Journal of Manufacturing Systems最新文献

{"title":"An Integrated Mathematical Programming and Reinforcement Learning Algorithm for the Flexible Job Shop Scheduling with Variable Lot-sizing","authors":"Chuanzhao Yu,&nbsp;Chunjiang Zhang,&nbsp;Jiaxin Fan,&nbsp;Weiming Shen","doi":"10.1016/j.jmsy.2025.05.002","DOIUrl":"10.1016/j.jmsy.2025.05.002","url":null,"abstract":"<div><div>The Flexible Job Shop Scheduling Problem with Variable Lot-Sizing (FJSP-VLS) extends the Flexible Job shop Scheduling Problem (FJSP) by permitting variable lot-sizing for jobs of the same type across different operations. This approach provides enhanced flexibility compared to the conventional method of consistent lot-sizing. However, existing algorithms face efficiency bottlenecks when scaling to real-world production scenarios. To address this challenge, the authors propose an Integrated Mathematical Programming and Reinforcement Learning (IMPRL) algorithm that synergistically combines a dual-attention neural network with Proximal Policy Optimization (PPO) for adaptive scheduling, coupled with a Mixed Integer Linear Programming (MILP) model for joint lot-sizing and machine optimization. Extensive experiments on 10 benchmark-derived instance classes demonstrate IMPRL’s superiority: it reduces makespan by 6.86% (up to 11.87% for 30<span><math><mo>×</mo></math></span> 10 instances) compared to TOP PDR, achieves 9.81% improvement in generalization tests, and maintains solution quality while being an order-of-magnitude faster than MILP and GA-MH<span><math><msub><mrow></mrow><mrow><mtext>ER</mtext></mrow></msub></math></span> approaches. The algorithm’s hierarchical architecture effectively resolves inconsistencies in sublot completion times, while the case study fully demonstrates its practicality in large-scale FJSP-VLS implementations. The key managerial insights derived from the research findings are also highlighted, along with an acknowledgment of the algorithm’s limitations.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 210-223"},"PeriodicalIF":12.2,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279529","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":"Human-in-the-loop in smart manufacturing (H-SM): A review and perspective","authors":"Duck Bong Kim ,&nbsp;Mahdi Sadeqi Bajestani ,&nbsp;Ju Yeon Lee ,&nbsp;Seung-Jun Shin ,&nbsp;Goo-Young Kim ,&nbsp;Seyed Mohammad Mehdi Sajadieh ,&nbsp;Sangdo Noh","doi":"10.1016/j.jmsy.2025.05.020","DOIUrl":"10.1016/j.jmsy.2025.05.020","url":null,"abstract":"<div><div>Smart manufacturing, also known as Industry 4.0, is a manufacturing paradigm that aims to realize autonomous processes, minimizing human involvement. In the advent of manufacturing-unfriendly situations (e.g., pandemics), it has been learned that the paradigm does not work correctly and has limitations in handling those situations. There is a consensus that humans still play a crucial role in manufacturing, and the ultimate goal of manufacturing is to benefit them. To align with this, the European Commission introduced Industry 5.0, targeting human centricity, sustainability, and resilience. Operator 5.0 has also been presented to improve the physical and cognitive capabilities of shop operators. In contrast, the new concept of human-in-the-loop in smart manufacturing (H-SM), aiming for the involvement of diverse stakeholders, has been recently proposed. In this paper, we introduce the research methodology to elaborate on the current application fields of the H-SM concept. For this, we revisit the existing paradigms and their case studies. Also, we categorize them in terms of different components in H-SM and with respect to different levels of physical and cognitive capabilities and experiences. Then, we identify seven technology clusters and twenty-one key-enabling technologies for the H-SM implementation. It can be concluded the H-SM is well-aligned with human-intervened autonomous manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 178-199"},"PeriodicalIF":12.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271191","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":"Inverse design of ideal pre-stress distribution in assembly interface based on service performance","authors":"Qiyin Lin ,&nbsp;Kaiyi Zhou ,&nbsp;Mingjun Qiu ,&nbsp;Tao Wang ,&nbsp;Hao Guan ,&nbsp;Lifei Chen ,&nbsp;Chen Wang ,&nbsp;Jian Zhuang ,&nbsp;Jun Hong","doi":"10.1016/j.jmsy.2025.06.012","DOIUrl":"10.1016/j.jmsy.2025.06.012","url":null,"abstract":"<div><div>This paper proposes an inverse design method (IPIDM) that integrates deep learning with FEM for assembly interfaces under extreme service conditions. IPIDM provides a universal framework to inversely derive the contact stress distribution at the assembly stage (referred to as the ideal pre-stress distribution) from a uniform stress distribution on the assembly interfaces during the service state. This distribution is designed to ensure uniform contact stress under extreme service conditions. Concurrently, IPIDM predicts morphology layouts to guide manufacturing. In IPIDM, the displacement of mesh nodes is utilized to extract the mapping relationship