Journal of Manufacturing Systems最新文献

{"title":"A constraint programming-based lexicographic-Pareto approach for balancing two-sided robotic disassembly lines with 7-axis robots","authors":"Yu Zhang ,&nbsp;Zeqiang Zhang ,&nbsp;Feng Chu ,&nbsp;Yanqing Zeng ,&nbsp;Lei Guo ,&nbsp;Zongxing He","doi":"10.1016/j.jmsy.2025.09.006","DOIUrl":"10.1016/j.jmsy.2025.09.006","url":null,"abstract":"<div><div>Robotic disassembly lines play a pivotal role in remanufacturing by enabling automated operations. In two-sided disassembly scenarios involving large-scale products such as automobiles, their high load capacity significantly reduces the labor intensity of manual disassembly and eliminates the need for lifting equipment, thereby streamlining the process flow. When equipped with mobility systems, 7-axis robots can flexibly switch between multiple workstations, facilitating both rapid adaptation to process changes and precise execution of spatially heterogeneous disassembly tasks. However, despite these advantages, systematic research on the integration of mobile disassembly robots within disassembly line applications remains limited. To address this gap, this study integrates 7-axis mobile robots into two-sided disassembly lines and models the system using both mixed-integer programming and constraint programming approaches. The proposed models aim to minimize construction costs and ensure balanced workload distribution across stations. A novel constraint programming-based lexicographic-Pareto approach is developed to solve the resulting multi-objective optimization problem, this method is capable of generating verified Pareto frontiers for small-scale instances and providing high-quality approximate Pareto solution sets for large-scale problems. In the numerical experiments, a sensitivity analysis of key algorithm parameters is first conducted to achieve a balance between computational efficiency and solution quality. Subsequently, the proposed method is benchmarked against nine existing algorithms across twenty datasets to validate its effectiveness. Its practical feasibility is further demonstrated through an application to the disassembly of drum washing machines. The results show that, compared to conventional fixed-robot disassembly lines without cross-station coordination, the mobile robot configuration achieves a 10.7% reduction in total cost and a 66.7% improvement in robot workload balance, offering a promising pathway for advancing remanufacturing practices.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 235-251"},"PeriodicalIF":14.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096558","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":"Digital twin-driven staged error prediction and compensation framework for the whole process of robotic machining","authors":"Teng Zhang ,&nbsp;Fangyu Peng ,&nbsp;Zhao Yang ,&nbsp;Xiaowei Tang ,&nbsp;Jiangmiao Yuan ,&nbsp;Rong Yan","doi":"10.1016/j.jmsy.2025.09.009","DOIUrl":"10.1016/j.jmsy.2025.09.009","url":null,"abstract":"<div><div>Robotic machining has become another important machining paradigm after CNC machine tools. However, robot error has always been an important constraint in its progress towards high quality demand scenarios due to characteristics such as weak rigidity and pose dependence. Numerous scholars have carried out rich work around errors in robotic machining systems, and these studies have achieved excellent results in robot localization, trajectory continuous motion, and machining operations. However, due to the complexity of the robot machining system, the robot error has differentiated performance at different stages, and it is difficult to guarantee the global accuracy of the robot by focusing on and controlling a certain kind of error in a discrete manner. For this reason, a digital twin-driven staged error prediction and compensation framework for the whole robot machining process is constructed. In this framework, the whole process of robot machining is divided into three stages with significant differences: point planning, trajectory planning and material removal. And the error prediction function block in each stage is constructed for the error characteristics (distribution skew, error step, spatial-temporal coupling). For error compensation, a staged error compensation strategy is constructed from three aspects: offline point position, robot body and external three-axis platform, respectively. The constructed system was case-validated in the robotic machining of curved parts. All stages of the error prediction models show high prediction accuracy, and the excellent performance of the staged prediction models is verified by comparing with the classical prediction models. For the error compensation, the designed system is utilized to ensure that the robotic machining system provides a double guarantee on the robot end and the machining quality, the point position absolute error is controlled at 0.109 mm, the orientation error is controlled at 0.028°, the trajectory position error is controlled at 0.067 mm, the orientation error is controlled at 0.031°, and the final part machining error is controlled at 0.036 mm, which is almost approximates the repeatable positioning accuracy of the robot. The proposed framework realizes the system-level sensing and control of the robot machining system error, and provides a unified system framework for the subsequent research of related unit methods, which is conducive to promoting the development of robot machining to high-quality requirement scenarios.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 252-283"},"PeriodicalIF":14.