed in the literature for localization purposes are reported. Among all investigated methods, the metaheuristic algorithms, which do not exploit gradient information, are the most suitable to solve localization problems due to their flexibility and capability in solving non-convex and non-linear optimization functions.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080569
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 570: Assessment of the Uniform Wear Bending
Strength of Large Modulus Rack and Pinion Pair: Theoretical vs.
Experimental Results
Authors: Zongxing Gong, Baojia Chen, Xuan Cheng
First page: 570
Abstract: Due to long-term operation under low-speed and heavy-load conditions, large module gears and racks are inevitably subject to tooth surface wear. To investigate the changes in gear tooth bending strength, the Three Gorges ship lift was taken as the research object and a simulation test bench was established. An analytical method, a finite element method, and an experimental method were utilized to analyze the bending stress of gears under normal and various uniform wear conditions. The obtained results revealed that with the increase in wear degree and load, the bending stress of single-tooth meshing was significantly higher than that of double-tooth meshing, and the single-tooth meshing time also increased, which indicates that gear wear accelerated the process of performance degradation. Furthermore, the relative errors obtained by the three calculation methods were all at a low level. This investigation aims to provide a solid theoretical and experimental basis for the dynamic analysis of large module gear and rack transmission.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080570
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 571: A High-Speed Train Axle Box Bearing Fault
Diagnosis Method Based on Dimension Reduction Fusion and the Optimal
Bandpass Filtering Demodulation Spectrum of Multi-Dimensional Signals
Authors: Zhongyao Wang, Zejun Zheng, Dongli Song, Xiao Xu
First page: 571
Abstract: The operating state of axle box bearings is crucial to the safety of high-speed trains, and the vibration acceleration signal is a commonly used bearing-health-state monitoring signal. In order to extract hidden characteristic frequency information from the vibration acceleration signal of axle box bearings for fault diagnosis, a method for extracting the fault characteristic frequency based on principal component analysis (PCA) fusion and the optimal bandpass filtered denoising signal analytic energy operator (AEO) demodulation spectrum is proposed in this paper. PCA is used to measure the dimension reduction and fusion of three-direction vibration acceleration, reducing the interference of irrelevant noise components. A new type of multi-channel bandpass filter bank is constructed to obtain filtering signals in different frequency intervals. A new, improved average kurtosis index is used to select the optimal filtering signals for different channel filters in a bandpass filter bank. A dimensionless characteristic index characteristic frequency energy concentration coefficient (CFECC) is proposed for the first time to describe the energy prominence ability of characteristic frequency in the spectrum and can be used to determine the bearing fault type. The effectiveness and applicability of the proposed method are verified using the simulation signals and experimental signals of four fault bearing test cases. The results demonstrate the effectiveness of the proposed method for fault diagnosis and its advantages over other methods.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080571
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 572: Design and Prototyping of a Collaborative
Station for Machine Parts Assembly
Authors: Federico Emiliani, Albin Bajrami, Daniele Costa, Giacomo Palmieri, Daniele Polucci, Chiara Leoni, Massimo Callegari
First page: 572
Abstract: Collaboration between humans and machines is the core of the Industry 5.0 paradigm, and collaborative robotics is one the most impactful enabling technologies for small and medium Enterprises (SMEs). In fact, small batch production and high levels of product customization make parts assembly one of the most challenging operations to be automated, and it often still depends on the versatility of human labor. Collaborative robots, for their part, can be easily integrated in this productive paradigm, as they have been specifically developed for coexistence with human beings. This work investigates the performance of collaborative robots in machine parts assembly. Design and research activities were carried out as a case study of industrial relevance at the i-Labs industry laboratory, a pole of innovation that is briefly introduced at the beginning of the paper. A fully-functional prototype of the cobotized station was realized at the end of the project, and several experimental tests were performed to validate the robustness of the assembly process as well as the collaborative nature of the application.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080572
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 573: Fault Detection of Rotating Machines Using
poly-Coherent Composite Spectrum of Measured Vibration Responses with
Machine Learning
Authors: Khalid Almutairi, Jyoti K. Sinha, Haobin Wen
First page: 573
Abstract: This study presents an efficient vibration-based fault detection method for rotating machines utilising the poly-coherent composite spectrum (pCCS) and machine learning techniques. pCCS combines vibration measurements from multiple bearing locations into a single spectrum, retaining amplitude and phase information while reducing background noise. The use of pCCS significantly reduces the number of extracted parameters in the frequency domain compared to using individual spectra at each measurement location. This reduction in parameters is crucial, especially for large industrial rotating machines, as processing and analysing extensive datasets demand significant computational resources, increasing the time and cost of fault detection. An artificial neural network (ANN)-based machine learning model is then employed for fault detection using these reduced extracted parameters. The methodology is developed and validated on an experimental rotating machine at three different speeds: below the first critical speed, between the first and second critical speeds, and above the second critical speed. This range of speeds represents the diverse dynamic conditions commonly encountered in industrial settings. This study examines both healthy machine conditions and various simulated fault conditions, including misalignment, rotor-to-stator rub, shaft cracks, and bearing faults. By combining the pCCS technique with machine learning, this study enhances the reliability, efficiency, and practical applicability of fault detection in rotating machines under varying dynamic conditions and different machine conditions.