between interface morphology and stress. A U-Net-based deep learning network is developed and trained on this mapping model to simultaneously output the static contact stress distribution and the corresponding morphology layout. Compared with open-source neural networks, the proposed model demonstrates superior capabilities in global feature extraction and training efficiency. The training stability and predictive accuracy of IPIDM are verified, and the optimization effects of the predicted morphology layouts are verified through FEM. Results indicate that IPIDM significantly outperforms mainstream assembly interfaces optimization methods in optimization efficiency, particularly for 3D interfaces subjected to complex stress states. This makes IPIDM a promising tool for fast FEM simulations and digital twin applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 200-209"},"PeriodicalIF":12.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271192","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":"Process failure mode – product failure mechanism- effect analysis ((PFM)²EA): A novel risk assessment methodology for automated battery disassembly - Integrating process and product safety in repurposing","authors":"Stefan Grollitsch ,&nbsp;Gernot Schlögl ,&nbsp;Florian Feist ,&nbsp;Franz Haas ,&nbsp;Sinisa Jovic ,&nbsp;Harald Sehrschön ,&nbsp;Christian Ellersdorfer","doi":"10.1016/j.jmsy.2025.06.006","DOIUrl":"10.1016/j.jmsy.2025.06.006","url":null,"abstract":"<div><div>The increasing adoption of electric vehicles has led to a surge in end-of-life traction batteries, necessitating safe and efficient repurposing strategies. This study introduces a novel risk assessment methodology, the process failure mode - product failure mechanism - effect analysis ((PFM)²EA), designed to evaluate safety risks in automated battery disassembly processes. The (PFM)²EA method combines two established risk analysis approaches: one focused on manufacturing processes (process failure mode and effects analysis - PFMEA) and another on product failure behaviors (failure modes, mechanisms, and effects analysis - FMMEA). By linking these perspectives, the method addresses the critical gap between process and product risks in separation processes for battery repurposing. Our approach employs a tripartite risk categorization framework, distinguishing between immediate safety hazards, long-term safety risks, and potential performance issues of reused components. The method introduces a fourth variable to the traditional scoring system, which considers severity, likelihood of occurrence, and detectability of a product failure, by adding a fourth factor: the likelihood of process failure. The determination of which was simplified by implementing an analytic hierarchy process. This enhancement allows for a more comprehensive assessment of potential hazards originating from product failure mechanisms triggered by process faults. To validate the (PFM)²EA method, a preemptive risk assessment of theoretical automated disassembly processes for three commercially available battery systems has been conducted. The study focused on processes aimed at extracting energy storage components for reuse and repurposing, examining how safety considerations influence process selection. The findings demonstrate the effectiveness of the (PFM)²EA method in identifying and prioritizing safety risks in battery disassembly processes. A Monte Carlo Simulation confirmed the robustness of the risk evaluations under input uncertainty, reinforcing the method’s reliability. This research contributes to the development of safer and more efficient battery repurposing strategies, addressing critical challenges in the circular economy of energy storage systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 137-160"},"PeriodicalIF":12.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263141","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":"ConfigRec: An efficient recommendatory configuration design method for customized products","authors":"Zhiwei Pan ,&nbsp;Zili Wang ,&nbsp;Lemiao Qiu ,&nbsp;Shuyou Zhang ,&nbsp;Hong Zhu ,&nbsp;Huang Zhang ,&nbsp;Feifan Xiang ,&nbsp;Changlong Cheng","doi":"10.1016/j.jmsy.2025.06.013","DOIUrl":"10.1016/j.jmsy.2025.06.013","url":null,"abstract":"<div><div>Product configuration design is essential in mass customization and enables the rapid selection of configurable components to assemble a desired product. However, existing configuration methods struggle to balance customization flexibility with production efficiency. The configurators process multiple components and orders sequentially, leading to extended computation times. Additionally, component coupling relationships introduce extra costs and complexity. To address these challenges, we propose ConfigRec, an end-to-end recommendatory configuration design method that leverages the parallel computing capabilities of deep learning. Specifically, our approach: (1) constructs specialized parameter embeddings for components by encoding diverse design parameters; (2) decouples complex relationships within the product configuration tree through top-down and bottom-up message passing, while capturing implicit dependencies using a linear attention mechanism; and (3) predicts instance recommendation scores and generates a customized Engineering Bill of Materials based on a formally defined configuration decision law. A real-world case study on elevator products demonstrates that ConfigRec achieves up to 99.51 % accuracy within seconds. The proposed method is interpretable, efficient, and highly accurate, significantly reducing customized product delivery times.