Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Tigist Fetene Adane, Maria Floriana Bianchi, Andreas Archenti, Mihai Nicolescu Machining of parts by using dedicated production systems has been, and continues to be, a viable manufacturing method. There are situations, however, where this type of system is not feasible due to changes in product type, customer demand, work-piece material, or design specification. From a competitive manufacturing environment, production system selection is a crucial issue for all component manufacturing companies. Improper selections could negatively affect the overall performance of a manufacturing system, for instance the productivity, as well as the cost and quality of manufactured components. In this paper, the application of system dynamics modelling and simulation of a complex manufacturing process is presented as a potential tool to investigate and analyse the performance of manufacturing system in response to disturbances in the system’s inputs (e.g., volume of products). In order to investigate the model soundness, a case study applied to the manufacturing of an engine block will be examined. The model presented here has been developed based on current engine block production for the vehicle manufacturing industry. Such a model can assist manufacturing system selection–centered round the capacity to control machining system parameters –as a testable way to choose a machining strategy from pre-selected performance criteria. More specifically, the benefit of this research lies in the fact that it will enable companies to implement improved potential manufacturing system optimization that responds during unexpected demand fluctuations. In addition, it will help in understanding the complex interaction between the process and operational parameters of a manufacturing system and help identify those critical parameters, ones that can lead to an optimizing strategy in the manufacturing standards of engine block production.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Samir Ouaret, Jean-Pierre Kenné, Ali Gharbi This paper deals with the control of a hybrid manufacturing/remanufacturing system subject to random breakdowns and repairs. Given the heterogeneity of returned products, the remanufacturing machine deteriorates with time as a result of imperfect repairs, and needs to be replaced. The manufacturing machine receives homogeneous raw materials, and is not affected by this type of deterioration. The main objective of this paper is to find the production rates of both machines and the replacement rate of the remanufacturing machine that minimize the total cost, including production, inventory holding, backlog, repair and replacement costs, over an infinite planning horizon. A novel mathematical model is proposed for the underlying class of problems related to the history of breakdowns and repairs. This model is characterized by the extension of the state space, leading to a Markov decision model, allowing a derivation of the optimality conditions. Otherwise, such conditions are impossible to obtain. Optimality conditions in the form of Hamilton-Jacobi-Bellman (HJB) equations are thus developed. We show that despite the increased state space dimension, the problem remains tractable, and the solutions of HJB equations are obtained numerically. Finally, an illustrative example and a sensitivity analysis are provided to verify the robustness of the control policies obtained.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Wenyu Zhang, Jiepin Ding, Yan Wang, Shuai Zhang, Zhiying Xiong Scheduling multiple heterogeneous tasks in a manufacturing system to satisfy customized requirements becomes challenging, especially in uncertain manufacturing environment. In cloud manufacturing, a serious problem is to schedule multiple heterogeneous tasks to balance the benefit conflicts among customers, manufacturing enterprises, and manufacturing platform comprehensively. Therefore, this study formulates the multi-task scheduling problem mathematically as a new fuzzy mixed-integer linear programming (FMILP) model based on multi-perspective collaborative optimization and fuzzy set theory. To solve the FMILP model, an extended genetic algorithm (EGA) with the interval-valued intuitionistic fuzzy entropy weight (IVIFEW) method is proposed. The IVIFEW method is adapted to obtain the preference of QoS attributes and task priority. In addition, the basic genetic algorithm is improved by integrating a migration operator, local search, and restart strategy to maintain the diversity of population and enhance the exploitation ability. A suitable parameter combination of EGA is found in a series of experiments based on the Taguchi method. The experimental results demonstrate that the proposed EGA solves the FMILP model effectively, providing better optimal solutions compared with the baseline algorithms.
