Journal of Manufacturing Systems最新文献

英文中文

MetaFactory: A cloud-based framework to configure and generate dynamic data structures from the STEP-NC knowledge graph

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-03-04 DOI: 10.1016/j.jmsy.2025.02.012

Wenlei Xiao , Tianze Qiu , Jiurong Guo , Gang Zhao

In our previous studies, twin-oriented manufacturing has been identified as a crucial solution to address the manufacturing crisis. Within this context, the notion of “digital twin as a service” necessitates that various twin services share and communicate with each other in a standardized manner. STEP-NC offers a potentially unified model to facilitate data exchange, providing object-oriented and standardized data models for a comprehensive representation of manufacturing resources in the digital realm. However, the complexity of STEP-NC renders it too cumbersome for implementation in diverse cloud-based services or PC-based software. This complexity is a fundamental reason why STEP-NC has struggled to find application in commercial CNC systems despite years of research. To overcome this technical challenge, this paper introduces a novel concept termed “dynamic STEP-NC data structure”, inspired by the dynamic language philosophy of dynamic programming language (such as Python). This approach allows different services and software packages to maintain their own data definitions while still aligning with the original STEP-NC definition. We have developed a framework called MetaFactory that supports the configuration of streamlined data structures and generates the corresponding program code required by various service developers. On this basis, we implemented automatic modeling for a STEP-NC object-oriented database. Using the data trimming and dimensionality reduction methods provided by MetaFactory, several prototype systems for different application scenarios have been developed.

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引用次数: 0

An efficient online geometric simulation algorithm for real-time CNC machining process based on look-ahead method

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-03-04 DOI: 10.1016/j.jmsy.2025.02.013

Tianze Qiu , Bofang Dai , Wenlei Xiao , Chen Zhao , Gang Zhao

Intelligent CNC machining requires advanced online geometric simulation to improve transparency and optimize machining processes. The simulation algorithms need to be efficient enough to keep up with machine tool motions. However, traditional algorithms, which typically discretize the entire blank initially, often result in redundant computations, hindering efficiency in online environments. To strike a balance between efficiency and accuracy, this paper presents an efficient online simulation algorithm with three key innovations. First, the algorithm incorporates the concept of look-ahead into geometric simulation to pinpoint workpiece areas likely to contact the cutting tool. Second, it employs a dynamic voxel partitioning mechanism that adapts to the cutting tool’s movement, reducing data structures and eliminating redundant computations. Third, a hybrid modeling approach integrates voxel model spatial indexing with Tri-dexel model Boolean operations, enabling rapid local positioning and efficient micro-structural representation of the workpiece. Additionally, the algorithm is further optimized in key stages such as online interpolation and surface reconstruction. This algorithm has been integrated into several online simulation software systems and tested and validated on multiple typical 3/5-axis workpieces. Actual machining experiments confirm its efficiency, with over 99% of simulation computation times below 10 ms, meeting the requirements for online environments. The algorithm also demonstrates excellent performance in simulating large-scale aerospace workpieces, providing a solid foundation for real-time synchronization of geometric and physical parameters.

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引用次数: 0

Digital twin of dynamics for parallel kinematic machine with distributed force/position interaction

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-03-03 DOI: 10.1016/j.jmsy.2025.02.019

Fangyan Zheng , Xinghui Han , Lin Hua , Wenjun Xu

Extensibility is significant for digital twin (DT) manufacturing systems. However, existing DT models mainly focus on a specific task in manufacturing. The main challenge lies in the specific physical model when addressing different tasks. In fact, the dynamics of machines are the physical basis for most applications, e.g., motion planning, production scheduling, process monitoring, machine maintenance, and so on. Therefore, the Digital Twin of dynamics (DTOD) for machines will be a foundation for a highly integrated and extensible DT system. However, due to the challenges in real-time dynamic modeling and the corresponding data interaction methods, the DTOD for parallel kinematic machines (PKM) has not been realized.

Facing this challenge, this paper develops a DTOD for PKM with distributed force/position interaction. Firstly, a simplified rigid-flexible coupling dynamic model of PKM, considering link deformations, is established for real-time calculation. Then, a distributed position/force interaction method based on Kalman filter-based data fusion is proposed to realize high-performance data interaction between cyber and physical space. On this basis, a five-dimension digital twin model for DTOD of PKM is established. Further, the DTOD system with an architecture comprising dual central processors and multiple distributed edge executors/sensors is developed and validated by aircraft gear manufacturing, showing 80 % prediction accuracy of dynamic error. Finally, to show the extensibility, integrated error correction for aircraft gear manufacturing is proposed as an extended application of the DTOD system. The gear error is reduced to 218 μm (with error correction) from 503 μm, representing a reduction of about 57 %, showing the high performance of the developed DTOD system and its high application potential.

