Journal of Manufacturing Systems最新文献

{"title":"A knowledge-enhanced discrete artificial bee colony algorithm for flexible job shop scheduling problem with transport robots","authors":"Youjie Yao ,&nbsp;Kai Ye ,&nbsp;Chunjiang Zhang ,&nbsp;Xinyu Li ,&nbsp;Liang Gao","doi":"10.1016/j.jmsy.2025.12.023","DOIUrl":"10.1016/j.jmsy.2025.12.023","url":null,"abstract":"<div><div>The flexible job shop scheduling problem with transport robots (FJSPTR) has garnered significant attention due to the widespread integration of transport robots in manufacturing systems. Owing to its dual-resource scheduling characteristics, metaheuristic algorithms have become the dominant approach for solving the FJSPTR, as they can effectively handle its inherent complexity. However, existing algorithms often lack domain-specific knowledge, leading to suboptimal solution quality and limited robustness. To address these challenges, this study proposes a knowledge-enhanced discrete artificial bee colony (KEDABC) algorithm aimed at minimizing makespan. First, three tailored neighborhood structures are designed, and two feasibility theorems are introduced to ensure the validity of the generated neighborhood solutions. Second, these neighborhood structures are integrated with tabu search to form a knowledge-driven search approach, enabling a more comprehensive and detailed search of the neighborhoods. Third, to integrate domain knowledge into the discrete artificial bee colony algorithm, a novel task-based solution representation is proposed, which provides a complete mapping from the encoding to the solution space. In addition, a machine assignment-based local search strategy is developed to further improve algorithm performance. Finally, extensive experiments are conducted to benchmark the proposed algorithm against state-of-the-art methods using two benchmark datasets. The comparison results demonstrate that the KEDABC algorithm consistently achieves optimal or near-optimal solutions, outperforming other methods in both solution quality and computational efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 614-628"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879994","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":"Intention recognition and task allocation in human–robot collaborative assembly based on adaptive networks and DT belief space-GA","authors":"Jiawei He ,&nbsp;Chaoyang Zhang ,&nbsp;Yichen Wang ,&nbsp;Pai Zheng ,&nbsp;Juchen Zhang","doi":"10.1016/j.jmsy.2025.11.018","DOIUrl":"10.1016/j.jmsy.2025.11.018","url":null,"abstract":"<div><div>Human–robot collaboration (HRC) is pivotal in human-centered intelligent manufacturing, leveraging the complementary strengths of human adaptability and robotic efficiency to improve assembly quality and productivity. However, accurately recognizing human assembly intentions and optimizing task allocation remain key challenges. To address these issues, this study proposes an intention recognition-driven digital twin task allocation method, integrating an improved two-stream self-enhancing adaptive graph convolutional network (2S-SEAGCN) and a Belief Space genetic algorithm (BS-GA). First, a lightweight skeletal point detection model is developed to recognize human assembly intentions. Subsequently, the digital twin task allocation method applies the BS-GA algorithm to optimize resource allocation and enhance decision-making efficiency. The algorithm integrates a custom encoding scheme, belief space modeling, elitism, and local search mechanisms to avoid local optima, maintain optimization stability, and enhance global optimization. Finally, integrating reinforcement learning models to achieve end-to-end collaboration between robots and human operators, forming a closed-loop interactive assembly system. Experimental results demonstrate that 2S-SEAGCN outperforms traditional convolutional neural networks (CNN) and graph convolutional networks (GCN) in assembly intentions recognition, balancing accuracy and real-time performance. Moreover, the BS-GA algorithm achieves a higher average fitness than conventional genetic and heuristic algorithms in optimizing complex assembly tasks. In addition, reinforcement learning models demonstrate strong adaptability in dynamic and uncertain assembly scenarios. This closed-loop system provides practical solutions for real-world HRC applications, significantly improving production efficiency and quality, thereby demonstrating the substantial potential of industrial deployment.