Citation: Machines
PubDate: 2024-08-19
DOI: 10.3390/machines12080573
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 574: Novel Directions for Neuromorphic Machine
Intelligence Guided by Functional Connectivity: A Review
Authors: Mindula Illeperuma, Rafael Pina, Varuna De Silva, Xiaolan Liu
First page: 574
Abstract: As we move into the next stages of the technological revolution, artificial intelligence (AI) that is explainable and sustainable is becoming a key goal for researchers across multiple domains. Leveraging the concept of functional connectivity (FC) in the human brain, this paper provides novel research directions for neuromorphic machine intelligence (NMI) systems that are energy-efficient and human-compatible. This review serves as an accessible review for multidisciplinary researchers introducing a range of concepts inspired by neuroscience and analogous machine learning research. These include possibilities to facilitate network integration and segregation in artificial architectures, a novel learning representation framework inspired by two FC networks utilised in human learning, and we explore the functional connectivity underlying task prioritisation in humans and propose a framework for neuromorphic machines to improve their task-prioritisation and decision-making capabilities. Finally, we provide directions for key application domains such as autonomous driverless vehicles, swarm intelligence, and human augmentation, to name a few. Guided by how regional brain networks interact to facilitate cognition and behaviour such as the ones discussed in this review, we move toward a blueprint for creating NMI that mirrors these processes.
Citation: Machines
PubDate: 2024-08-20
DOI: 10.3390/machines12080574
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 575: Performance Analysis of New One-Piece Iron
Roughneck and Its Spinning Mechanism
Authors: Yongbai Sha, Donghe Han, Donghu Chen, Congzhi Liu
First page: 575
Abstract: The iron roughneck is an automated piece of equipment utilized for the connection and removal of drilling tools. This paper presents the design of an integrated iron roughneck, providing a detailed introduction to its clamp body structure, along with an analysis of its structural characteristics and performance requirements. The study delves into the integration mode and working characteristics of the clamping mechanism and spin buckle mechanism for the integrated upper clamp body structure of the iron roughneck. Additionally, this paper conducts an in-depth theoretical study on the spin buckle mechanism. Firstly, it analyzes the actual working condition of the spin buckle roller from two perspectives, namely contact theory and rolling friction theory, determining the structural form of the spin buckle roller. Secondly, it investigates the relative displacement between the spin buckle mechanism and the drilling tool, proposing a design method for the floating device mounted on the spin buckle roller and establishing the kinematic equation of the spin buckle roller under the influence of the floating device. Furthermore, the kinematic equations of the spin buckle roller under the influence of the floating device are established. Finally, a dynamics simulation experiment is performed to simulate the working process of the spin buckle mechanism under actual working conditions, analyzing the dynamics and kinematics of the spin buckle mechanism and obtaining the relevant parameter curves of the spin buckle mechanism and drilling tools. Through data comparison and analysis, the correctness of the theoretical analysis results and the rationality of the performance and structure of the spin buckle mechanism are verified.
