Journal of Manufacturing Systems最新文献

{"title":"An order postponement strategy for multi-stage production-logistics synchronization towards zero-warehousing smart manufacturing","authors":"Mingxing Li ,&nbsp;Xiaoyu Qian ,&nbsp;Ming Li ,&nbsp;Ting Qu ,&nbsp;Zhen He","doi":"10.1016/j.jmsy.2025.06.024","DOIUrl":"10.1016/j.jmsy.2025.06.024","url":null,"abstract":"<div><div>Nowadays, driven by market dynamics and evolving consumer demands, customized production has emerged as a prevalent trend. This paradigm shift from mass production to customization introduces challenges in production-logistics management, compelling manufacturers to pursue efficient strategies. Zero-warehousing smart manufacturing (ZWSM), an advanced form of Lean manufacturing (LM) and Just-In-Time production (JIT), presents a potential solution to these challenges. ZWSM leverages Industry 4.0 technologies to facilitate seamless production-logistics for eliminating warehouses and minimizing inventory in workshop. Despite the encouraging visions, field study reveals that ZWSM requires highly coordinated material supply, production, and delivery operations, misalignment among these stages frequently results in operational inefficiencies and resource waste, especially when confronted with volatile and diversified customer demand. It is defined as Multi-Stage Production-Logistics Synchronization (MS-PLSync) problem. This study proposes a novel order postponement strategy for MS-PLSync towards ZWSM, a generalizable MS-PLSync model under assemble-to-order (ATO) is formulated using mixed-integer linear programming for production-logistics operations under intricate spatiotemporal constraints. Considering dynamic order arrivals, a postponement strategy is designed and integrated into MS-PLSync model to enhance overall operational efficiency through postponed real-time decision-making, achieving a balance between rapid response to demand fluctuations and economy of scale in customized production-logistics. Numerical analysis validates the effectiveness of the proposed postponement strategy in addressing MS-PLSync problem. Notably, a little postponement can yield substantial operational benefits, while excessive postponement only generates minimal marginal benefits. Furthermore, sensitivity analysis reveals that the proposed postponement strategy performs particularly well in mass customization scenarios characterized by large-scale orders, diverse product portfolios, and extensive distribution networks.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 389-404"},"PeriodicalIF":12.2,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518900","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}

{"title":"Skeleton-based action recognition for manufacturing assembly task through graph convolution network","authors":"Maryam Soleymani ,&nbsp;Mahdi Bonyani ,&nbsp;Chao Wang","doi":"10.1016/j.jmsy.2025.06.019","DOIUrl":"10.1016/j.jmsy.2025.06.019","url":null,"abstract":"<div><div>In modern manufacturing, human participation in assembly processes is essential, despite advancements in automation. However, accurately recognizing human actions in these environments presents challenges due to complex spatial–temporal dependencies and dynamic joint relationships. Graph Convolution Networks (GCNs) are utilized widely for action recognition, but they have poor accuracy for modeling long-range node correlations. Also, current GCNs have limitations in extracting various features due to utilizing the same pattern extraction for all frames. To overcome these issues, this study presents a novel approach to skeleton-based action recognition for manufacturing tasks using a Dual-Attention Graph Convolution Network (DAGCN). The proposed model integrates a Parallel Attention-Graph Mixer (PAGM) and Temporal–Spatial Attention Integrator (TSAI), enhancing the capture of both global and local joint relations and addressing the dynamic nature of skeletal joint relationships. Extensive evaluations on benchmark datasets, including HA4M that specifically designed for assembly tasks, NTU RGB+D, Northwestern-UCLA, and NTU RGB+D120, reveal the superior performance of DAGCN over state-of-the-art methods in terms of accuracy and computational efficiency. Experimental results demonstrate that DAGCN outperforms state-of-the-art methods, achieving a Top-1 accuracy of 89.0% on the HA4M dataset. The results validate DAGCN’s effectiveness in recognizing fine-grained human actions in industrial settings, contributing to improved efficiency and safety in human–robot collaboration. The proposed model offers a scalable and computationally efficient solution for intelligent assembly monitoring and automation in smart manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 362-375"},"PeriodicalIF":12.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513978","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}

