Surface roughness is a critical indicator of machined workpiece quality, and accurately modeling its relationship with process parameters is essential for process optimization and intelligent decision-making. Fuzzy broad learning system (FBLS) has demonstrated considerable advantages in nonlinear predictive modeling; however, its performance under small-sample conditions may be limited due to an incomplete rule base and the lack of explicit physical mechanisms. To address this challenge, this article proposes a knowledge-enhanced fuzzy broad learning system (KEFBLS) that integrates dual sources of prior knowledge — expert-knowledge-guided fuzzy partition and physics-based fuzzy rule consequents — to improve predictive accuracy and generalization ability. The effectiveness of KEFBLS is validated on both real-world robotic grinding experiments and a publicly available machining dataset, achieving average prediction errors of only 10.3% and 4.7%, respectively, representing over 20% accuracy improvement over the FBLS baseline. These results highlight the significance of combining domain-specific prior knowledge with data-driven learning, enabling robust performance under limited-data conditions. Overall, KEFBLS provides a unified knowledge- and data-driven framework for surface roughness prediction, with potential applicability to other manufacturing processes where labeled data are scarce.
{"title":"Small-sample machining quality prediction via a fuzzy broad learning system enhanced by prior knowledge","authors":"Zewen Hu , Yu Shen , Shuyue Zhang , Hongcai Chen , Kanjian Zhang , Haikun Wei","doi":"10.1016/j.jmsy.2025.12.021","DOIUrl":"10.1016/j.jmsy.2025.12.021","url":null,"abstract":"<div><div>Surface roughness is a critical indicator of machined workpiece quality, and accurately modeling its relationship with process parameters is essential for process optimization and intelligent decision-making. Fuzzy broad learning system (FBLS) has demonstrated considerable advantages in nonlinear predictive modeling; however, its performance under small-sample conditions may be limited due to an incomplete rule base and the lack of explicit physical mechanisms. To address this challenge, this article proposes a knowledge-enhanced fuzzy broad learning system (KEFBLS) that integrates dual sources of prior knowledge — expert-knowledge-guided fuzzy partition and physics-based fuzzy rule consequents — to improve predictive accuracy and generalization ability. The effectiveness of KEFBLS is validated on both real-world robotic grinding experiments and a publicly available machining dataset, achieving average prediction errors of only 10.3% and 4.7%, respectively, representing over 20% accuracy improvement over the FBLS baseline. These results highlight the significance of combining domain-specific prior knowledge with data-driven learning, enabling robust performance under limited-data conditions. Overall, KEFBLS provides a unified knowledge- and data-driven framework for surface roughness prediction, with potential applicability to other manufacturing processes where labeled data are scarce.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 601-613"},"PeriodicalIF":14.2,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879995","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}
Pub Date : 2025-12-27DOI: 10.1016/j.jmsy.2025.12.022
Sai Geng , Yu Guo , Weiwei Qian , Weiguang Fang , Shengbo Wang , Shaohua Huang , Xiaoyu Hou
In the complex and dynamic discrete manufacturing environment, accurate prediction of the workshop operation situation (WOS) is crucial to ensure on-time delivery of orders. However, the spatio-temporal (ST) coupling characteristics of the manufacturing process, dynamic fluctuations of workshop performance, and varying contributions of samples to the prediction model make WOS prediction more challenging. To address these issues, this paper proposes a ST parallel ensemble learning approach for WOS prediction. Specifically, based on workshop production data, a temporal data model and a dynamic graph model are constructed to comprehensively characterize the ST characteristics of the production process. Subsequently, this paper proposes a ST parallel ensemble learning method, named Adaboost-GLT, which integrates three ST weak learners (GCN, LSTM, and TGCN) to effectively capture the ST characteristics. Furthermore, a dynamic optimal selection mechanism is designed to adaptively select the best-performing weak learner at each stage, enabling the prediction method to evolve synchronously with the dynamic changes of the manufacturing process. Additionally, a sample weight updating strategy that takes into account sample timeliness and prediction error is introduced to improve the rationality of Adaboost-GLT's attention allocation to samples during training. Finally, the performance of Adaboost-GLT is experimentally validated on real workshop production datasets. The experimental results show that Adaboost-GLT can fully exploit the ST characteristics, effectively cope with the dynamic fluctuations of workshop performance, and thereby achieve high-precision prediction of WOS.