Citation: Machines
PubDate: 2024-08-21
DOI: 10.3390/machines12080575
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 576: Analyzing Higher-Order Curvature of Four-Bar
Linkages with Derivatives of Screws
Authors: Liheng Wu, Jianguo Cai, Jian S. Dai
First page: 576
Abstract: Curvature theory, a fundamental subject in kinematics, is typically addressed through the concepts of instantaneous invariants and canonical coordinates, which are pivotal for the generation of mechanical paths. This paper delves into this subject with a higher-order analysis of screws, employing both canonical and natural coordinates. Through this exploration, a new Euler–Savary equation is derived, one that does not rely on canonical coordinates. Additionally, the paper provides a comprehensive classification of the degenerate conditions of the cubic of stationary curves of four-bar linkages at rotational positions. A thorough examination of four-bar linkages in translational positions—the couple links translate instantaneously—is also presented, with analyses extending up to the sixth order. The findings reveal that the Burmester’s points at translational positions can be extended to Burmester’s points with excess one, provided that all pivot points are symmetrically distributed about the pole norm with the two cranks in corotating senses. However, the extension to Burmester’s points with excess two is not possible. Similarly, the Ball’s point with excess one does not progress to Ball’s point with excess two. The paper also highlights that the traditional method, which is based on canonical coordinates, is ineffective when the four-bar linkage forms a parallelogram. Fortunately, this scenario can be effectively analyzed using the screw-based approach. Ultimately, the results presented can also serve as analytical solutions for 3-RR platforms with higher-order shakiness.
Citation: Machines
PubDate: 2024-08-21
DOI: 10.3390/machines12080576
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 577: Flow Field Noise Analysis and Noise
Reduction Research of Twin-Screw Air Compressor Based on Multi-Field
Coupling Technology
Authors: Yayin He, Xuyang He, Lijun Chen, Junli Wang, Yongqiang Zhao, Zhigui Ren
First page: 577
Abstract: To address the flow field noise problem in twin-screw air compressors, multi-physical-field coupling technology is employed to perform flow field noise calculations for the compressor. Based on the structural characteristics and noise generation principles of the twin-screw compressor, a noise reduction design method is proposed that employs a Helmholtz resonator and a three-chamber perforated muffler at the exhaust end. The muffler’s structural optimization is performed using a genetic algorithm, and the effectiveness of the noise reduction method is validated through calculations. The results indicate that the Helmholtz resonator effectively mitigates airflow pulsation at the exhaust port, stabilizing the flow and reducing low-frequency noise at the exhaust end. Additionally, the designed three-chamber perforated muffler achieves noise reduction across a broad frequency range. With this noise reduction method, the exhaust port noise of the twin-screw compressor is reduced from 100–114 dB to 37–68 dB. These findings provide valuable insights for vibration and noise reduction in twin-screw air compressors.
Citation: Machines
PubDate: 2024-08-21
DOI: 10.3390/machines12080577
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 578: Thermo-Mechanical Coupling Analysis of
Inserts Supporting Run-Flat Tires under Zero-Pressure Conditions
Authors: Cheng Xue, Liguo Zang, Fengqi Wei, Yuxin Feng, Chong Zhou, Tian Lv
First page: 578
Abstract: The inserts supporting run-flat tire (ISRFT) is mainly used in military off-road vehicles, which need to maintain high mobility after a blowout. Regulations show that the ISRFT can be driven safely for at least 100 km at a speed of 30 km/h to 40 km/h under zero-pressure conditions. However, the ISRFT generates serious heat during zero-pressure driving, which accelerates the aging of the tire rubber and degrades its performance. In order to study the thermo-mechanical coupling characteristics of the ISRFT, a three-dimensional finite element model verified by bench tests was established. Then, the stress–strain, energy loss and heat generation of the ISRFT were analyzed by the sequential thermo-mechanical coupling method to obtain the steady-state temperature field (SSTF). Finally, four kinds of honeycomb inserts bodies were designed based on the tangent method, and the SSTF of the honeycomb and the original ISRFT were compared. The results indicated that the high-temperature region of the ISRFT is concentrated in the shoulder area. For every 1 km/h increase in velocity, the temperature at the shoulder of the tire increases by approximately 1.6 °C. The SSTF of the honeycomb ISRFT is more uniformly distributed, and the maximum temperature of the shoulder decreases by about 30 °C, but the maximum temperature of the tread increases by about 40 °C. This study provides methodological guidance for investigating the temperature and mechanical characteristics of the ISRFT under zero-pressure conditions.