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 161-177"},"PeriodicalIF":12.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271190","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 frequency mask and decoupling max-logit based XAI method to explain DNN for fault diagnosis","authors":"Junfei Du,&nbsp;Yiping Gao,&nbsp;Liang Gao,&nbsp;Xiuyu Li","doi":"10.1016/j.jmsy.2025.06.004","DOIUrl":"10.1016/j.jmsy.2025.06.004","url":null,"abstract":"<div><div>Recently, various deep neural network (DNN) models have been proposed for fault diagnosis. Owing to the black-box nature of the DNN, Diagnosis results are unexplainable. Therefore, explainable artificial intelligence (XAI) methods are required. However, it is difficult for existing XAI methods to separate fault and irrelevant features because the fault features are instantaneous. To address this issue, a frequency mask and decoupling max-logit-based XAI method (FM-Explainer) is proposed to explain the DNN for fault diagnosis. Because the fault features can be well represented in the frequency domain, the proposed method optimizes a mask on the frequency domain of the input to identify the fault features. In addition, to avoid unreliable explanations caused by out-of-distribution (OoD) data, a regularization is designed based on decoupling max-logit, and the spatial penalty is used, which ensures that no irrelevant features remain in the explanation. Extensive experiments are carried out to verify the effectiveness of the proposed method using five quantitative evaluation metrics: Insertion/Deletion, Sensitivity-N, and Degradation. The results show that the FM-Explainer outperforms existing methods, and explanations by the FM-Explainer are consistent with the fault characteristic frequency. This indicates that the FM-Explainer is effective in precisely identifying fault features.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 98-113"},"PeriodicalIF":12.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254010","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":"Energy flexible manufacturing systems: A systematic literature review, trends and challenges","authors":"Longyao Xu ,&nbsp;Peiji Liu ,&nbsp;Xu Wang ,&nbsp;Yifei Lin ,&nbsp;Yameng Shi ,&nbsp;Xi Huang ,&nbsp;Xi Vincent Wang","doi":"10.1016/j.jmsy.2025.06.010","DOIUrl":"10.1016/j.jmsy.2025.06.010","url":null,"abstract":"<div><div>Utilizing renewable energy nearby has become a critical measure for manufacturing to reduce carbon emissions and energy cost. To mitigate the impact of fluctuations in renewable energy on manufacturing, energy flexibility (EF) technology has become a key approach to balance energy supply and demand. Furthermore, with deepening application of EF technology in manufacturing systems, energy flexible manufacturing systems (EFMS) have gradually become a promising direction for the future development of manufacturing systems. However, since EFMS involves multiple fields, the current understanding of EFMS remains unclear. In this review, we provide a comprehensive overview of the advancements, trends and challenges in EF technology and EFMS. For this, this paper first summarizes the definitions and evaluation indicators of EF in different fields; Second, a quantitative analysis was carried out on 68 studies related to EF technology and EFMS from 5 aspects; Beyond the overview of literature, we identify the trends and challenges for EFMS. On the one hand, the trends of EFMS include low carbonization, digitalization, intelligentization, flexibility and clustering. On the other hand, identified challenges encompass aspects like energy-related data processing, energy flow modeling, prediction, regulation and system design. Through this work, it is hoped to provide comprehensive guidance for addressing common challenges in the development of EFMS from diverse research perspectives, while highlighting potential future research trends and challenges.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 83-97"},"PeriodicalIF":12.