{"title":"A spatio-temporal parallel ensemble learning approach for operation situation prediction in discrete manufacturing workshop","authors":"Sai Geng , Yu Guo , Weiwei Qian , Weiguang Fang , Shengbo Wang , Shaohua Huang , Xiaoyu Hou","doi":"10.1016/j.jmsy.2025.12.022","DOIUrl":"10.1016/j.jmsy.2025.12.022","url":null,"abstract":"<div><div>In the complex and dynamic discrete manufacturing environment, accurate prediction of the workshop operation situation (WOS) is crucial to ensure on-time delivery of orders. However, the spatio-temporal (ST) coupling characteristics of the manufacturing process, dynamic fluctuations of workshop performance, and varying contributions of samples to the prediction model make WOS prediction more challenging. To address these issues, this paper proposes a ST parallel ensemble learning approach for WOS prediction. Specifically, based on workshop production data, a temporal data model and a dynamic graph model are constructed to comprehensively characterize the ST characteristics of the production process. Subsequently, this paper proposes a ST parallel ensemble learning method, named Adaboost-GLT, which integrates three ST weak learners (GCN, LSTM, and TGCN) to effectively capture the ST characteristics. Furthermore, a dynamic optimal selection mechanism is designed to adaptively select the best-performing weak learner at each stage, enabling the prediction method to evolve synchronously with the dynamic changes of the manufacturing process. Additionally, a sample weight updating strategy that takes into account sample timeliness and prediction error is introduced to improve the rationality of Adaboost-GLT's attention allocation to samples during training. Finally, the performance of Adaboost-GLT is experimentally validated on real workshop production datasets. The experimental results show that Adaboost-GLT can fully exploit the ST characteristics, effectively cope with the dynamic fluctuations of workshop performance, and thereby achieve high-precision prediction of WOS.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 584-600"},"PeriodicalIF":14.2,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836450","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}
Pub Date : 2025-12-26DOI: 10.1016/j.jmsy.2025.12.019
Jintao Xue, Xiao Li, Nianmin Zhang
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 particle filter with constrained dueling double deep Q-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.
{"title":"Safe reinforcement learning with online filtering for fatigue-predictive human–robot task planning and allocation in production","authors":"Jintao Xue, Xiao Li, 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":"2025-12-26","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}
Pub Date : 2025-12-24DOI: 10.1016/j.jmsy.2025.12.018
Haorui Sun , Yifan Zhang , Linbei Jiang , Shouguo Zheng , Qing Wang , Yinglin Ke
The precise assembly of aircraft structures remains a critical challenge in aerospace manufacturing, as assembly gaps are a primary factor undermining precision. This study proposes a surrogate model for the rapid and accurate prediction of assembly gaps in the presence of structural deformation. The methodology begins with creating a comprehensive assembly gap dataset that integrates both geometric deviations and structural deformations through assembly modeling and automated workflow. Building upon this dataset, an enhanced PointNet+ + (PNP) network is employed to extract and fuse multi-source assembly features from part shape point clouds and tooling movements. These fused features are then integrated with a generation network based on the Conditional Generative Adversarial Network (CGAN) architecture, reformulating gap prediction as a conditional generation task. The innovative integration of the two networks realizes an end-to-end pipeline, from initial assembly feature extraction to final assembly gap prediction. A representative wing-box structure was employed as a case study to validate the approach. The trained model efficiently predicts gap fields directly from multi-source assembly information. Experimental results demonstrate that the proposed model achieves prediction accuracy comparable to virtual assembly methods while significantly enhancing computational efficiency. These findings underscore the model’s efficacy, positioning it as a valuable tool for rapidly predicting gaps in aircraft assembly.