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096556","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":"Generative AI-powered planning: A hybrid graph-diffusion approach for demand-driven flexible manufacturing systems","authors":"Chen Li,&nbsp;Qing Chang","doi":"10.1016/j.jmsy.2025.08.016","DOIUrl":"10.1016/j.jmsy.2025.08.016","url":null,"abstract":"<div><div>Flexible Smart Manufacturing Systems (FSMS) are critical to achieving mass customization and operational agility under Industry 4.0. However, planning effective FSMS configurations remains challenging due to fluctuating market demands, heterogeneous system components, complex interdependencies, and the need to optimize resource utilization. Conventional planning methods often require predefined line configurations and lack adaptability, scalability, and awareness of dynamic system properties. This paper presents a novel Hybrid Graph-Diffusion Based Planning Framework that integrates generative AI with system-theoretic modeling to autonomously generate optimal FSMS configurations based on different market demands. Specifically, we introduce a system model-embedded Heterogeneous Graph (HG) to represent the structure and properties of an FSMS and infuse it within a system property-tailored diffusion model to generate reconfigurable plan configurations. The final system property-guided refinement guarantees that the final plan configuration is optimal in both demand satisfaction and resource use. Furthermore, our ablation studies validate that our framework significantly outperforms conventional approaches in both demand satisfaction and resource efficiency. Furthermore, our ablation studies validate the effectiveness of the system property guidance and HG-based representation in enhancing planning feasibility, robustness, and adaptability.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 175-195"},"PeriodicalIF":14.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096553","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":"RGB video and inertial sensing fusion method for human action recognition in human-robot collaborative manufacturing","authors":"Lili Dong ,&nbsp;Tianliang Hu ,&nbsp;Tianyi Sun ,&nbsp;Junrui Li ,&nbsp;Songhua Ma","doi":"10.1016/j.jmsy.2025.09.007","DOIUrl":"10.1016/j.jmsy.2025.09.007","url":null,"abstract":"<div><div>Human action recognition (HAR), as a prerequisite for robotic dynamic decision-making, is crucial for achieving efficient human-robot collaborative manufacturing (HRCM). Compared with single modality, multi-modality provides a more comprehensive understanding of human actions. However, it is a challenge to effectively integrate this information to fully leverage the advantages of multi-modality for HAR in HRCM. Therefore, in this paper, the RGB video and inertial sensing fusion method for HAR in HRCM is proposed, presenting the systematic exploration of this multi-modality in industrial contexts. Two fusion strategies of two modalities are studied: decision-level fusion and feature-level fusion. Secondly, taking the rotary vector (RV) reducer assembly as an example, a multi-modal human assembly action dataset for HAR (HAAD-SDU) is designed, filling the gap in the HRCM field where publicly representative datasets are scarce. This dataset synchronously introduces RGB video and inertial sensing data containing human assembly information. Finally, the feasibility and effectiveness of the proposed approach are verified by the designed dataset and public dataset, demonstrating superior performance over baseline methods. The experimental results demonstrate that the proposed fusion approach integrating RGB video and inertial sensing modalities not only overcomes the limitations of the single modality but also exhibits strong cross-domain generalizability, proving effective for both industrial tasks and daily activity recognition. In the HRCM scenario specifically, both decision-level and feature-level fusion strategies demonstrate superior recognition capabilities. The decision-level fusion provides a higher recognition accuracy of 95.71 %, while the feature-level fusion achieves competitive accuracy at 94.42 % with low recognition latency of 1.67 s. Notably, the proposed fusion model can accurately recognize human behaviors at least 2 s before they are completed, providing sufficient leftover time for the robotic system to complete collaborative tasks.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 216-234"},"PeriodicalIF":14.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096555","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 multitasking workforce-constrained flexible job shop scheduling problem: An application from a real-world workshop","authors":"Jiahang Li ,&nbsp;Xinyu Li ,&nbsp;Liang Gao ,&nbsp;Cuiyu Wang ,&nbsp;Haojie Chen","doi":"10.1016/j.jmsy.2025.09.004","DOIUrl":"10.1016/j.jmsy.2025.09.004","url":null,"abstract":"<div><div>The flexible job shop scheduling problem with worker constraints plays a vital role in production scheduling because of workforce flexibility. Existing research assumes that single tasking workers can process one operation simultaneously. However, multitasking workers can be observed in a real-world enterprise, where they can handle multiple operations simultaneously. There is a lack of research on the effect of multitasking workers in flexible job shops. This paper investigates a flexible job-shop scheduling problem considering multitasking workers (FJSP-MW). First, a mixed-integer linear programming model with multitasking worker constraints is constructed to minimize total weighted tardiness (TWT). Second, an operation-sequence and resource-sequence (OSRS) encoding method is proposed to represent the solution space using the OS and RS vectors. Besides, a multitasking decoding method is introduced to decode the OS and RS vectors as a feasible schedule in the objective space. Third, a hybrid algorithm (IGA4MW) is proposed and consists of two aspects: (1) three modified genetic operators are designed for the OS and RS vectors to enhance the exploration ability, and (2) a resource-balanced local search method is presented to improve the exploitation ability. Finally, experiments are conducted on medium and large instances to demonstrate the effectiveness and efficiency of IGA4MW. The case study illustrates that the TWT and makespan of the obtained scheduling solution from IGA4MW are reduced by 15.43% and 41.16%, compared to the original scheduling solution. Furthermore, the performance gain of the resource-balanced local search method increases as the solution space is expanded.