Abstract: Publication date: Available online 29 October 2019Source: Journal of Manufacturing SystemsAuthor(s): Qinglin Qi, Fei Tao, Tianliang Hu, Nabil Anwer, Ang Liu, Yongli Wei, Lihui Wang, A.Y.C. Nee Digital twin is revolutionizing industry. Fired by sensor updates and history data, the sophisticated models can mirror almost every facet of a product, process or service. In the future, everything in the physical world would be replicated in the digital space through digital twin technology. As a cutting-edge technology, digital twin has received a lot of attention. However, digital twin is far from realizing their potential, which is a complex system and long-drawn process. Researchers must model all the different parts of the objects or systems. Varied types of data needed to be collected and merged. Many researchers and participators in engineering are not clear which technologies and tools should be used. 5-dimension digital twin model provides reference guidance for understanding and implementing digital twin. From the perspective of 5-dimension digital twin model, this paper tries to investigate and summarize the frequently-used enabling technologies and tools for digital twin to provide technologies and tools references for the applications of digital twin in the future.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Li Yi, Christopher Gläßner, Jan C. Aurich Additive Manufacturing (AM) is the umbrella term for manufacturing processes that add materials layer by layer to create parts. AM technologies show numerous potentials in terms of rapid prototyping, tooling and direct manufacturing of functional parts and imply revolutionary benefits for the manufacturing industry. Currently, many industrial areas are marching to a more comprehensive application of AM. Hence, the development of new tools, methods, and concepts for guiding companies to implement AM technologies requires more research attention. This paper introduces the results of a research project carried out by academic and industrial partners from the German commercial vehicle industry. The research project addressed four issues for a long-term application of AM technologies: identification of barriers for AM applications, cost estimation for AM application, design of hybrid additive-subtractive process chains, and quality management with AM.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Tianyi Wu, Xiaobing Ma, Li Yang, Yu Zhao Wait time due to the exhaustion of raw materials or lack of demand is common in manufacturing/production processes, which provides cost-effective maintenance opportunities. This paper designs a two-phase opportunistic maintenance framework based on defect information, which integrates properties of production waits into the decision-making process. At the first phase, a finite number of inspections are executed to reveal the defective state, followed by imperfect preventive repair. At the second phase, no maintenance action is taken until reaching a scheduled maintenance window (postponed maintenance) or arrivals of production waits (opportunistic maintenance). The integration of imperfect repair with postponed and opportunistic maintenance enables a sufficient utilization of remaining useful lifetime and a flexible resource allocation. Under the constraint of the steady-availability requirement, the optimal policy minimizing the system maintenance cost is obtained via the genetic algorithm. A case study from a steel convertor plant shows that our policy outperforms some classic maintenance policies.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Yuhang Yang, Yifang Zhang, Y. Dora Cai, Qiyue Lu, Seid Koric, Chenhui Shao High-resolution spatiotemporal data is crucial for characterizing, modeling, and monitoring the space–time dynamics of complex systems in manufacturing. However, the acquisition of such data is generally expensive and time-consuming. Spatiotemporal interpolation aims to predict the values at unmeasured locations using measured data, and emerges as a promising solution to cost-effectively characterizing spatiotemporal processes. Since the interpolation performance is largely influenced by the available measurement data, an intelligent measurement strategy is an important prerequisite to the success of interpolation methods. In this paper, a hierarchical measurement strategy is developed to achieve a balance between interpolation precision and measurement cost in spatiotemporal interpolation. A hierarchical decision-making problem is formulated to determine the observation times and measurement locations at each observation. To expedite the solution search process, hierarchical genetic algorithm is adopted and implemented using high-performance computing. Moreover, a new form of the covariance function is developed using a Bessel additive periodic variogram to more accurately model the periodic spatial variations in spatiotemporal processes. Case studies using real-world data collected from ultrasonic metal welding are reported to demonstrate the effectiveness of the proposed method.