{"title":"Predicting aircraft assembly gaps considering structural deformation: A CGAN-based surrogate modeling approach","authors":"Haorui Sun , Yifan Zhang , Linbei Jiang , Shouguo Zheng , Qing Wang , Yinglin Ke","doi":"10.1016/j.jmsy.2025.12.018","DOIUrl":"10.1016/j.jmsy.2025.12.018","url":null,"abstract":"<div><div>The precise assembly of aircraft structures remains a critical challenge in aerospace manufacturing, as assembly gaps are a primary factor undermining precision. This study proposes a surrogate model for the rapid and accurate prediction of assembly gaps in the presence of structural deformation. The methodology begins with creating a comprehensive assembly gap dataset that integrates both geometric deviations and structural deformations through assembly modeling and automated workflow. Building upon this dataset, an enhanced PointNet+ + (PNP) network is employed to extract and fuse multi-source assembly features from part shape point clouds and tooling movements. These fused features are then integrated with a generation network based on the Conditional Generative Adversarial Network (CGAN) architecture, reformulating gap prediction as a conditional generation task. The innovative integration of the two networks realizes an end-to-end pipeline, from initial assembly feature extraction to final assembly gap prediction. A representative wing-box structure was employed as a case study to validate the approach. The trained model efficiently predicts gap fields directly from multi-source assembly information. Experimental results demonstrate that the proposed model achieves prediction accuracy comparable to virtual assembly methods while significantly enhancing computational efficiency. These findings underscore the model’s efficacy, positioning it as a valuable tool for rapidly predicting gaps in aircraft assembly.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 518-540"},"PeriodicalIF":14.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836453","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}
In manufacturing systems with a job shop organization, queues between workstations create an intermittent process flow, allowing workers to schedule tasks entering the queue based on their needs and preferences. The resulting scheduling autonomy of individual workers often leads to inefficiencies in the overall production process due to the loss of control. Companies are therefore increasingly using algorithmic scheduling systems to assign task sequences to workers, thereby drastically reducing their autonomy and negatively affecting their job performance and well-being. This paper extends the existing flexible job shop scheduling problem by sequencing preferences (FJSPSP) to incorporate a human-centered perspective by predicting workers’ task sequencing decisions using learning-to-rank (LTR) methods. By learning workers’ individual task sequencing preferences, it becomes possible to predict the processing sequence based on task characteristics. The scheduling algorithm for the FJSPSP presented in the paper incorporates workers’ learned sequencing preferences as constraints. Considering workers’ learned task sequencing decisions, the FJSPSP optimizes only task assignments to maintain workers’ autonomy over task sequences. The contributions of this paper are fourfold, namely, (1) presenting an approach to elicit sequencing decision datasets from workers, (2) demonstrating the successful prediction of humans’ and an actual worker’s task sequencing decisions with LTR, (3) formulating the FJSPSP variant that integrates workers’ sequencing preferences as constraints and proving its effectiveness in a simulation study, and (4) consolidating these steps into an explainable artificial intelligence (XAI)- and LTR-enabled sociotechnical system design framework. The paper closes with a discussion of the overall methodology and future research perspectives.