{"title":"Enhancing sustainability of human-robot collaboration in industry 5.0: Context- and interaction-aware human motion prediction for proactive robot control","authors":"Guoyi Xia ,&nbsp;Zied Ghrairi ,&nbsp;Aaron Heuermann ,&nbsp;Klaus-Dieter Thoben","doi":"10.1016/j.jmsy.2025.06.022","DOIUrl":"10.1016/j.jmsy.2025.06.022","url":null,"abstract":"<div><div>Industry 5.0 (I5.0) marks a shift towards human-centric, sustainable, and resilient production systems, with Human-Robot Collaboration (HRC) contributing to these goals. Achieving sustainability of HRC, encompassing economic, environmental, and social dimensions, remains challenging to ensure safety, efficiency, and adaptability. Human Motion Prediction (HMP) can address these challenges by enabling robots to anticipate human actions and respond proactively. However, existing HMP studies often neglect to incorporate contextual and interaction-based information. The practical applicability of HMP in industrial settings requires further demonstration. Therefore, this study aims to apply context- and interaction-aware HMP to enhance sustainability of HRC in I5.0. A motion capture system collects human motion data, while a camera tracks object position as contextual information. Human-Object Interaction (HOI) is identified for HMP. A transformer model is applied for HMP based on integrated context and interaction data. Additionally, the applicability of HMP in industrial settings is demonstrated by a power transformer assembly. Two additional cases are applied for validation. Results show that object recognition achieved 98 % accuracy. The identified interaction periods are effective in enhancing HMP performance. HMP with context and interaction data achieves an Average Displacement Error (ADE) of 0.07 m and a Final Displacement Error (FDE) of 0.10 m. The demonstration results suggest that the HMP enabled proactive robot control, contributing to safer, more efficient, and adaptive production. The findings of this research contribute to enhancing the sustainability of HRC in I5.0, with potential benefits for environmental efficiency, worker safety, and productivity in industrial settings.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 376-388"},"PeriodicalIF":12.2,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144513979","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}

{"title":"Multi-robot collaborative manufacturing driven by digital twins: Advancements, challenges, and future directions","authors":"Gang Wang ,&nbsp;Cheng Zhang ,&nbsp;Sichao Liu ,&nbsp;Yongxuan Zhao ,&nbsp;Yingfeng Zhang ,&nbsp;Lihui Wang","doi":"10.1016/j.jmsy.2025.06.014","DOIUrl":"10.1016/j.jmsy.2025.06.014","url":null,"abstract":"<div><div>Multi-robot systems envisioned for future factories will promote advancements and capabilities of handling complex tasks and realising optimal robotic operations. However, existing multi-robot systems face challenges such as integration complexity, difficult coordination and control, low scalability, and flexibility, and thus are far from realising adaptive and efficient multi-robot collaborative manufacturing (MRCM). Digital twin technology improves visualisation, consistency, and spatial–temporal collaboration in MRCM through real-time interaction and iterative optimisation in physical and virtual spaces. Despite these improvements, barriers such as undeveloped modelling capabilities, indeterminate collaborative strategies, and limited applicability impede widespread integration of MRCM. In response to these needs, this study provides a comprehensive review of the foundational concepts, systematic architecture, and enabling technologies of digital twin-driven MRCM, serving as a prospective vision for future work in collaborative intelligent manufacturing. With the development of sensors and computational capabilities, robot intelligence is evolving towards multi-robot collaboration, including perceptual, cognitive, and behavioural collaboration. Digital twins play a critical supporting role in multi-robot collaboration, and the architecture, methodologies, and applications are elaborated across diverse stages of MRCM processes. This paper also identifies current challenges and future research directions. It encourages academic and industrial stakeholders to integrate state-of-the-art AI technologies more thoroughly into multi-robot digital twin systems for enhanced efficiency and reliability in production.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 333-361"},"PeriodicalIF":12.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481048","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}

{"title":"From cost to capability: Technology Multiplier in EV manufacturing strategy","authors":"Roman Girke ,&nbsp;Louis Schäfer ,&nbsp;Tanja Maier ,&nbsp;Florian Stamer ,&nbsp;Shun Yang ,&nbsp;Jung-Hoon Chun ,&nbsp;Gisela Lanza","doi":"10.1016/j.jmsy.2025.06.008","DOIUrl":"10.1016/j.jmsy.2025.06.008","url":null,"abstract":"<div><div>In an increasingly dynamic and uncertain global environment, companies face significant challenges in aligning production strategies with long-term competitiveness and innovation. Many firms focus on short-term cost savings through outsourcing and relocation, which can negatively impact internal knowledge, capabilities, and long-term growth. This study explores the strategic decision-making process of production segment allocation, emphasizing the need for a systematic and holistic approach. Using the Analytical Hierarchy Process (AHP), key decision factors are introduced, including cost, innovation capability, and social impact. The qualitative study is applied to the case of electric vehicle (EV) battery production, where in-house production, outsourcing, and joint ventures are evaluated. The results reveal key drivers such as design capabilities and customer centricity, highlighting the strategic importance of maintaining core technological capabilities in-house. The study provides a foundation for the concept of a ”Technology Multiplier” to quantify the long-term value of in-house production in terms of innovation, adaptability, and competitive advantage, offering a new framework for strategic manufacturing decisions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 319-332"},"PeriodicalIF":12.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144471582","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}