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Johannes Fisel, Yannick Exner, Nicole Stricker, Gisela Lanza Current trends, such as customers' demand for individual products and shorter product life cycles, are addressed by companies through a greater variety of products and variants. With regard to the line balancing of flow assembly systems, however, adjustments are associated with high investments, which requires a new planning approach for assembly line balancing. Existing approaches do not consider the reallocation of assembly tasks or the dimensioning of system-inherent flexibility and changeability according to requirements. Furthermore, they neglect the uncertainty of the future market situation. The proposed approach aims at optimizing the line balancing of flow assembly systems, taking into account the potential need for adaptation in order to meet this uncertain planning environment. For this purpose, the exchange of occurring costs as well as flexibility and changeability of the system is focused. Based on scenarios, potential future compositions of the variant mix are investigated and the resulting implications for the assembly system are derived. By applying the approach, an adequate adaptable assembly line balancing is generated by performing a mixed integer linear optimization. Since the evaluation and identification of adequacy are subject to subjective factors, several potentially adequate solutions are generated, which differ in terms of costs, flexibility and changeability. The result of the presented approach is a front of pareto-optimal assembly line balancing configurations. In order to show its practical applicability, a use case in automotive assembly line balancing is presented.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Annamaria Gisario, Michele Kazarian, Filomeno Martina, Mehrshad Mehrpouya The applications of Additive Manufacturing (AM) have been grown up rapidly in various industries in the past few decades. Among them, aerospace has been attracted more attention due to heavy investment of the principal aviation companies for developing the AM industrial applications. However, many studies have been going on to make it more versatile and safer technology and require making development in novel materials, technologies, process design, and cost efficiency. As a matter of fact, AM has a great potential to make a revolution in the global parts manufacturing and distribution while offering less complexity, lower cost, and energy consumption, and very highly customization. The current paper aims to review the last updates on AM technologies, material issues, post-processes, and design aspects, particularly in the aviation industry. Moreover, the AM process is investigated economically including various cost models, spare part digitalization and environmental consequences. This review would be helpfully applied in both academia and industry as well.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Woo-sung Kim, Kyungsu Park Inspired by flexible manufacturing systems with setups, in this study, we investigate the steady-state behavior of flow line models with deterministic service durations. In such systems, different types of products with different batch sizes are produced in a single production line, and a setup is required when switching from one type of product to another. In this study, we aim at analyzing the effect of different setups on a system’s performance metrics, i.e., the maximum production rate and delay distribution of a product experienced inside the system. The maximum production rate of a deterministic flow line is the reciprocal of its bottleneck process time; however, the different setups existing in a system complicate the analysis. Furthermore, setups are necessary in flexible manufacturing systems. The systems that are the subject of our model are steel pipe manufacturing process and semiconductor wafer fabrications. Additionally, we obtain and analyze a two-dimensional, discrete-time, time-homogeneous Markov chain model to derive the system’s performance measures.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Adolfo Muñoz, Xavier Mahiques, J. Ernesto Solanes, Ana Martí, Luis Gracia, Josep Tornero In recent years, the quality control of car body surfaces production lines have been put in the context of Industry 4.0. The emergence of automatic defect detection systems have helped to standardize the brand quality and gather information about all quality control tasks performed by workers. However, current worker interfaces used to indicate the location and other characteristics of the defects found by these systems have overcome the ergonomics of workers and increased their stress at work. This paper presents a novel mixed reality-based user interface for quality control inspection which is more intuitive, in order to improve the ergonomics of workers, reduce their stress at work and improve the productivity of current quality control production lines. An experimental prototype is shown in the paper in order to demonstrate the benefits of the proposed interface. In addition, the paper shows the results of several usability tests that compare the proposed mixed reality-based user interface with current interfaces used in important factories such as Mercedes-Benz, analyzing the benefits and drawbacks of each interface.