{"title":"Incorporating scheduling autonomy of workers into flexible job shop scheduling: Learning and balancing decentralized task sequencing decisions with overall scheduling performance","authors":"Jan-Phillip Herrmann , Sven Tackenberg , Tharsika Pakeerathan Srirajan , Verena Nitsch","doi":"10.1016/j.jmsy.2025.12.020","DOIUrl":"10.1016/j.jmsy.2025.12.020","url":null,"abstract":"<div><div>In manufacturing systems with a job shop organization, queues between workstations create an intermittent process flow, allowing workers to schedule tasks entering the queue based on their needs and preferences. The resulting scheduling autonomy of individual workers often leads to inefficiencies in the overall production process due to the loss of control. Companies are therefore increasingly using algorithmic scheduling systems to assign task sequences to workers, thereby drastically reducing their autonomy and negatively affecting their job performance and well-being. This paper extends the existing flexible job shop scheduling problem by sequencing preferences (FJSPSP) to incorporate a human-centered perspective by predicting workers’ task sequencing decisions using learning-to-rank (LTR) methods. By learning workers’ individual task sequencing preferences, it becomes possible to predict the processing sequence based on task characteristics. The scheduling algorithm for the FJSPSP presented in the paper incorporates workers’ learned sequencing preferences as constraints. Considering workers’ learned task sequencing decisions, the FJSPSP optimizes only task assignments to maintain workers’ autonomy over task sequences. The contributions of this paper are fourfold, namely, (1) presenting an approach to elicit sequencing decision datasets from workers, (2) demonstrating the successful prediction of humans’ and an actual worker’s task sequencing decisions with LTR, (3) formulating the FJSPSP variant that integrates workers’ sequencing preferences as constraints and proving its effectiveness in a simulation study, and (4) consolidating these steps into an explainable artificial intelligence (XAI)- and LTR-enabled sociotechnical system design framework. The paper closes with a discussion of the overall methodology and future research perspectives.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 541-560"},"PeriodicalIF":14.2,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145836452","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}
Pub Date : 2025-12-19DOI: 10.1016/j.jmsy.2025.12.014
James Josep Perry , Pablo Garcia-Conde Ortiz , George Konstantinou , Cornelie Vergouwen, Edlyn Santha Kumaran, Morteza Moradi
Health indicators (HIs) are central to diagnosing and prognosing the condition of aerospace composite structures, enabling efficient maintenance and operational safety. However, extracting reliable HIs remains challenging due to variability in material properties, stochastic damage evolution, and diverse damage modes. Manufacturing defects (e.g. disbonds) and in-service incidents (e.g. bird strikes) further complicate this process. This study presents a comprehensive data-driven framework that learns HIs via two learning approaches integrated with multi-domain signal processing. Because ground-truth HIs are unavailable, a semi-supervised and an unsupervised approach are proposed: (i) a diversity deep semi-supervised anomaly detection (Diversity-DeepSAD) approach augmented with continuous auxiliary labels used as hypothetical damage proxies, which overcomes the limitation of prior binary labels and enables modelling of intermediate degradation, and (ii) a degradation-trend-constrained variational autoencoder (DTC-VAE), in which the monotonicity criterion is embedded via an explicit trend constraint. Guided waves with multiple excitation frequencies are used to monitor single-stiffener composite structures under fatigue loading. Time, frequency, and time–frequency representations are explored, and per-frequency HIs are fused via unsupervised ensemble learning to mitigate frequency dependence and reduce variance. Using fast Fourier transform features, the models achieved fitness scores of 81.6% (Diversity-DeepSAD) and 92.3% (DTC-VAE), indicating improved monotonicity and consistency over existing baselines. The proposed history-independent framework, supported by prognostic metrics–guided Bayesian optimisation and excitation frequency-agnostic HI fusion, enables the estimation of more robust HIs for aeronautical composite structures.