{"title":"A novel XR-based real-time machine interaction system for Industry 4.0: Usability evaluation in a learning factory","authors":"Kaveh Amouzgar ,&nbsp;Justus Willebrand","doi":"10.1016/j.jmsy.2025.05.019","DOIUrl":"10.1016/j.jmsy.2025.05.019","url":null,"abstract":"<div><div>Traditional methods of data visualization and process monitoring are increasingly inadequate in fast-paced, data-intensive manufacturing environments. Extended Reality (XR) technologies, including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), have the potential to enhance human–machine interaction and operational efficiency in Industry 4.0 framework. While previous research has demonstrated the effectiveness of XR in areas such as assembly, training, maintenance, and human–robot interaction, limited attention has been given to developing and evaluating XR systems for real-time machine data visualization. Most existing studies focus on demonstrating AR applications without rigorous comparative evaluations against other XR technologies or traditional Human–Machine Interfaces (HMIs), often with limited user testing. This study addresses these gaps by developing and evaluating an XR application using Microsoft HoloLens 2 for real-time process control in a Learning Factory environment. A mixed-methods approach, including experimental design, surveys, and time measurements, compared the XR system with conventional 2D HMIs. Data from 22 participants were analyzed, focusing on alarm response times, usability, and preventive maintenance. The findings show that the XR system significantly improves alarm response times, increases frequency of preventive refills, and enhances usability compared to traditional HMIs. However, challenges related to ergonomics and limited field of view were noted. This study contributes to advancing smart manufacturing by showcasing the potential of XR to improve human–machine interfaces and foster better interaction between machines and operators.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 254-283"},"PeriodicalIF":12.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331142","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}

{"title":"A mutual cross-attention fusion network for surface roughness prediction in robotic machining process using internal and external signals","authors":"Zhiqi Wang,&nbsp;Dong Gao,&nbsp;Yong Lu,&nbsp;Kenan Deng,&nbsp;Zhaojun Yuan,&nbsp;Minglong Huang,&nbsp;Tianci Jiang","doi":"10.1016/j.jmsy.2025.06.018","DOIUrl":"10.1016/j.jmsy.2025.06.018","url":null,"abstract":"<div><div>Compared with machine tools, industrial robots exhibit low, position-dependent stiffness. This dynamic compliance leads to inconsistent surface roughness under identical machining parameters when the robot configuration changes, thereby significantly complicating roughness prediction. Therefore, to address the challenge of predicting surface roughness in robotic machining processes and provide reference for its effective surface roughness monitoring, this paper proposes a Mutual Cross-attention Fusion Network (MCFN) for surface roughness prediction in robotic machining process using internal and external signals. Firstly, the machined surface roughness data set is obtained through the robotic machining experiments with different workpiece placements and machining parameters. The internal torque signals and external vibration signals of the robot are acquired to better reflect the state information during the machining process. Secondly, Uniform Manifold Approximation and Projection(UMAP) is used to reduce the dimension of time domain, frequency domain and time-frequency domain features extracted by signal channel to reduce the interference of redundant features. The features after dimension reduction are used to form a double-branch structure, and the dynamic interaction between different channels features is realized by Parallel Multi-channel Feature Enhancement Module(PMFEM). Then, the mutual fusion module based on the Dual Multi-head Cross-attention Mechanism(Dual-MCM) is used to realize the collaborative interaction of cross-modal information, to complete the bidirectional deep collaborative representation between the robot internal and external signals features in the fusion process. And the features are segmented and aggregated to predict the robot machined surface roughness. Finally, based on the performance evaluation index, the effectiveness of the MCFN is verified through hyperparameter adjustment, ablation experiment, comparison experiment of different dimension reduction techniques and data-driven methods. The verification results show that MCFN can realize the prediction of robot machined surface roughness at different postures and machining parameters, which provides an effective method for the accurate prediction and monitoring of robot machined surface roughness.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 284-300"},"PeriodicalIF":12.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331143","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}