Citation: Machines
PubDate: 2024-08-21
DOI: 10.3390/machines12080578
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 579: Research on Scheduling Algorithm of Knitting
Production Workshop Based on Deep Reinforcement Learning
Authors: Lei Sun, Weimin Shi, Chang Xuan, Yongchao Zhang
First page: 579
Abstract: Intelligent scheduling of knitting workshops is the key to realizing knitting intelligent manufacturing. In view of the uncertainty of the workshop environment, it is difficult for existing scheduling algorithms to flexibly adjust scheduling strategies. This paper proposes a scheduling algorithm architecture based on deep reinforcement learning (DRL). First, the scheduling problem of knitting intelligent workshops is represented by a disjunctive graph, and a mathematical model is established. Then, a multi-proximal strategy (multi-PPO) optimization training algorithm is designed to obtain the optimal strategy, and the job selection strategy and machine selection strategy are trained at the same time. Finally, a knitting intelligent workshop scheduling experimental platform is built, and the algorithm proposed in this paper is compared with common heuristic rules and metaheuristic algorithms for experimental testing. The results show that the algorithm proposed in this paper is superior to heuristic rules in solving the knitting workshop scheduling problem, and can achieve the accuracy of the metaheuristic algorithm. In addition, the response speed of the algorithm in this paper is excellent, which meets the production scheduling needs of knitting intelligent workshops and has a good guiding significance for promoting knitting intelligent manufacturing.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080579
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 580: Analysis of Resistance Influencing Factors
of a Bench System Based on a Self-Developed Four-Wheel Drive Motor Vehicle
Chassis Dynamometer
Authors: Wanyou Huang, Dongying Liu, Ruixia Chu, Fangyuan Qiu, Zhenyu Li, Xiaoyue Jin, Hongtao Zhang, Yan Wang, Shaobo Ji
First page: 580
Abstract: In order to accurately simulate the actual road driving resistance of four-wheel drive motor vehicles based on the chassis dynamometer and efficiently test the vehicle performance, it is necessary to analyze the influencing factors of the additional loss resistance and the loading resistance of the chassis dynamometer bench system. In this paper, the effects of the drum speed, the sampling speed interval, and basic inertia on the test results of the additional loss resistance are tested and analyzed based on the self-developed chassis dynamometer of a four-wheel drive motor vehicle. The static and dynamic components of the additional loss resistance are defined by linear regression through ordinary least squares, and the additional loss resistance of the four-axis eight-drum chassis dynamometer and mainstream chassis dynamometer system for four-wheel drive motor vehicles are compared. In addition, the effects of the dynamometer type and the control strategy on the loading resistance are discussed, and the transient condition, steady-state condition, and overall operating condition deviation coefficient of loading force are defined, according to which the advantages and disadvantages of the control strategy of the chassis dynamometer system for four-wheel drive motor vehicles are evaluated. The analysis of the influencing factors and laws of the resistance of the four-wheel drive motor vehicle chassis dynamometer bench system can provide a reference basis for accurately simulating the resistance of vehicle road driving based on the bench testing.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080580
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 581: An Advanced Technique for the Detection of
Pathological Gaits from Electromyography Signals: A Comprehensive Approach
Authors: Karina Lenkevitciute, Jurgita Ziziene, Kristina Daunoraviciene
First page: 581
Abstract: The aim of this study was to determine the most appropriate advanced methods for distinguishing the gait of healthy children (CO) from the gait of children with cerebral palsy (CP) based on electromyography (EMG) parameters and coactivations. An EMG database of 22 children (aged 4–11 years) was used in this study, which included 17 subjects in the CO group and 5 subjects in the CP group. EMG time parameters were calculated for the biceps femoris (BF) and semitendinosus (SE) muscles and coactivations for the rectus femoris (RF)/BF and RF/SE muscle pairs. To obtain a more accurate classification result, data augmentation was performed, and three classification algorithms were used: support vector machine (SVM), k-nearest neighbors (KNNs), and decision tree (DT). The accuracy of the root-mean-square (RMS) parameter and KNN algorithm was 95%, the precision was 94%, the sensitivity was 90%, the F1 score was 92%, and the area under the curve (AUC) score was 98%. The highest classification accuracy based on coactivations was achieved using the KNN algorithm (91–95%). It was determined that the KNN algorithm is the most effective, and muscle coactivation can be used as a reliable parameter in gait classification tasks.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080581