{"title":"Semi-supervised and unsupervised learning for health indicator extraction from guided waves in aerospace composite structures","authors":"James Josep Perry , Pablo Garcia-Conde Ortiz , George Konstantinou , Cornelie Vergouwen, Edlyn Santha Kumaran, Morteza Moradi","doi":"10.1016/j.jmsy.2025.12.014","DOIUrl":"10.1016/j.jmsy.2025.12.014","url":null,"abstract":"<div><div>Health indicators (HIs) are central to diagnosing and prognosing the condition of aerospace composite structures, enabling efficient maintenance and operational safety. However, extracting reliable HIs remains challenging due to variability in material properties, stochastic damage evolution, and diverse damage modes. Manufacturing defects (e.g. disbonds) and in-service incidents (e.g. bird strikes) further complicate this process. This study presents a comprehensive data-driven framework that learns HIs via two learning approaches integrated with multi-domain signal processing. Because ground-truth HIs are unavailable, a semi-supervised and an unsupervised approach are proposed: (i) a diversity deep semi-supervised anomaly detection (Diversity-DeepSAD) approach augmented with continuous auxiliary labels used as hypothetical damage proxies, which overcomes the limitation of prior binary labels and enables modelling of intermediate degradation, and (ii) a degradation-trend-constrained variational autoencoder (DTC-VAE), in which the monotonicity criterion is embedded via an explicit trend constraint. Guided waves with multiple excitation frequencies are used to monitor single-stiffener composite structures under fatigue loading. Time, frequency, and time–frequency representations are explored, and per-frequency HIs are fused via unsupervised ensemble learning to mitigate frequency dependence and reduce variance. Using fast Fourier transform features, the models achieved fitness scores of 81.6% (Diversity-DeepSAD) and 92.3% (DTC-VAE), indicating improved monotonicity and consistency over existing baselines. The proposed history-independent framework, supported by prognostic metrics–guided Bayesian optimisation and excitation frequency-agnostic HI fusion, enables the estimation of more robust HIs for aeronautical composite structures.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 468-492"},"PeriodicalIF":14.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786670","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}
Pub Date : 2025-12-19DOI: 10.1016/j.jmsy.2025.12.003
Weinan Sha , Xinguo Ming , Zhihua Chen , Xianyu Zhang , Jiapeng You
Industrial Symbiosis (IS) is regarded as a key enabler of the Circular Economy; its objective is to establish closed resource cycles and promote sustainable production through intensive inter-industrial exchanges of energy, materials, water, and by-products/waste. However, there exists a lack of value-oriented propositions in IS practices within industrial parks before realizing the circular economy vision, and a significant gap remains between the existing IS production system and the evolutionary prospects of a mature industrial ecosystem. To provide prospective insights for the next IS evolution, in this paper, a very first discussion of the Industrial Value Symbiont (IVS) is proposed by retrospecting its ongoing evolutionary paradigm. Then, this paper analyzes several frontier concepts and definitions related to IVS, and presents a proper connotation. To refine its intrinsic constituent diversification and delineate its operational essence, the conceptual framework of IVS is further constructed. Finally, pathways for implementing IVS towards IS 5.0, together with related enablers, digital initiatives, and potential strategies, are discussed. Barriers, challenges, and future research directions of IVS are concluded, respectively. We expect that this work may serve as a cornerstone resource for advancing the evolution and development of IS, offering guidance for value-driven IS in an underexplored research domain.