{"title":"Toward digital twins for intelligence manufacturing: Self-adaptive control in assisted equipment through multi-sensor fusion smart tool real-time machine condition monitoring","authors":"Jin Zhang ,&nbsp;Chengchao Li ,&nbsp;Chenjie Deng ,&nbsp;Taimin Luo ,&nbsp;Ruihua Deng ,&nbsp;Daixin Luo ,&nbsp;Guibao Tao ,&nbsp;Huajun Cao","doi":"10.1016/j.jmsy.2025.06.020","DOIUrl":"10.1016/j.jmsy.2025.06.020","url":null,"abstract":"<div><div>Compared to traditional monitoring methods, multi-sensor fusion smart tool offers several advantages, including full-process monitoring and a broader range of applications (e.g., flat, curved, and complex surfaces). When integrated with artificial intelligence models for tool state monitoring, these tools provide strong generalization capabilities and high prediction accuracy. They can also adjust machine tool process parameters to extend tool life. However, the quasi-in-situ regulation of cutting parameters has a limited scope, making it challenging to achieve full working condition adaptability. The introduction of assisted equipment can enhance process adaptability. Furthermore, adaptive control mechanisms can regulate the machining process to reduce energy consumption by adjusting the opening and closing parameters. Despite these advantages, the linkage control mechanism for the smart tool remains unclear, and existing tool wear models struggle to adapt to variable working conditions across multiple scenarios. To address these challenges, this paper explores the digital twin modeling and application of smart tool machining processes. First, a digital twin-driven tool machining process model is developed, with an exploration of specific application scenarios and methods. Secondly, an adaptive coupling mechanism for assisted equipment based on digital twins is established, which simultaneously improves machining quality and reduces energy consumption. Additionally, the online tool wear identification model is enhanced to increase its generalization and reduce the cost of model reconstruction when working conditions change, thus enabling green intelligent manufacturing under high-quality machining conditions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 301-318"},"PeriodicalIF":12.2,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144331144","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}

{"title":"Matrix manufacturing system layout and scheduling via graph neural network and multi‐action deep reinforcement learning","authors":"Tong Zhu ,&nbsp;Xuemei Liu ,&nbsp;Yanbin Yu ,&nbsp;Ling Fu","doi":"10.1016/j.jmsy.2025.06.005","DOIUrl":"10.1016/j.jmsy.2025.06.005","url":null,"abstract":"<div><div>Matrix manufacturing system (MMS) is a novel production paradigm in Industry 4.0 that provides a highly flexible production environment based on the principles of decentralization, modularity, and unrestricted connectivity. MMS effectively addresses the challenges of personalized customization, which, in turn, imposes stricter demands on the optimization of its layout and scheduling. However, existing research on MMS primarily focuses on constructing theoretical frameworks, with limited attention to practical layout and scheduling optimization. Moreover, layout and scheduling decisions in MMS are highly coupled, and the system state has complex topological structures and dynamics. Conventional vector representation methods struggle to fully capture these intricate relationships, which limits the ability of MMS to address complex production demands. Therefore, to solve the MMS layout and scheduling (MMSLS) problem, this paper proposes an end-to-end multi-action deep reinforcement learning (MADRL) method based on a three-stage embedded heterogeneous graph neural network (HGNN) to learn the optimal policy for parallel decision-making for MMSLS, which aims to minimize makespan. Firstly, the traditional disjunctive graph of flexible scheduling problems is expanded into a heterogeneous graph by incorporating workstation and location nodes, which more intuitively captures the complex associations in MMS between operations and workstations and between workstations and locations. Secondly, we propose a novel HGNN algorithm to enhance representation learning by first transforming MMSLS heterogeneous graph into node-level embeddings and then using heterogeneous graph-level representation vectors as inputs. Finally, the agent sequentially performs actions based on two parameterized sub-policies, operation-workstation actions and location actions, which are trained to learn the optimal MMSLS policy using the proximal policy optimization (PPO) algorithm. Experimental results from both randomized and benchmark instances reveal that the proposed method not only outperforms manually crafted heuristic scheduling rules in solution quality but also exceeds metaheuristic algorithms in computational velocity. Furthermore, it demonstrates strong generalization when handling larger-scale instances.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 239-253"},"PeriodicalIF":12.2,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313218","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}

{"title":"Q-learning-based multi-objective spotted hyena algorithm for flexible open shop scheduling problem with consideration of preventive maintenance and travel/setup times","authors":"Jun Guo ,&nbsp;Bin Peng ,&nbsp;Baigang Du ,&nbsp;Kaipu Wang ,&nbsp;Yibing Li","doi":"10.1016/j.jmsy.2025.06.001","DOIUrl":"10.1016/j.jmsy.2025.06.001","url":null,"abstract":"<div><div>This paper presents a flexible open shop scheduling problem considering preventive maintenance, travel time between machines, and sequence-dependent setup time (FOSSP-PM&amp;TT) to address the impact of routine maintenance on shop productivity. According to the characteristics of the problem, a mathematical model is developed to simultaneously minimize the makespan and mean flow time. Then, a Q-learning-based multi-objective spotted hyena optimization algorithm (Q-MSHO) is proposed to solve this problem. Four neighborhood structures are designed in accordance with characteristics of the FOSSP-PM&amp;TT. And a Q-learning-based variable neighborhood search strategy is proposed to update the selection of local search operations in each iteration. Finally, computational experiments are performed on test instances of different sizes to evaluate the performance of the proposed algorithm. The experimental outcomes demonstrate that the Q-MSHO algorithm exhibits superior performance compared to the other algorithms in addressing the FOSSP-PM&amp;TT problem.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 224-238"},"PeriodicalIF":12.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290566","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}