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 196-215"},"PeriodicalIF":14.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096554","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":"Digital twin-based framework for an efficient execution of CPPS reconfiguration through human–robot collaboration","authors":"Francesco Giuseppe Ciampi ,&nbsp;Thierno M.L. Diallo ,&nbsp;Faïda Mhenni ,&nbsp;Jean-Yves Choley ,&nbsp;Stanislao Patalano","doi":"10.1016/j.jmsy.2025.09.001","DOIUrl":"10.1016/j.jmsy.2025.09.001","url":null,"abstract":"<div><div>This paper presents a Digital Twin-based framework to support the reconfiguration process of Cyber–Physical Production Systems (CPPSs) through human–robot collaboration and Industry 5.0 enabling technologies. The proposed approach integrates a Mixed Reality (MR) module into the digital twin architecture to enhance human–machine interaction, data visualisation, and robot programming. It also incorporates Physics-Informed Neural Networks (PINNs), a hybrid methodology that combines machine learning and physical modelling to improve prediction accuracy and physical consistency. A proof of concept implementation of the framework is carried out on the reconfiguration of a real-world production line within a research platform. The communication mechanism between system modules is tested and discussed in detail. Additionally, the use of PINNs for predicting the energy consumption of a mobile robotic system involved in the reconfiguration task is implemented and benchmarked. The mobile robotic system integrates an AMR (Autonomous Mobile Robot) and a Cobot (collaborative robotic arm). Results show that the proposed model outperforms conventional physics-based and data-driven methods, significantly enhancing the predictive capabilities of the digital twin and broadening its applicability beyond the specific use case.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 157-174"},"PeriodicalIF":14.2,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046931","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":"Metallurgy-guided bidirectional generative framework for performance prediction and inverse process design in hot-rolled steel","authors":"Pengju Xu,&nbsp;Keming Hu,&nbsp;Yuchun Wu,&nbsp;Haodong Zhang,&nbsp;Zhimin Lv,&nbsp;Zhiyan Zhang,&nbsp;Yiquan An,&nbsp;Qian Sun,&nbsp;Xiang He","doi":"10.1016/j.jmsy.2025.08.020","DOIUrl":"10.1016/j.jmsy.2025.08.020","url":null,"abstract":"<div><div>To effectively handle the complex industrial data characteristics inherent in hot-rolled steel manufacturing, this paper proposes a Cycle-Consistent Bidirectional Generative Framework with Metallurgical Priors (C2-BIGF). Focusing on hot-rolled steel strips, the framework leverages an enhanced variational autoencoder (VAE) integrated with metallurgical priors for informed feature engineering and representation learning. Key process features are quantitatively extracted via physically-based microstructural evolution equations and are systematically integrated into both model input and solution validation, thereby embedding metallurgical prior knowledge throughout the entire modeling framework. The proposed approach adopts a bidirectional generative structure combined with a cycle-consistency mechanism, significantly enhancing prediction stability and structural coherence between forward property prediction and inverse process generation. Comprehensive validation is conducted using real-world industrial datasets, including rigorous ablation studies evaluating individual module contributions. Additionally, two practical industrial scenarios are presented: inverse generation of process parameters from desired mechanical properties under fixed alloy compositions, and cost-optimized alloy composition design under fixed process constraints. In each scenario, the feasibility and rationality of generated solutions are critically evaluated through metallurgical prior knowledge and cycle-consistency verification. Experimental results validate the framework's effectiveness. In forward prediction, it achieves an overall R² of 0.9689. In inverse design, it reliably generates solutions for target yield strengths within a ± 15 MPa margin and successfully produces cost-optimized alloy designs, effectively supporting customized and flexible steel production.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 134-156"},"PeriodicalIF":14.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046930","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":"AI Agents and Agentic AI–navigating a plethora of concepts for future manufacturing","authors":"Yinwang Ren,&nbsp;Yangyang Liu,&nbsp;Tang Ji,&nbsp;Xun Xu","doi":"10.1016/j.jmsy.2025.08.017","DOIUrl":"10.1016/j.jmsy.2025.08.017","url":null,"abstract":"<div><div>AI agents are autonomous systems designed to perceive, reason, and act within dynamic environments. With the rapid advancements in generative AI (GenAI), large language models (LLMs) and multimodal large language models (MLLMs) have significantly improved AI agents’ capabilities in semantic comprehension, complex reasoning, and autonomous decision-making. At the same time, the rise of Agentic AI highlights adaptability and goal-directed autonomy in dynamic and complex environments. LLMs-based AI Agents (LLM-Agents), MLLMs-based AI Agents (MLLM-Agents), and Agentic AI contribute to expanding AI’s capabilities in information processing, environmental perception, and autonomous decision-making, opening new avenues for smart manufacturing. However, the definitions, capability boundaries, and practical applications of these emerging AI paradigms in smart manufacturing remain unclear. To address this gap, this study systematically reviews the evolution of AI and AI agent technologies, examines the core concepts and technological advancements of LLM-Agents, MLLM-Agents, and Agentic AI, and explores their potential applications in and integration into manufacturing, along with the potential challenges they may face.