{"title":"Industrial Value Symbiont in the context of Industrial Symbiosis: Retrospect and prospect","authors":"Weinan Sha , Xinguo Ming , Zhihua Chen , Xianyu Zhang , Jiapeng You","doi":"10.1016/j.jmsy.2025.12.003","DOIUrl":"10.1016/j.jmsy.2025.12.003","url":null,"abstract":"<div><div>Industrial Symbiosis (IS) is regarded as a key enabler of the Circular Economy; its objective is to establish closed resource cycles and promote sustainable production through intensive inter-industrial exchanges of energy, materials, water, and by-products/waste. However, there exists a lack of value-oriented propositions in IS practices within industrial parks before realizing the circular economy vision, and a significant gap remains between the existing IS production system and the evolutionary prospects of a mature industrial ecosystem. To provide prospective insights for the next IS evolution, in this paper, a very first discussion of the Industrial Value Symbiont (IVS) is proposed by retrospecting its ongoing evolutionary paradigm. Then, this paper analyzes several frontier concepts and definitions related to IVS, and presents a proper connotation. To refine its intrinsic constituent diversification and delineate its operational essence, the conceptual framework of IVS is further constructed. Finally, pathways for implementing IVS towards IS 5.0, together with related enablers, digital initiatives, and potential strategies, are discussed. Barriers, challenges, and future research directions of IVS are concluded, respectively. We expect that this work may serve as a cornerstone resource for advancing the evolution and development of IS, offering guidance for value-driven IS in an underexplored research domain.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 493-517"},"PeriodicalIF":14.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786610","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}
Pub Date : 2025-12-19DOI: 10.1016/j.jmsy.2025.12.015
Tao Wu , Jie Li , Qiang Liu , Jinsong Bao
Low-carbon smart industrial parks face challenges such as the high volatility of renewable energy, highly uncertain load demands, and the pronounced dynamic complexity of multi-energy coupled systems. However, current scheduling systems heavily rely on expert knowledge and statistical correlations, which generally suffer from insufficient accuracy, low efficiency, and poor transparency in decision-making. To address these issues, this paper proposes an energy causality-driven digital twins multi-agent scheduling system, named MCMAS, aimed at achieving high-precision, reliable, and interpretable multi-energy coordinated optimization under complex operating conditions. Initially, a multi-energy causal dynamic model is proposed, which initializes the causal model by integrating conditional mutual information with domain knowledge and applies causal intervention to effectively eliminate spurious correlations, thereby accurately characterizing the high-fidelity causal topology of the system. Subsequently, a causality cognition-driven multi-agent collaborative decision-making mechanism is designed, where a cooperative reward function, integrating local rewards, upstream penalties, and downstream incentives, guides global cooperative strategies of agents, thereby enhancing the economic efficiency and reliability of the scheduling system. Finally, a large language model-driven dual-cross evaluation mechanism is designed, which integrates process mining and counterfactual causal inference to conduct dual-cross validation of scheduling strategies, thereby quantifying confidence levels and enhancing the interpretability of decision-making schemes. Comparative experiments conducted in a representative smart industrial park in Shanghai demonstrate that, compared with benchmark models such as MPC and QMIX-MAS, MCMAS reduces total operating costs by approximately 37.04 %, decreases carbon emissions by 45.19 %, and improves the Sharpe Ratio by 37.3 %. The results indicate that MCMAS can effectively coordinate multi-energy supply and dynamic production loads across different scenarios, reducing operational costs and carbon emissions.
{"title":"MCMAS: Causality-driven collaborative optimization in low-carbon industrial parks with large language models-empowered multi-agent systems","authors":"Tao Wu , Jie Li , Qiang Liu , Jinsong Bao","doi":"10.1016/j.jmsy.2025.12.015","DOIUrl":"10.1016/j.jmsy.2025.12.015","url":null,"abstract":"<div><div>Low-carbon smart industrial parks face challenges such as the high volatility of renewable energy, highly uncertain load demands, and the pronounced dynamic complexity of multi-energy coupled systems. However, current scheduling systems heavily rely on expert knowledge and statistical correlations, which generally suffer from insufficient accuracy, low efficiency, and poor transparency in decision-making. To address these issues, this paper proposes an energy causality-driven digital twins multi-agent scheduling system, named MCMAS, aimed at achieving high-precision, reliable, and interpretable multi-energy coordinated optimization under complex operating conditions. Initially, a multi-energy causal dynamic model is proposed, which initializes the causal model by integrating conditional mutual information with domain knowledge and applies causal intervention to effectively eliminate spurious correlations, thereby accurately characterizing the high-fidelity causal topology of the system. Subsequently, a causality cognition-driven multi-agent collaborative decision-making mechanism is designed, where a cooperative reward