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引用次数: 0

Energy-efficient human-robot collaborative U-shaped disassembly line balancing problem considering turn on-off strategy: Uncertain modeling and solution method

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-27 DOI: 10.1016/j.jmsy.2025.02.004

Zhongwei Huang , Honghao Zhang , Guangdong Tian , Mingzhi Yang , Danqi Wang , Zhiwu Li

The quantity of waste automobile is becoming very large. Waste automobile not only occupies resources, but also easily pollutes the environment. How to realize the efficient and green treatment of recycled automobile is a hot topic in the industrial circular economy today. The disassembly line is the most efficient way to address large-scale waste automobile. Therefore, this paper takes the disassembly experiment of recycled automobile engine as the information orientation to construct energy-efficient human-robot collaborative U-shaped disassembly line balancing (HRU-DLB) framework considering turn on-off strategy. An engine disassembly information modeling method is proposed to address the issue on the actual disassembly space limitation. Establish a based-normal cloud HRU-DLBP mathematical model including disassembly smoothness, disassembly energy consumption (DEC), disassembly cost, disassembly idle time and disassembly carbon emission (DCE). To further reduce the disassembly energy consumption and carbon emission, the well-accepted energy-saving measure, known as the turn on-off strategy, is also integrated. Subsequently, a hybrid multi-objective optimization algorithm called ALNS-NSGA II, which combines the NSGA-II algorithm and adaptive large-scale neighborhood search algorithm is developed to explore the optimal Pareto solution set. Finally, the novel behavioral decision model is proposed to select the optimal HRU-DLB scheme. The comparative analysis shows that the turn on-off strategy can reduce DEC by 26 % and DCE by 3.1 % in a cycle time, respectively. The computational results confirm the feasibility and effectiveness of the proposed ALNS-NSGA II in solving the HRU-DLBP. The comparative analysis and sensitivity analysis demonstrate that the proposed behavioral decision model has better ranking and classification effects.

{"title":"Energy-efficient human-robot collaborative U-shaped disassembly line balancing problem considering turn on-off strategy: Uncertain modeling and solution method","authors":"Zhongwei Huang , Honghao Zhang , Guangdong Tian , Mingzhi Yang , Danqi Wang , Zhiwu Li","doi":"10.1016/j.jmsy.2025.02.004","DOIUrl":"10.1016/j.jmsy.2025.02.004","url":null,"abstract":"<div><div>The quantity of waste automobile is becoming very large. Waste automobile not only occupies resources, but also easily pollutes the environment. How to realize the efficient and green treatment of recycled automobile is a hot topic in the industrial circular economy today. The disassembly line is the most efficient way to address large-scale waste automobile. Therefore, this paper takes the disassembly experiment of recycled automobile engine as the information orientation to construct energy-efficient human-robot collaborative U-shaped disassembly line balancing (HRU-DLB) framework considering turn on-off strategy. An engine disassembly information modeling method is proposed to address the issue on the actual disassembly space limitation. Establish a based-normal cloud HRU-DLBP mathematical model including disassembly smoothness, disassembly energy consumption (DEC), disassembly cost, disassembly idle time and disassembly carbon emission (DCE). To further reduce the disassembly energy consumption and carbon emission, the well-accepted energy-saving measure, known as the turn on-off strategy, is also integrated. Subsequently, a hybrid multi-objective optimization algorithm called ALNS-NSGA II, which combines the NSGA-II algorithm and adaptive large-scale neighborhood search algorithm is developed to explore the optimal Pareto solution set. Finally, the novel behavioral decision model is proposed to select the optimal HRU-DLB scheme. The comparative analysis shows that the turn on-off strategy can reduce DEC by 26 % and DCE by 3.1 % in a cycle time, respectively. The computational results confirm the feasibility and effectiveness of the proposed ALNS-NSGA II in solving the HRU-DLBP. The comparative analysis and sensitivity analysis demonstrate that the proposed behavioral decision model has better ranking and classification effects.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 38-69"},"PeriodicalIF":12.2,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143509815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive genetic algorithm based on Q-learning for energy-efficient e-waste disassembly line balancing and rebalancing considering task failures 基于 Q-learning 的自适应遗传算法，用于考虑任务失败的高能效电子废物拆解线平衡和再平衡

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-26 DOI: 10.1016/j.jmsy.2025.02.009