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 582: Support Vector Machine-Based Fault Diagnosis
under Data Imbalance with Application to High-Speed Train Electric
Traction Systems
Authors: Yunkai Wu, Tianxiang Ji, Yang Zhou, Yijin Zhou
First page: 582
Abstract: The safety and reliability of high-speed train electric traction systems are crucial. However, the operating environment for China Railway High-speed (CRH) trains is challenging, with severe working conditions. Dataset imbalance further complicates fault diagnosis. Therefore, conducting fault diagnosis for high-speed train electric traction systems under data imbalance is not only theoretically important but also crucial for ensuring vehicle safety. Firstly, when addressing the data imbalance issue, the fault diagnosis mechanism based on support vector machines tends to prioritize the majority class when constructing the classification hyperplane. This frequently leads to a reduction in the recognition rate of minority-class samples. To tackle this problem, a self-tuning support vector machine is proposed in this paper by setting distinct penalty factors for each class based on sample information. This approach aims to ensure equal misclassification costs for both classes and achieve the objective of suppressing the deviation of the classification hyperplane. Finally, simulation experiments are conducted on the Traction Drive Control System-Fault Injection Benchmark (TDCS-FIB) platform using three different imbalance ratios to address the data imbalance issue. The experimental results demonstrate consistent misclassification costs for both the minority- and majority-class samples. Additionally, the proposed self-tuning support vector machine effectively mitigates hyperplane deviation, further confirming the effectiveness of this fault diagnosis mechanism for high-speed train electric traction systems.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080582
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 583: The Workspace Analysis of the Delta Robot
Using a Cross-Section Diagram Based on Zero Platform
Authors: Jun-Ho Hong, Ji-Ho Lim, Euntaek Lee, Dongwon Shin
First page: 583
Abstract: This paper introduces a new concept of a zero-platform delta robot with three key parameters affecting the shape and size of the workspace. This concept is applied to directly bring the torus configuration into the links of the robot and shows its usefulness in configuring and generating the workspace conveniently. Analyzing the workspace of parallel robots, such as delta robots, requires extensive computation due to the constraints between the links, typically requiring complex equations or numerical methods. This paper proposes a new method for quickly estimating the shape and size of the workspace using a cross-section diagram based on a geometrical analysis of the zero-platform delta robot. The shape and size of the workspace can be rapidly estimated because the intersection of three cross-section diagrams needs only the torus’s 2D operation. Comparing the workspace between the cross-section diagram and the 3D CAD software, this paper shows that the cross-section diagram can analyze the shape and size of the workspace quickly and give a more geometrical understanding of the workspace.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080583
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 584: Solving Flexible Job-Shop Scheduling Problem
with Heterogeneous Graph Neural Network Based on Relation and Deep
Reinforcement Learning
Authors: Hengliang Tang, Jinda Dong
First page: 584
Abstract: Driven by the rise of intelligent manufacturing and Industry 4.0, the manufacturing industry faces significant challenges in adapting to flexible and efficient production methods. This study presents an innovative approach to solving the Flexible Job-Shop Scheduling Problem (FJSP) by integrating Heterogeneous Graph Neural Networks based on Relation (HGNNR) with Deep Reinforcement Learning (DRL). The proposed framework models the complex relationships in FJSP using heterogeneous graphs, where operations and machines are represented as nodes, with directed and undirected arcs indicating dependencies and compatibilities. The HGNNR framework comprises four key components: relation-specific subgraph decomposition, data preprocessing, feature extraction through graph convolution, and cross-relation feature fusion using a multi-head attention mechanism. For decision-making, we employ the Proximal Policy Optimization (PPO) algorithm, which iteratively updates policies to maximize cumulative rewards through continuous interaction with the environment. Experimental results on four public benchmark datasets demonstrate that our proposed method outperforms four state-of-the-art DRL-based techniques and three common rule-based heuristic algorithms, achieving superior scheduling efficiency and generalization capabilities. This framework offers a robust and scalable solution for complex industrial scheduling problems, enhancing production efficiency and adaptability.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080584