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 20-39"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145617340","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 comprehensive survey for real-world industrial surface defect detection: Challenges, approaches, and prospects","authors":"Yuqi Cheng ,&nbsp;Yunkang Cao ,&nbsp;Haiming Yao ,&nbsp;Wei Luo ,&nbsp;Cheng Jiang ,&nbsp;Hui Zhang ,&nbsp;Weiming Shen","doi":"10.1016/j.jmsy.2025.11.022","DOIUrl":"10.1016/j.jmsy.2025.11.022","url":null,"abstract":"<div><div>Industrial surface defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 152-172"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786611","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 deep reinforcement learning approach driven by temporal knowledge graph for dynamic scheduling in discrete manufacturing workshops","authors":"Xinyu Liu ,&nbsp;Yunlei Zan ,&nbsp;Honghui Wang ,&nbsp;Xu Han ,&nbsp;Guijie Liu","doi":"10.1016/j.jmsy.2025.12.008","DOIUrl":"10.1016/j.jmsy.2025.12.008","url":null,"abstract":"<div><div>Against the background of low-carbon and efficient sustainable manufacturing, production scheduling must not only focus on efficiency and delivery times but also balance energy consumption and carbon emission constraints. However, modern manufacturing workshops commonly adopt a model where batch production and trial production coexist. Production orders and equipment load evolve over time, with frequent occurrences of order insertion, maintenance. At the same time, workshop data exhibits multisource, heterogeneous, and time-varying characteristics. It leads to underutilized production information and delayed scheduling decisions, resulting in efficiency losses and redundant energy consumption. To address the above issues, a deep reinforcement learning approach driven by temporal knowledge graph is proposed in this paper. Firstly, a knowledge graph-based workshop scheduling framework is established to perform unified semantic modeling of production factors and the scheduling relationships. It constructs a multidimensional information matrix with temporal rule constraints. Then, a temporal knowledge-driven LSTM-TD3 algorithm is proposed by replacing the original policy network with an LSTM and employing dual Q-networks with policy delay updates to improve training convergence in continuous action spaces. On this basis, an adaptive weighting mechanism for reward functions targeting different production events is defined to achieve a reasonable balance and dynamic decision-making among multiple objectives. Finally, a case study is conducted with a boiler screen tube production line, and a prototype system is built. The results indicate that compared to historical production data, the average energy consumption of production line equipment decreased by 4.10 %, and the overall efficiency of the workshop increased by 14.29 %, validating the effectiveness of this approach.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 242-258"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786666","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":"Safe reinforcement learning with online filtering for fatigue-predictive human–robot task planning and allocation in production","authors":"Jintao Xue,&nbsp;Xiao Li,&nbsp;Nianmin Zhang","doi":"10.1016/j.jmsy.2025.12.019","DOIUrl":"10.1016/j.jmsy.2025.12.019","url":null,"abstract":"<div><div>Human–robot collaborative manufacturing, a core aspect of Industry 5.0, emphasizes ergonomics to enhance worker well-being. This paper addresses the dynamic human–robot task planning and allocation (HRTPA) problem, which involves determining when to perform tasks and who should execute them to maximize efficiency while ensuring workers’ physical fatigue remains within safe limits. The inclusion of fatigue constraints, combined with production dynamics, significantly increases the complexity of the HRTPA problem. Traditional fatigue–recovery models in HRTPA often rely on static, predefined hyperparameters. However, in practice, human fatigue sensitivity varies daily due to factors such as changed work conditions and insufficient sleep. To better capture this uncertainty, we treat fatigue-related parameters as inaccurate and estimate them online based on observed fatigue progression during production. To address these challenges, we propose PF-CD3Q, a safe reinforcement learning (safe RL) approach that integrates the <strong>p</strong>article <strong>f</strong>ilter with <strong>c</strong>onstrained <strong>d</strong>ueling <strong>d</strong>ouble <strong>d</strong>eep <strong>Q</strong>-learning for real-time fatigue-predictive HRTPA. Specifically, we first develop PF-based estimators to track human fatigue and update fatigue model parameters in real-time. These estimators are then integrated into CD3Q by making task-level fatigue predictions during decision-making and excluding tasks that exceed fatigue limits, thereby constraining the action space and formulating the problem as a constrained Markov decision process (CMDP). Experimental results demonstrate that our PF-based estimators achieve high prediction accuracy and strong noise robustness, and that PF-CD3Q outperforms other algorithms across multiple performance metrics, significantly reducing the occurrence of overwork and adapting to unseen fatigue constraints after training. These findings validate the effectiveness of our approach under complex and dynamic production conditions, supporting both human well-being and the development of a more sustainable and efficient manufacturing paradigm.