Kaipu Wang , Xiaoyi Ma , Yibing Li , Yabo Luo , Yingli Li , Liang Gao

The efficient disassembly and recycling of e-waste not only provides economic benefits but also contributes to reducing energy consumption. However, the disassembly process is often influenced by uncertainties, such as damage or deformation of components, which may result in potential task failures. These failures can disrupt the balance of the disassembly line, affecting the efficiency of subsequent tasks. Therefore, it is crucial to develop a decision-making model and optimization method to address disassembly failures. This study presents a predictive disassembly line balancing model with objectives focused on the number of workstations, the smoothness index, and energy consumption. The optimization objective of adjusting the disassembly sequence is introduced, and a rebalancing model is developed to reallocate the remaining tasks in response to various failures. The sequence combination that minimizes comprehensive energy consumption is selected as the optimal disassembly strategy. Considering the complexity and dynamic disturbance of the problem, an adaptive multi-objective genetic algorithm based on Q-learning is proposed. To improve the quality of the disassembly solutions, six evolutionary actions and four population performance states are designed. During the algorithm’s iteration, the search strategy is dynamically adjusted through Q-learning. The effectiveness of the proposed algorithm is verified by solving several classic disassembly cases and comparing the results with those from six advanced algorithms. Finally, in an actual refrigerator disassembly case, 11 disassembly schemes are generated, accounting for task failures. The results indicate that, compared to traditional disassembly methods, the rebalancing approach not only optimizes the station loads but also increases revenue by 11.98 %, demonstrating the effectiveness of the proposed model and method in handling task failures on disassembly lines.

{"title":"An adaptive genetic algorithm based on Q-learning for energy-efficient e-waste disassembly line balancing and rebalancing considering task failures","authors":"Kaipu Wang , Xiaoyi Ma , Yibing Li , Yabo Luo , Yingli Li , Liang Gao","doi":"10.1016/j.jmsy.2025.02.009","DOIUrl":"10.1016/j.jmsy.2025.02.009","url":null,"abstract":"<div><div>The efficient disassembly and recycling of e-waste not only provides economic benefits but also contributes to reducing energy consumption. However, the disassembly process is often influenced by uncertainties, such as damage or deformation of components, which may result in potential task failures. These failures can disrupt the balance of the disassembly line, affecting the efficiency of subsequent tasks. Therefore, it is crucial to develop a decision-making model and optimization method to address disassembly failures. This study presents a predictive disassembly line balancing model with objectives focused on the number of workstations, the smoothness index, and energy consumption. The optimization objective of adjusting the disassembly sequence is introduced, and a rebalancing model is developed to reallocate the remaining tasks in response to various failures. The sequence combination that minimizes comprehensive energy consumption is selected as the optimal disassembly strategy. Considering the complexity and dynamic disturbance of the problem, an adaptive multi-objective genetic algorithm based on Q-learning is proposed. To improve the quality of the disassembly solutions, six evolutionary actions and four population performance states are designed. During the algorithm’s iteration, the search strategy is dynamically adjusted through Q-learning. The effectiveness of the proposed algorithm is verified by solving several classic disassembly cases and comparing the results with those from six advanced algorithms. Finally, in an actual refrigerator disassembly case, 11 disassembly schemes are generated, accounting for task failures. The results indicate that, compared to traditional disassembly methods, the rebalancing approach not only optimizes the station loads but also increases revenue by 11.98 %, demonstrating the effectiveness of the proposed model and method in handling task failures on disassembly lines.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 1-19"},"PeriodicalIF":12.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rescheduling human-robot collaboration tasks under dynamic disassembly scenarios: An MLLM-KG collaboratively enabled approach

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-25 DOI: 10.1016/j.jmsy.2025.02.015

Weigang Yu, Jianhao Lv, Weibin Zhuang, Xinyu Pan, Sijie Wen, Jinsong Bao, Xinyu Li

During product recycling, the uncertainty of the degradation level of end-of-life products leads to dynamic conditions such as component corrosion and damage during the disassembly process. Therefore, enhancing the robot's perception of disassembly scenarios and matching historical disassembly experiences is crucial for task rescheduling in human-robot collaborative disassembly (HRCD) under dynamic conditions. To address this, this paper proposes a dynamic task rescheduling method for human-robot collaborative disassembly, empowered by the synergy of Knowledge Graph (KG) and Multimodal Large Language Model (MLLM). Leveraging a Mark-Aware image preprocessing module and prompt-based scene understanding, the physical characteristics and occlusion relationships of disassembly targets are extracted. The concept of affordance is introduced, and an Affordance KG is constructed to recommend disassembly actions based on the physical features of objects in the scene. A task allocation standard for human-robot collaboration is designed, which, combined with depth and human factor information from mixed reality scenarios, enables dynamic task rescheduling and reconstruction of the entire human-robot collaborative disassembly process. The proposed method is validated through a case study on human-robot collaborative disassembly of end-of-life automotive lithium-ion batteries. Experimental results demonstrate that the method exhibits strong robustness and generalizability in dynamic disassembly scenarios, accurately identifying the physical features of components and recommending appropriate disassembly actions under conditions such as component corrosion, damage, and tool unavailability, thus achieving effective task rescheduling.