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 585: Optimization of Desired Multiple Resonant
Modes of Compliant Parallel Mechanism Using Specific Frequency Range and
Targeted Ratios
Authors: Vin Low, Song Huat Yeo, Minh Tuan Pham
First page: 585
Abstract: In this paper, a dynamic optimization method capable of optimizing the dynamic responses of a compliant parallel mechanism (CPM), in terms of its multiple primary resonant modes, is presented. A novel two-term objective function is formulated based on the specific frequency range and targeted ratios. The first term of the function is used to optimize the first resonant mode of the CPM, within a specific frequency range. The obtained frequency value of the first mode is used in the second term to define the remaining resonant modes to be optimized in terms of targeted ratios. Using the proposed objective function, the resonant modes of a CPM can be customized for a specific purpose, overcoming the limitations of existing methods. A 6-degree-of-freedom (DoF) CPM with decoupled motion is synthesized, monolithically prototyped, and investigated experimentally to demonstrate the effectiveness of the proposed function. The experimental results showed that the objective function is capable of optimizing the six resonant modes within the desired frequency range and the targeted ratios. The highest deviation between the experimental results and the predictions among the six resonant modes is found to be 9.42%, while the highest deviation in the compliances is 10.77%. The ranges of motions are found to be 10.0 mm in the translations, and 10.8° in the rotations.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080585
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 586: Vibration and Flow Characteristics of a 200
MW Kaplan Turbine Unit under Off-Cam Conditions
Authors: Dandan Yan, Shuqiang Chen, Peng Ren, Weiqiang Zhao, Xiaobin Chen, Chengming Liu, Lingjiu Zhou, Zhengwei Wang
First page: 586
Abstract: Kaplan turbine units can adjust their blades to achieve wider outputs without a significant loss of efficiency. The combination of guide vane angle (GVA) and blade angle (BA) is selected based on efficiency curves obtained from cam tests. However, the vibration characteristics are not considered in the test. The vibration and flow characteristics are complex with different combinations of guide vane and blade angles. Different cam relation selection principles lead to varying machine vibration and flow characteristics. In this research, the flow and vibration characteristics were obtained by means of field test and numerical simulation. Vibration, pressure pulsation, and other stability indicators have been extracted and investigated under off-cam conditions. The flow and variation rules of different indicators have been thoroughly researched. The findings suggest that the magnitude of vibration in the X direction surpassed that in the Y direction for the head cover, upper frame, and lower frame under 22 experimental conditions. The disparity between the head cover and upper frame in both directions was not significant, whereas a substantial contrast existed between the lower frame in the X and Y directions. The calculation results indicate that when the guide vane angle was small, vortices appeared near the high-pressure edge of the runner in the vaneless region and caused disorganized flow lines in the runner, and this complex vortex behavior led to multiple frequency components in the pressure pulsation frequency domain. The conclusions provide references for the designers of Kaplan turbine units and improves the operating safety of Kaplan turbine power stations.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080586
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 587: Research on a Bearing Fault Diagnosis Method
Based on an Improved Wasserstein Generative Adversarial Network
Authors: Chengshun Zhu, Wei Lin, Hongji Zhang, Youren Cao, Qiming Fan, Hui Zhang
First page: 587
Abstract: In this paper, an advanced Wasserstein generative adversarial network (WGAN)-based bearing fault diagnosis approach is proposed to bolster the diagnostic efficacy of conventional WGANs and tackle the challenge of selecting optimal hyperparameters while reducing the reliance on sample labeling. Raw vibration signals undergo continuous wavelet transform (CWT) processing to generate time–frequency images that align with the model’s input dimensions. Subsequently, these images are incorporated into a region-based fully convolutional network (R-FCN), substituting the traditional discriminator for feature capturing. The WGAN model is refined through the utilization of the Bayesian optimization algorithm (BOA) to optimize the generator and discriminator’s semi-supervised learning loss function. This approach is verified using the Case Western Reserve University (CWRU) dataset and a centrifugal pump failure experimental dataset. The results showed improvements in data input generalization and fault feature extraction capabilities. By avoiding the need to label large quantities of sample data, the diagnostic accuracy was improved to 98.9% and 97.4%.