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 561-583"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836451","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":"Tool wear condition diagnosis using ensemble learning with regularized fusion of domain knowledge and physical information for Ti-6Al-4V milling","authors":"Yan Xu ,&nbsp;Li Li ,&nbsp;Guanghui Lang ,&nbsp;Yang Luo ,&nbsp;Junhua Zhao ,&nbsp;Congbo Li","doi":"10.1016/j.jmsy.2025.12.010","DOIUrl":"10.1016/j.jmsy.2025.12.010","url":null,"abstract":"<div><div>Tool wear is a critical factor influencing the cost of machining products, especially in the milling of complex Ti-6Al-4V components, where tool wear is significantly accelerated. Therefore, it is essential to develop accurate methods for diagnosing tool wear. This paper proposes an ensemble learning model that integrates domain knowledge and physical information through regularized fusion for the diagnosis of tool wear conditions. Initially, a physics-based model was developed to relate milling forces to the width of tool flank wear (<em>VB</em>) by analyzing the characteristics of milling forces under conditions of progressive tool wear. Following this, a stacking ensemble framework was utilized to combine the selected base models. Domain-specific knowledge, encompassing different stages of tool wear, as well as physical information, including the force-<em>VB</em> relationship, were integrated through the formulation of the loss function. Moreover, a two-stage feature reduction methodology integrating Spearman's rank correlation analysis (Spearman's ρ) with Principal Component Analysis (PCA) was introduced to improve the relevance and compactness of the features. Subsequently, milling experiments were performed on a machining center to assess the effectiveness and practical applicability of the proposed approach.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 383-402"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786609","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 cradle-to-cradle life cycle assessment framework linking machining parameters, tool life and part durability","authors":"I. Rodriguez ,&nbsp;P.J. Arrazola ,&nbsp;M. Mori ,&nbsp;G. Ortiz-de-Zarate ,&nbsp;A. Madariaga ,&nbsp;M. Cuesta ,&nbsp;F. Pušavec","doi":"10.1016/j.jmsy.2025.11.021","DOIUrl":"10.1016/j.jmsy.2025.11.021","url":null,"abstract":"<div><div>Machining operations influence sustainability not only through immediate energy and resource use but also through their effects on tool longevity and part durability. Conventional life cycle assessments (LCAs) of machining typically adopt gate-to-gate boundaries, overlooking how machining parameters affect surface integrity, fatigue life, and the frequency of cutting-tool and machined component replacement. This study develops a novel cradle-to-cradle LCA framework that incorporates machining-induced tool wear and part durability into system-level environmental impacts. The approach is demonstrated through a real case study of drilling the CFRP/Ti6Al4V stacks used for the Boeing 787 fuselage, comparing dry drilling with liquid carbon dioxide combined with minimum quantity lubrication (LCO₂+MQL). Experimental tests quantified energy demand, resource consumption, tool life, and hole quality, while part durability was evaluated via fatigue testing and finite element simulations. Results show that the cutting tool production was the dominant contributor to the overall environmental impact. Despite requiring additional auxiliary inputs, LCO₂+MQL drilling reduced overall environmental impacts by 60–70 % relative to dry drilling, due to extended tool life and improved component service life. These findings demonstrate that coolant-assisted machining, when durability and tool-life effects are considered, yields a net environmental benefit. The proposed framework provides a transferable method to expand LCAs beyond gate-to-gate boundaries by integrating the influence of machined part quality on durability and in-service repairs, enabling cradle-to-cradle assessments that better capture the system-level implications of machining decisions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 207-222"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786668","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":"Agentic digital twin-embedded maintenance methodology for energy equipment: A self-evolving operational paradigm","authors":"Wang Cong,&nbsp;Wu Tao,&nbsp;Bao Jinsong","doi":"10.1016/j.jmsy.2025.11.020","DOIUrl":"10.1016/j.jmsy.2025.11.020","url":null,"abstract":"<div><div>Intelligent operation and maintenance of energy equipment represents a critical component in ensuring the stable performance of new-generation power systems. Faced with complex operational conditions and nonlinear fault characteristics, conventional manual maintenance suffers from delayed perception and ambiguous causality. However, while digital twin technology can establish a virtual-real interaction space, its static modeling approach exhibits prediction failure in dynamic scenarios. To address these challenges, this study proposes an intelligent maintenance methodology based on cognitive agents and virtual-real co-evolution: constructing a dynamic environment representation model to achieve spatiotemporal