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Chen Zheng, Xiansheng Qin, Benoît Eynard, Jing Bai, Jing Li, Yicha Zhang Continuously growing pressures due to the shorter development lead-time and the uncertainty of increasingly complex market require a high flexibility from production companies and their manufacturing systems. Comparing with large enterprises, small and medium-sized enterprises (SMEs) which lack concentrated market power and powerful original equipment manufacturers (OEMs) have difficulty and risk to develop flexible and customised robotic manufacturing systems. Nowadays, although various design methods for robotic manufacturing systems have been made to fix this problem, however, there still exist two main challenges: flexibility in design and high degree of customisation.To deal with the existing challenges, this paper proposes an SME-oriented design approach based on a configuration design paradigm. This method can offer decision support to designers to form flexible architecture for robotic manufacturing systems and, at the same time, give more interactive configuration freedom to customers so as to achieve a high productivity and flexibility for customisation but with less risk and cost of product development. To validate the proposed method, two design cases of robotic manufacturing systems are presented for demonstration.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Manoj Babu, Pasquale Franciosa, Dariusz Ceglarek In-line dimensional inspection of free form surfaces using robotic 3D-optical scanners provide an opportunity to reduce the mean-time-to-detection of product quality defects and has thus emerged as a critical enabler in Industry 4.0 to achieve near-zero defects. However, the time needed to inspect large industrial size sheet metal parts by 3D-optical scanners frequently exceeds the production cycle time (CT), consequently, limiting the application of in-line measurement systems for high production volume manufacturing processes such as those used in the automotive industry. This paper addresses the aforementioned challenge by developing the Spatio-Temporal Adaptive Sampling (STAS) methodology which has the capability for (i) estimation of whole part deviations based on partial measurement of a free form surface; and, (ii) adaptive selection of the next region to be measured in order to satisfy pre-defined measurement criterion. This is achieved by first, modelling spatio-temporal correlations in the high dimensional Cloud-of-Points measurement data by using a dimension reduced space-time Kalman filter; then, dynamically updating the model parameters during the inspection process by incorporating partial measurement data to predict entire part deviations and adaptively choose the next critical region of the part to be measured.The developed STAS methodology enhances the current free form surface inspection models, which are mostly based on spatial analysis; into spatio-temporal model, which uses (i) the spatial analysis to model part deformation; and, (ii) temporal analysis to model autoregressive behaviour of the manufacturing process for prediction of next part deviations. This provides capability to predict the whole part deviation based on partial measurement information and consequently reduces measurement cycle time. The industrial case study using a robotic 3D-optical scanner for the measurement of an automotive door inner part demonstrates the STAS methodology, which resulted in (i) a 3 Sigma error of prediction of whole part deviations within 0.27 mm based on measurement of 33% of the part surface; and, (ii) a corresponding CT reduction of 42.2% from 510.5 s required by current best practice to measure the whole part to 295.18 s required to partially measure the part.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Fengtian Chang, Guanghui Zhou, Chao Zhang, Zhongdong Xiao, Chuang Wang The development of product-service-system (PSS) urges the emergence of service-oriented maintenance mode (SOMA), which leads to the shift of maintenance manners from traditional high-cost in-house maintenance to the performance-based proactive service offerings for complex multi-component systems. In this case, a novel and adaptive maintenance grouping strategy that considers service responses, service interactions and dynamic prediction requirements is desired to implement the predictive maintenance planning. Thus, this paper presents a service-oriented dynamic multi-level predictive maintenance grouping strategy. It involves component level, dynamic grouping level and system level to fulfill the individual optimization and grouping optimization. Firstly, the individual service time based on real-time remaining useful life (RUL) distribution information is optimized and obtained with the minimum average cost. Secondly, considering the existing economic and resource dependence in predictive and corrective services, this paper dynamically groups the predicted optimal individual services from rolling planning horizon at each calculated service planning time. The penalty costs and grouping service costs are both constructed. Then, a modified k-means method is designed to find out the optimal short-term grouping execution strategy from medium-term prediction information where the average cost savings and relative availability improvement degree are regarded as the optimization objectives. Finally, some numerical examples with three predictive grouping scenarios show that our grouping strategy could provide a feasible and effective long-term dynamic maintenance planning for proactive OEM or PSS providers. It is adapted to the SOMA on complex multi-component systems.