{"title":"Rescheduling human-robot collaboration tasks under dynamic disassembly scenarios: An MLLM-KG collaboratively enabled approach","authors":"Weigang Yu, Jianhao Lv, Weibin Zhuang, Xinyu Pan, Sijie Wen, Jinsong Bao, Xinyu Li","doi":"10.1016/j.jmsy.2025.02.015","DOIUrl":"10.1016/j.jmsy.2025.02.015","url":null,"abstract":"<div><div>During product recycling, the uncertainty of the degradation level of end-of-life products leads to dynamic conditions such as component corrosion and damage during the disassembly process. Therefore, enhancing the robot's perception of disassembly scenarios and matching historical disassembly experiences is crucial for task rescheduling in human-robot collaborative disassembly (HRCD) under dynamic conditions. To address this, this paper proposes a dynamic task rescheduling method for human-robot collaborative disassembly, empowered by the synergy of Knowledge Graph (KG) and Multimodal Large Language Model (MLLM). Leveraging a Mark-Aware image preprocessing module and prompt-based scene understanding, the physical characteristics and occlusion relationships of disassembly targets are extracted. The concept of affordance is introduced, and an Affordance KG is constructed to recommend disassembly actions based on the physical features of objects in the scene. A task allocation standard for human-robot collaboration is designed, which, combined with depth and human factor information from mixed reality scenarios, enables dynamic task rescheduling and reconstruction of the entire human-robot collaborative disassembly process. The proposed method is validated through a case study on human-robot collaborative disassembly of end-of-life automotive lithium-ion batteries. Experimental results demonstrate that the method exhibits strong robustness and generalizability in dynamic disassembly scenarios, accurately identifying the physical features of components and recommending appropriate disassembly actions under conditions such as component corrosion, damage, and tool unavailability, thus achieving effective task rescheduling.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 20-37"},"PeriodicalIF":12.2,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143487681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian neural networks for predicting quality in reclaimed waste sand for foundry applications

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-19 DOI: 10.1016/j.jmsy.2025.02.007

Boyeon Kim , Wonjong Jung , Youngsim Choi , Jeongsu Lee

Although advancements in smart manufacturing technologies have profoundly transformed the manufacturing industry, their application in traditional industries remains challenging. In particular, the casting industry faces significant obstacles, such as limited quality data acquisition for quantifying tacit knowledge and insufficient adoption of smart manufacturing technologies. As a potential remedy, this study demonstrates the application of smart manufacturing technologies for predicting the quality of reclaimed sand, specifically tailored for the sand casting industry. The developed strategy integrates: 1) detailed measurements of the environmental conditions in the sand reclamation process, and 2) a deep-learning-based model for predicting the loss on ignition (LOI) of reclaimed sand as a quality measure. The model is constructed using feature extraction from time-series data and Bayesian neural networks to predict LOI with quantified uncertainty. We propose a normality score-based reclaimed sand management strategy, which was evaluated over one and a half years of production conditions and reclaimed sand quality monitoring experiments. The demonstration case exhibits an average accuracy of 96.83 % in detecting problematic sand quality. Notably, the method significantly improved failure detection accuracy, increasing test data results from 38.34 % without uncertainty consideration to 72.5 % when uncertainty was incorporated. The proposed approach has the potential to advance the casting industry by enabling quality-data-driven management of the sand reclamation process, ultimately reducing defect rates and optimizing production costs.