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254011","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":"Towards industry 5.0: A framework of reconfigurable matrix-structured manufacturing system","authors":"Miao Wang ,&nbsp;Yifei Tong ,&nbsp;Cunbo Zhuang ,&nbsp;Xiaodong Du","doi":"10.1016/j.jmsy.2025.06.002","DOIUrl":"10.1016/j.jmsy.2025.06.002","url":null,"abstract":"<div><div>Current manufacturing faces increasingly volatile supply chains and diverse customer demands, requiring a balance between flexibility and efficiency. The matrix-structured manufacturing system (MMS) is a promising solution to frequent disruptions, yet existing research lacks a comprehensive framework for real-world implementation. This study proposes a reconfigurable matrix-structured manufacturing system (RMMS) that integrates advanced Industry 4.0 (I4.0) digital tools with Industry 5.0 (I5.0) visions of resilience and human–centricity. By introducing a “configuration” concept, RMMS organizes logical production lines to address the randomness and uncertainty of workflow typically seen in classic MMS, enabling efficient multi-variety, variable-batch production. Building on this foundation, we present the conceptual and systematic architecture of RMMS. Besides, a feasible technology roadmap is given, including order feature analysis, cell formation method and multidimensional reconfiguration mechanism. To validate our approach, we demonstrate the simulation comparisons and software implementation of RMMS through a case study in a high-precision electronics technology company, which shows improved resilience and rapid changeovers. Overall, the results indicate that RMMS provides a practical blueprint for human-centric, resilient manufacturing in dynamic market conditions, effectively bridging the gaps in current MMS implementations and advancing the evolving I5.0 landscape.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 114-136"},"PeriodicalIF":12.2,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263138","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":"Physics-informed orthogonal network with hierarchical time-frequency feature refining strategy for tool wear recognition","authors":"Yujun Zhou,&nbsp;Tangbin Xia,&nbsp;Rourou Li,&nbsp;Yuhui Xu,&nbsp;Guojin Si,&nbsp;Lifeng Xi","doi":"10.1016/j.jmsy.2025.06.009","DOIUrl":"10.1016/j.jmsy.2025.06.009","url":null,"abstract":"<div><div>Tool wear recognition is critical to improve the safety and reliability of machining operations with real-time tool status assessment. Conventional deep learning-based (DL-based) recognition approaches map time-frequency representations (TFRs) of the monitoring signals to tool wear with neural networks. However, data-driven mapping suffers from hardships in excavating wear-sensitive information due to the lack of explicit constraints on wear mechanisms, resulting in inferior recognition performance and recognition results against physical laws. To address this issue, this paper develops a time-frequency refining physics-informed orthogonal network (TFRPION). Firstly, a hierarchical time-frequency refining strategy consisting of energy concentration and adaptive amplitude modulation is conducted to emphasize machining dynamics-related characteristic frequency components in TFRs, highlighting wear-sensitive signal features. Secondly, an orthogonal network module maps features from the refined TFRs by capturing temporal amplitude variations within characteristic frequency components, improving the physical representational capability for the TFR mapping process. Thirdly, a physical network module and a modified loss function that take wear mechanisms into account are integrated to regularize the calculation path and optimization of the proposed network, enhancing the physical consistency between its mapping process and wear mechanisms. The feasibility and effectiveness of the proposed network are verified with collected spindle current signals in high-speed milling tool wear recognition experiments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 60-82"},"PeriodicalIF":12.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254012","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":"Comprehensive issue identification for manufacturing data analytics implementation: Systematic literature review and case studies","authors":"Sa-Eun Park ,&nbsp;Sang-Jae Lee ,&nbsp;Hyerim Bae ,&nbsp;Ki-Hun Kim ,&nbsp;Eung-Jun Kang ,&nbsp;Jae-Sung Kim ,&nbsp;Yong-Min Park ,&nbsp;Min-Ji Park","doi":"10.1016/j.jmsy.2025.05.006","DOIUrl":"10.1016/j.jmsy.2025.05.006","url":null,"abstract":"<div><div>Manufacturers are increasingly adopting manufacturing data analytics (MDA) as a key factor for smart manufacturing. However, successful MDA implementation remains limited due to various issues. Existing studies have barely suggested a comprehensive such issues by focusing on parts of technological, organizational, and environmental (TOE) contexts or issues limited to partial steps of MDA. This study addresses these gaps by identifying comprehensive issue set for MDA implementation (CISM) through a systematic review of 35 papers. The 29 distinct issues with 9 categories were derived to cover both TOE contexts and the five major steps of MDA. The comprehensiveness of CISM was validated through three real-world MDA implementation case studies. CISM is expected to suggest issues for manufacturers to address proactively in MDA implementation and to serve as a basis for stimulating future studies on MDA implementation.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"82 ","pages":"Pages 42-59"},"PeriodicalIF":12.2,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144243209","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}