feature correlation of equipment states and operational condition migration; designing a memory-planning-decision architecture to enhance causal reasoning capabilities for equipment faults and integrating with digital twin models for virtual-real interaction. The methodology is validated through an 18-month case study of a gas-steam boiler in a combined heat and power plant, utilizing 5.2 million historical operational records. Experimental results demonstrate that this approach achieves a 97.3 % accuracy rate in diagnosing non-stationary faults for gas-steam boiler equipment, realizes a 20-fold improvement in knowledge update time (from 48 to 2.3 h), and attains significant performance enhancements including 31.2 % cost efficiency improvement, 3-fold early warning lead time extension (from 24 to 72 h), and 16.2 % overall collaborative performance improvement (from 82.2 % to 95.5 %). The research validates the engineering value of dynamic cognitive paradigms in intelligent maintenance of power equipment, providing a feasible solution for autonomous decision-making in high-real-time scenarios.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 100-116"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786761","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":"Predicting transfer times across production lines using data pooling","authors":"Seohyun Choi ,&nbsp;Young Ha Joo ,&nbsp;Hoonseok Park ,&nbsp;Younkook Kang ,&nbsp;Ri Choe ,&nbsp;Jae-Yoon Jung","doi":"10.1016/j.jmsy.2025.12.025","DOIUrl":"10.1016/j.jmsy.2025.12.025","url":null,"abstract":"<div><div>Reliable transfer time prediction is crucial for productivity in automated material handling systems within modern manufacturing environments. However, the complexity and dynamic behavior of manufacturing and logistics systems make accurate transfer time estimation highly challenging. This study proposes a transfer time prediction approach for automated material handling systems operating across production lines. To forecast inter-building and inter-floor transfer times, this study proposes a hybrid method, called time-series residual regression, that integrates linear time-series analysis with nonlinear machine learning. The framework further employs three data pooling strategies to effectively capture device heterogeneity and improve forecasting robustness. The hybrid method was validated using transfer records from inter-building stockers and inter-floor lifters in a Korean semiconductor fab. The experimental results show that the proposed model delivers superior performance, achieving R-squared values of 64.01 % for inter-building transfers and 72.00 % for inter-floor transfers.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 629-645"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145880098","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":"Aircraft assembly process planning based on knowledge graph constructed by integrating LLMs and SLMs","authors":"Yunfei Ma ,&nbsp;Shuai Zheng ,&nbsp;Zheng Yang ,&nbsp;Pai Zheng ,&nbsp;Jiewu Leng ,&nbsp;Jun Hong","doi":"10.1016/j.jmsy.2025.11.016","DOIUrl":"10.1016/j.jmsy.2025.11.016","url":null,"abstract":"<div><div>In commercial aircraft manufacturing, process planning serves as a crucial bridge between design and production, ensuring the accurate realization of design concepts and significantly improving manufacturing efficiency and product quality. With the development of knowledge graph technologies, significant progress has been made in using historical process documentation for commercial aircraft manufacturing process planning. However, traditional deep learning-based methods for constructing knowledge graph heavily rely on manual object selection and label assignment, making the process highly time-consuming. Additionally, the methods often face challenges in the field of process planning, including low domain-specific terminology recognition rates and incomplete entity extraction. To tackle these challenges, this paper introduces a hybrid approach that integrates large and small language models to construct an aircraft process planning knowledge graph. Initially, clustering-based multi-agent approach is employed to pre-annotate the process planning dataset, with domain experts re-annotate the defect data to create a high-quality process planning dataset. Subsequently, a knowledge extraction framework for aircraft process planning, KE-LSM, was constructed using the small language model trained on this dataset, together with the LLM. Experimental results show that KE-LSM outperforms existing named entity recognition models. Finally, KE-LSM is applied in a commercial aircraft manufacturing company, accompanied by the development of a prototype system designed to facilitate intelligent process planning. It is hoped that the research can provide valuable insights and support for the application of LLM-based solutions in the field of aircraft manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 1-19"},"PeriodicalIF":14.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145594700","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}