{"title":"Bayesian neural networks for predicting quality in reclaimed waste sand for foundry applications","authors":"Boyeon Kim , Wonjong Jung , Youngsim Choi , Jeongsu Lee","doi":"10.1016/j.jmsy.2025.02.007","DOIUrl":"10.1016/j.jmsy.2025.02.007","url":null,"abstract":"<div><div>Although advancements in smart manufacturing technologies have profoundly transformed the manufacturing industry, their application in traditional industries remains challenging. In particular, the casting industry faces significant obstacles, such as limited quality data acquisition for quantifying tacit knowledge and insufficient adoption of smart manufacturing technologies. As a potential remedy, this study demonstrates the application of smart manufacturing technologies for predicting the quality of reclaimed sand, specifically tailored for the sand casting industry. The developed strategy integrates: 1) detailed measurements of the environmental conditions in the sand reclamation process, and 2) a deep-learning-based model for predicting the loss on ignition (LOI) of reclaimed sand as a quality measure. The model is constructed using feature extraction from time-series data and Bayesian neural networks to predict LOI with quantified uncertainty. We propose a normality score-based reclaimed sand management strategy, which was evaluated over one and a half years of production conditions and reclaimed sand quality monitoring experiments. The demonstration case exhibits an average accuracy of 96.83 % in detecting problematic sand quality. Notably, the method significantly improved failure detection accuracy, increasing test data results from 38.34 % without uncertainty consideration to 72.5 % when uncertainty was incorporated. The proposed approach has the potential to advance the casting industry by enabling quality-data-driven management of the sand reclamation process, ultimately reducing defect rates and optimizing production costs.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 584-597"},"PeriodicalIF":12.2,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient manufacturing: A review of disruptions, assessment, and pathways

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-19 DOI: 10.1016/j.jmsy.2025.02.006

Jiewu Leng , Junxing Xie , Rongjie Li , Xueliang Zhou , Xi Gu , Qiang Liu , Xin Chen , Wenjun Zhang , Andrew Kusiak

Manufacturers are increasingly concerned with the resilience of manufacturing systems due to rising disruptions. This paper reviews the research on resilient manufacturing, emphasizing the definitions and drivers of resiliency in manufacturing. Typical disruptions affecting manufacturing are discussed and classified. The assessment of resilience is explored through three key pillars: absorbency, adaptability, and recoverability. Resilience is also benchmarked against other manufacturing performance indicators. Pathways to resilient manufacturing are outlined, focusing on system design, configuration, and operations. Practical implementations are discussed, along with challenges and research directions aligned with Industry 5.0. This study aims to establish a foundational framework for integrating resiliency into modern manufacturing systems.

引用次数: 0

Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part II): Design processes and enablers

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-19 DOI: 10.1016/j.jmsy.2025.02.005

Jiewu Leng , Jiwei Guo , Junxing Xie , Xueliang Zhou , Ang Liu , Xi Gu , Dimitris Mourtzis , Qinglin Qi , Qiang Liu , Weiming Shen , Lihui Wang

Following up on our previous review paper ‘Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part I): Design thinking and modeling methods’ ^[1], based on the proposed Thinking-Modelling-Process-Enabler (TMPE) framework of Manufacturing System Design (MSD), this paper (Part II of the two-part review) further reviews the Process and Enabler dimensions of MSD in the interplay of Industry 4.0 and Industry 5.0. MSD methods are reviewed from the single-dimensional design process and cross-dimensional design process perspectives, respectively. MSD methods are reorganized and categorized from the key enabler's perspective. Finally, challenges are discussed along with directions for future research in the domain of MSD. This review is anticipated to offer novel insights for advancing MSD research and engineering in the interplay of Industry 4.0 and Industry 5.0.

引用次数: 0

Why decision support systems are needed for addressing the theory-practice gap in assembly line balancing

IF 12.2 1区工程技术 Q1 ENGINEERING, INDUSTRIAL

Journal of Manufacturing Systems

Pub Date : 2025-02-16 DOI: 10.1016/j.jmsy.2025.01.019

Christoffer Fink, Ulf Bodin, Olov Schelén

The efficiency of an assembly line depends on how the work is distributed along the line. This is known as the Assembly Line Balancing Problem, an NP-hard optimization problem. Automatic solvers for this problem have been studied for decades but have not been widely adopted in the industry, resulting in a theory-practice gap. The typical automation approach assumes that all constraints and objectives are known and can be statically defined ahead of time such that solvers with a precisely defined objective function can take a fully specified problem instance as input and produce a (near) optimal solution as output. In some industries, meeting these assumptions is particularly challenging because of properties such as mixed-model production with high model variance, multi-manned stations, large task graphs, etc. This paper explains why, in certain industries, such as automotive end assembly, complete automation is likely infeasible in practice due to challenges in modeling the problem, collecting data, and specifying the objective function. Manual intervention by an engineer as a decision-maker is therefore unavoidable. We argue that maximizing automation, by helping the decision-maker be as effective as possible, requires a decision support system (DSS) that supports an interactive and iterative workflow, thereby enabling assisted planning. Furthermore, we identify solver features that become relevant in the DSS context, thus making the case that focusing on standalone solvers, and treating the integration into a DSS as an implementation detail, is not a viable option. We conclude that decision support systems play a central role in closing the theory-practice gap.

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