function, integrating local rewards, upstream penalties, and downstream incentives, guides global cooperative strategies of agents, thereby enhancing the economic efficiency and reliability of the scheduling system. Finally, a large language model-driven dual-cross evaluation mechanism is designed, which integrates process mining and counterfactual causal inference to conduct dual-cross validation of scheduling strategies, thereby quantifying confidence levels and enhancing the interpretability of decision-making schemes. Comparative experiments conducted in a representative smart industrial park in Shanghai demonstrate that, compared with benchmark models such as MPC and QMIX-MAS, MCMAS reduces total operating costs by approximately 37.04 %, decreases carbon emissions by 45.19 %, and improves the Sharpe Ratio by 37.3 %. The results indicate that MCMAS can effectively coordinate multi-energy supply and dynamic production loads across different scenarios, reducing operational costs and carbon emissions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 451-467"},"PeriodicalIF":14.2,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786671","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jmsy.2025.12.007
Lin Huang , Donglin Wang , Shikui Zhao
Modern manufacturing often requires collaboration across multiple factories due to the dispersion of specialized equipment and differences in processing capacity. In such scenarios, jobs must be processed across different sites, and cross-factory transfers significantly impact scheduling performance. This paper studies the distributed job shop scheduling problem with transfers and proposes an Extended Graphical Method with Reinforcement Learning (EGMRL) to effectively address it. The main innovations of EGMRL are fourfold. First, a transfer-zone is introduced into the graphical method, enabling explicit modeling of both intra-factory and inter-factory transportation times. Second, a layered path search algorithm is developed to accelerate path exploration, thereby improving computational efficiency while maintaining accuracy. Third, a Q-learning–based adaptive strategy dynamically guides job deletion and reinsertion according to the inter-factory state, enhancing adaptability across different problem scales. Finally, a tabu search module is integrated as a local improvement strategy to refine factory-level schedules and prevent premature convergence. Comprehensive experiments on 240 extended benchmark instances and a real-world engineering case study demonstrate that EGMRL consistently outperforms four competitive algorithms in terms of solution quality and stability. Furthermore, the results suggest that the extended graphical method provides promising new solution approaches for tackling scheduling problems with other practical constraints, such as worker–machine collaboration and sequence-dependent setup times.
{"title":"An extended graphical method with reinforcement learning for distributed job shop scheduling problems with transfers","authors":"Lin Huang , Donglin Wang , Shikui Zhao","doi":"10.1016/j.jmsy.2025.12.007","DOIUrl":"10.1016/j.jmsy.2025.12.007","url":null,"abstract":"<div><div>Modern manufacturing often requires collaboration across multiple factories due to the dispersion of specialized equipment and differences in processing capacity. In such scenarios, jobs must be processed across different sites, and cross-factory transfers significantly impact scheduling performance. This paper studies the distributed job shop scheduling problem with transfers and proposes an Extended Graphical Method with Reinforcement Learning (EGMRL) to effectively address it. The main innovations of EGMRL are fourfold. First, a transfer-zone is introduced into the graphical method, enabling explicit modeling of both intra-factory and inter-factory transportation times. Second, a layered path search algorithm is developed to accelerate path exploration, thereby improving computational efficiency while maintaining accuracy. Third, a Q-learning–based adaptive strategy dynamically guides job deletion and reinsertion according to the inter-factory state, enhancing adaptability across different problem scales. Finally, a tabu search module is integrated as a local improvement strategy to refine factory-level schedules and prevent premature convergence. Comprehensive experiments on 240 extended benchmark instances and a real-world engineering case study demonstrate that EGMRL consistently outperforms four competitive algorithms in terms of solution quality and stability. Furthermore, the results suggest that the extended graphical method provides promising new solution approaches for tackling scheduling problems with other practical constraints, such as worker–machine collaboration and sequence-dependent setup times.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 426-450"},"PeriodicalIF":14.2,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786605","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}
Pub Date : 2025-12-18DOI: 10.1016/j.jmsy.2025.12.010
Yan Xu , Li Li , Guanghui Lang , Yang Luo , Junhua Zhao , Congbo Li
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 (VB) 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-VB 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.
{"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 , Li Li , Guanghui Lang , Yang Luo , Junhua Zhao , 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":"2025-12-18","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}
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