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Chao Guan, Zeqiang Zhang, Silu Liu, Juhua Gong The novel multi-workshop facility layout problem presented in this paper involves the placement of a group of departments into several workshops; it deals with the distribution of departments over workshops and their optimal exact coordinates without overlapping. In considering practical situations, the internal material handling flows and external transport flows are taken into account in the problem. In this study, the problem is first formulated as a mixed integer linear programming model with three objectives: minimization of overall material handling costs, minimization of number of workshops, and maximization of utilization ratio of workshop floor. Thereafter, the proposed multi-objective particle swarm optimization algorithm with an innovative discrete framework and incorporated with a two-stage approach is employed to search for feasible solutions locally and globally. Finally, several benchmark instances derived from literature that satisfy our case requirements are employed to evaluate the performance of the proposed method; highly preferable results are typically achieved.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Bugra Alkan, Robert Harrison Highly diverse factors including technological advancements, uncertain global market and mass personalisation are believed to be main causes of ever-growing complexity of manufacturing systems. Although complex systems may be needed to achieve global manufacturing requirements, complexity affects on various factors, such as: system development effort and cost, ease of re-configuration, level of skill required across the system life-cycle (e.g. design, operate and maintain). This article aims to develop a scientifically valid and industrially applicable complexity assessment approach to support early life-cycle phases of component-based automation systems against unwanted implications of structural system complexity. The presented approach defines component-based automation system as a constellation of basic components which can be represented in various design domains, such as: mechanical, electrical, pneumatic, control, etc. Accordingly, structural complexity is expressed as the combination of both inherent complexity of system entities and topological complexity resulting from the integration of elements of such constellations in a multi-layered network. The proposed approach is used to specify and implement a complexity assessment module which can be integrated into a series of virtual system design software solutions, in order to add complexity assessment as part of the design support and validation tools used by manufacturing engineers. Consequently, the proposed approach is integrated into the vueOne virtual engineering tool, wherein virtual automation system design data can be streamlined and used as input to the theoretical complexity model. In the developed tool, only mechanical and logical design domains are considered due to the limited data availability in early design phase. Inherent complexity of both mechanical and logical system entities and their interactions are expressed as a function of domain-specific design elements, and topological complexity is defined as the graph energy of the corresponding design connectivity matrix. Furthermore, the values of mathematical model parameters are determined based on an optimisation study, where subjective opinions of system/control engineers regarding the effort/difficulty associated with the development of thirty different component-based automation system designs are correlated with the corresponding complexity model outputs to minimise the prediction errors. The proposed approach is also demonstrated on a modular production system consisting of four sub-modules. The study shows that the approach can help designers/managers to better identify root causes of structural system complexity, and provides a systemic approach to compare alternate system designs during early system planning phase.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Lukas Baier, Julian Frommherz, Elmar Nöth, Toni Donhauser, Peter Schuderer, Jörg Franke Fast identification of failure root causes is a major task in minimizing rejects in manufacturing. Due to increasing complexity of products and supply chains many interdependencies affect the final product quality. As knowledge about every possible interdependence is hardly held by individuals, data analysis strategies are required to evaluate captured information. Hence, we propose a root cause identification method for determining the main influencing factors on end products failing end of line tests. Additionally, graphical representation of calculation results in spectrograms similar to audio signals facilitates human interpretation. The evaluation of the proposed method on the basis of a use case proves the applicability in a real scenario. The method is able to identify the root cause for rejects within short periods of time. In this specific case it shortened the analysis time by a factor of about 50. In the future, it empowers smart production systems to automatically identify failure root causes and to take countermeasures like adjusting process parameters.