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 126-133"},"PeriodicalIF":14.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145046929","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 flexible tooling system for aero-engine pipelines with complex components based on human-robot collaboration","authors":"Bo Zhou ,&nbsp;Jibin Zhao ,&nbsp;Renbo Xia ,&nbsp;Yueling Chen ,&nbsp;Tianyu Zhang ,&nbsp;Hongfeng Wang ,&nbsp;Junwei Wang ,&nbsp;Jiangyu Li ,&nbsp;Jun Zhang ,&nbsp;Ming Li ,&nbsp;Yong Qiao","doi":"10.1016/j.jmsy.2025.09.005","DOIUrl":"10.1016/j.jmsy.2025.09.005","url":null,"abstract":"<div><div>The demand for the manufacturing multi-variety, small batch, and customized pipelines in the aero-engine manufacturing industry is continuously increasing. Driven by advanced manufacturing technologies, the key factor affecting product quality has shifted from various manufacturing stages to the final assembly stage. Currently, automated assembly solutions are constrained by technical bottlenecks and cost pressures, making them difficult to implement; as a result, assembly tasks still rely heavily on manual operations. To adapt to fierce market competition and production changes, manufacturers must strive to automate pipeline assembly. For this purpose, this paper proposes an automated flexible tooling system for pre-welding positioning and assembly quality evaluation. The system adopts a multiple collaborative robots’ architecture and performs reconfigurable assembly in a human-robot collaboration (HRC) mode. The specific implementation steps are as follows: first, workers install connectors on the fixtures of each robot's end effector, adjust them to an appropriate posture, and obtain the spatial posture data of the connectors through scanning; subsequently, a method for determining registration schemes is developed on the basis of the structural characteristics of the connectors. The feature elements are further extracted, and data registration is completed through human-computer interaction (HCI); then, an improved NSGA-III method that integrates the Levenshtein Distance Congestion Elimination (LCE) method is proposed. This method incorporates three types of constraints: obstacle avoidance constraints for the collaborative movement of multiple collaborative robots, feasibility constraints for robot movement, and rotational angle constraints for connectors. It solves the multi-objective optimization problem among total assembly time, uniformity of time allocation, and energy consumption, enabling rapid and efficient robot posture reconstruction; finally, simulation and experimental verification of the system are conducted. The field assembly verification results demonstrate that the assembly quality is significantly improved compared with that of traditional and recent representative algorithms. The proposed assembly method has an accuracy ranging from 0.0452 mm to 0.0807 mm, with an assembly precision of approximately 0.0607 mm, and ensures the stability and predictability of the assembly quality.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 103-125"},"PeriodicalIF":14.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027193","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-centric proactive integrated design paradigm of intelligent manufacturing system towards Industry 5.0","authors":"Shuo Zhang ,&nbsp;Hanwu He ,&nbsp;Xian Yang","doi":"10.1016/j.jmsy.2025.09.002","DOIUrl":"10.1016/j.jmsy.2025.09.002","url":null,"abstract":"<div><div>With the ongoing transition from Industry 4.0 to Industry 5.0, intelligent manufacturing systems (IMS) based on machine vision are increasingly prioritizing both human-centric principles and proactive integrated design. However, existing systems often face challenges in effectively balancing technical performance with the integration of human factors. To address these challenges, this paper proposes a human-centric proactive IMS integration framework aligned with Industry 5.0 objectives. The framework is structured around three key synergistic steps: First, hierarchical task decomposition: Complex production tasks are subdivided into essential subtasks such as calibration, positioning. Second, human-centric design: Human factors are systematically embedded in each subtask through unified design frameworks or computational models. Third, proactive integrated design: enhances autonomous collaboration is achieved through the proactive integrated design of communication protocols, establishing autonomous interactions between IMS modules to enable intelligent cooperation. The experimental results demonstrate that the proposed framework not only retains the technical characteristics of high precision and stability from Industry 4.0, but also incorporates the human-centric usability required for Industry 5.0. This approach extends Industry 4.0 foundations and provides a scalable pathway toward Industry 5.0 development through human-centric proactive integration.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 65-81"},"PeriodicalIF":14.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020028","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}