Citation: Machines
PubDate: 2024-08-22
DOI: 10.3390/machines12080587
Issue No: Vol. 12, No. 8 (2024)
- Machines, Vol. 12, Pages 494: Friction Stir Channeling in Heat Sink
Applications: Innovative Manufacturing Approaches and Performance
Evaluation
Authors: Sooraj Patel, Amit Arora
First page: 494
Abstract: The fabrication of compact heat exchangers with precisely designed micro- and mini-channels is crucial for enhancing the efficiency of thermal management systems. Friction stir channeling (FSC) emerges as a cost-effective advanced manufacturing process to create complex integral channels, offering channel shape and size flexibility. This review article highlights the pivotal role of processing parameters in channel formation and maintaining their integrity, necessitating a comprehensive understanding of material flow dynamics. A rigorous assessment has been conducted on the channel under mechanical stresses, including tension, bending, and fatigue. The paper emphasizes the potential of FSC to revolutionize heat sink applications by exploring the fundamental concepts, governing parameters, ongoing enhancements in tool design, microstructural and mechanical properties, and heat transfer performance.
Citation: Machines
PubDate: 2024-07-22
DOI: 10.3390/machines12070494
Issue No: Vol. 12, No. 7 (2024)
- Machines, Vol. 12, Pages 495: An Advanced Diagnostic Approach for Broken
Rotor Bar Detection and Classification in DTC Controlled Induction Motors
by Leveraging Dynamic SHAP Interaction Feature Selection (DSHAP-IFS) GBDT
Methodology
Authors: Muhammad Amir Khan, Bilal Asad, Toomas Vaimann, Ants Kallaste
First page: 495
Abstract: This paper introduces a sophisticated approach for identifying and categorizing broken rotor bars in direct torque-controlled (DTC) induction motors. DTC is implemented in industrial drive systems as a suitable control method to preserve torque control performance, which sometimes shows its impact on fault-representing frequencies. This is because of the DTC’s closed-loop control nature, whichtriesto reduce speed and torque ripples by changing the voltage profile. The proposed model utilizes the modified Shapley Additive exPlanations (SHAP) technique in combination with gradient-boosting decision trees (GBDT) to detect and classify the abnormalities in BRBs at diverse (0%, 25%, 50%, 75%, and 100%) loading conditions. To prevent overfitting of the proposed model, we used the adaptive fold cross-validation (AF-CV) technique, which can dynamically adjust the number of folds during the optimization process. By employing extensive feature engineering in the original dataset and then applying Shapely Additive exPlanations(SHAP)-based feature selection, our methodology effectively identifies informative features from signals (three-phase current, three-phase voltage, torque, and speed) and motor characteristics. The gradient-boosting decision tree (GBDT) classifier, trained using the given characteristics, extracts consistent and reliable classification performance under different loading circumstances and enables precise and accurate detection and classification of broken rotor bars. The proposed approach (SHAP-Fusion GBDT with AF-CV) is a major advancement in the field of machine learning in detecting motor anomalies at varying loading conditions and proved to be an effective mechanism for preventative maintenance and preventing faults in DTC-controlled induction motors byattaining an accuracy rate of 99% for all loading conditions.
Citation: Machines
PubDate: 2024-07-22
DOI: 10.3390/machines12070495
Issue No: Vol. 12, No. 7 (2024)