Abstract: Publication date: October 2019Source: Journal of Manufacturing Systems, Volume 53Author(s): Xiao Zhang, Benjamin Lies, Hao Lyu, Hantang Qin Electrohydrodynamic inkjet (e-jet) printing is a technique which utilizes electrical forces to generate droplets in micro/nano scale using conductive inks. Currently, there is no procedure in place to measure the printed patterns without taking the sample away from the printer setup. Removal of the substrate from the printing stage during the e-jet printing process prevents any additional work from being performed on the sample. We investigated the application of scalar diffraction for the in-situ measurement and digital reconstruction of opaque material printed on transparent substrates. Measurement and characterization of the printed material can be achieved in-situ to alter printing condition in process for quality assurance. In order to accomplish the sample reconstruction, a digital recording of a scalar diffraction pattern in the image plane was employed in this paper with a magnification of 5× with the help of a combination of lenses. The reconstructed images were then compared to images captured by an offline high-resolution microscope. The results indicated a submicron accuracy of the feature radii and the locations of feature centers. In addition to the quantitative measurements, this method also allows for the operator to view the overall form of the printed patterns. Our findings demonstrate an effective approach for in-situ monitoring of e-jet printing and printed patterns, which could pave the way for the industrial application in printing testing field.
Abstract: Publication date: Available online 26 August 2019Source: Journal of Manufacturing SystemsAuthor(s): Qianqian Dong, Raed Kontar, Min Li, Gang Xu, Jinwu Xu Statistical monitoring of advanced production processes is becoming increasingly challenging due to the large number of key performance variables that characterize a process. These variables often are non-Gaussian, highly correlated and exhibit non-linear dependencies. Traditional multivariate monitoring methods handle non-Gaussian data through combining both independent component analysis (ICA) and support vector data description (SVDD). However, redundant independent components not only increase the modeling complexity of SVDD but also reduce the accuracy of the monitoring. In this article, we solve the above problems by introducing a Laplacian based weighting score to adjust the ICA-SVDD procedure. The key aspect of our model is that independent components are automictically selected using a Laplacian algorithm, which are inputted to a SVDD model to determine the control limits. The advantageous features of the proposed method are demonstrated through a numerical study as well as a case study which concerns an application to a hot rolling process for monitoring steel production processes.
Abstract: Publication date: Available online 3 July 2019Source: Journal of Manufacturing SystemsAuthor(s): Qi Tian, Weihong Guo In order to stay responsive to evolving customer demands and to meet the need for greater product customizations, many manufacturing enterprises are recognizing the need to quickly reconfigure their manufacturing systems and supply chains. Making reconfiguration decisions requires a system-level optimization that involves many factors such as manufacturing tasks, outsourcing decisions, supply chain configurations, as well as risks. This paper proposes a graph-based cost model to optimize the configuration of manufacturing supply chain networks to support reconfiguration decision-making. An optimization model is formulated to minimize the total cost of the manufacturing enterprise with the consideration of operating cost and reconfiguration cost. To effectively quantify the reconfiguration cost, a graph-based cost model is developed to characterize the relationship between the graphical similarity of two supply chain networks and the reconfiguration cost. Outsourcing decisions and supply chain risks are also considered in the proposed model. A case study on a supply chain for laptop computer assembly is presented to demonstrate the effectiveness of the proposed method.
Abstract: Publication date: Available online 11 June 2019Source: Journal of Manufacturing SystemsAuthor(s): Botao Zhang, Archak Goel, Omkar Ghalsasi, Sam Anand This paper discusses a set of geometry based computational pre-processing algorithms developed for Powder Bed Fusion Additive Manufacturing (PBFAM) processes. To start with, based on an initial part design, an automatic support structure generation module generates customized CAD-based support structures for a given part build orientation. Various additive manufacturing (AM) parameters and Design for Additive Manufacturing (DFAM) metrics are calculated on the fly for assigning producibility scores at different part build orientations. A set of stand-alone computational geometry-based algorithms with associated graphical user interfaces (GUI) are developed for calculating support structure parameters, as well as for detecting and highlighting DFAM features that are difficult to manufacture. These stand-alone tools provide a quantified output for each of the parameters or features, which are then used downstream during the producibility index (PI) calculation. An algorithm that evaluates ease of removing supports during the post-processing phase, and suggests the optimum number of setups needed to remove support structures is developed. Finally, Producibility Index, which is a weighted optimization metric, brings together the quantified outputs of the DFAM analysis, support structure parameters, accessibility analysis and suggests the best build orientations for the given part geometry. All the algorithms are implemented within the Siemens NX modelling environment utilizing C++ and NX API functions. The developed algorithm and tools have been succesfully demonstrated on two sample parts.
Abstract: Publication date: Available online 22 April 2019Source: Journal of Manufacturing SystemsAuthor(s): Maxwell Praniewicz, Thomas R. Kurfess, Christopher Saldana Multi-axis machining processes are often used to fabricate complex components with tight geometric tolerances. Thus, the need for highly accurate 5-axis machine tools is imperative in high precision industries such as aerospace and mold and die. Often, construction errors result in geometric errors within the machine tool. These errors must be identified and compensated in order to guarantee accuracy of the machine. In this work, kinematic equations of motion for a BC-style machine tool were derived while incorporating the 8 distinct kinematic error constants associated with a 5-axis machine tool. A method is presented to derive these kinematic error constants from eccentricity values obtained using 3-axis simultaneous tests for table-table style 5-axis machine tools. To validate this method, error constants were input into the kinematic simulation. Eccentricity values were then output from the simulation and error constants were derived and compared to the input values. It was shown that if the procedure is followed, the error constants can be correctly derived and compensated. This method was then implemented on a BC-style machine tool and error constants were derived.
Abstract: Publication date: Available online 22 April 2019Source: Journal of Manufacturing SystemsAuthor(s): Brian K. Paul, Steven Kawula, Chuankai Song Heating, ventilation, and air conditioning systems represent nearly half of building energy usage in the United States. Recent building regulations requiring increased air turnover within buildings will result in even greater air conditioning usage. To alleviate these concerns, membrane-based heat-and-moisture exchangers known as energy recovery ventilators have been developed to reduce the energy expenditure of air conditioning systems by pre-conditioning incoming air through sensible and latent heat transfer with building air exhaust. In this work, we propose and validate a manufacturing process design for channel lamination based on surface mount adhesives capable of meeting the process requirements of a membrane-based ventilator. In particular, the device requires rib supports with height-to-width aspect ratios greater than one. The proposed manufacturing process design is capable of meeting this process requirement by stretching the adhesive after initial adhesive tacking. A manufacturing process flow diagram, a machine tool specification and a cost model are proposed for meeting process requirements. A set of design constraints are developed detailing the adhesive requirements necessary for meeting requirements. The cost model is validated by building a sub-scale mesochannel array demonstrating the ability to meet process requirements. Results show that additional work is needed to validate the curing step but that the process is capable of producing a mesochannel array with an acceptable level of variation.
Abstract: Publication date: Available online 22 April 2019Source: Journal of Manufacturing SystemsAuthor(s): Ji Hoon Kim, Lyang Suan Wang, Kaushalya Putta, Payam Haghighi, Jami J. Shah, Pete Edwards Multi-Material Joining Design Explorer is discussed in this paper, which is a knowledge-based advisory system to help structural designers at the early design phase to select the potential joining methods. Data mining on various joining methods was conducted from any available sources, such as experts from academia and industry, handbooks, and vendors. Collected data was organized in a concept map which is an informal way of representing the data structure. The data were arranged into several categories according to their characteristics which include joinable materials, mechanical and design requirements, geometry, and so on. Common parameters and unique parameters were extracted from deep investigation of the gathered data to create a formalized data structure. A database using a general tree structure was then created to be fed into the advisory system. Searching algorithm using SQL query was implemented to navigate through the database to find the joining methods that match the requirements defined by the user. Two test cases were generated to validate the function of the knowledge-based system.
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