Journal of Manufacturing Systems最新文献

{"title":"Small-sample machining quality prediction via a fuzzy broad learning system enhanced by prior knowledge","authors":"Zewen Hu ,&nbsp;Yu Shen ,&nbsp;Shuyue Zhang ,&nbsp;Hongcai Chen ,&nbsp;Kanjian Zhang ,&nbsp;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":"2026-02-01","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}

{"title":"Industrial Value Symbiont in the context of Industrial Symbiosis: Retrospect and prospect","authors":"Weinan Sha ,&nbsp;Xinguo Ming ,&nbsp;Zhihua Chen ,&nbsp;Xianyu Zhang ,&nbsp;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":"2026-02-01","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}

{"title":"MCMAS: Causality-driven collaborative optimization in low-carbon industrial parks with large language models-empowered multi-agent systems","authors":"Tao Wu ,&nbsp;Jie Li ,&nbsp;Qiang Liu ,&nbsp;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":"2026-02-01","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}

{"title":"A surrogate model-driven assembly coordination framework for aircraft components based on cooperative multi-agent deep reinforcement learning","authors":"Yifan Zhang ,&nbsp;Wenxu Luo ,&nbsp;Ye Hu ,&nbsp;Qing Wang ,&nbsp;Liang Cheng ,&nbsp;Yinglin Ke","doi":"10.1016/j.jmsy.2025.08.021","DOIUrl":"10.1016/j.jmsy.2025.08.021","url":null,"abstract":"<div><div>This study presents a multi-agent reinforcement learning (MARL) approach to address coordination challenges in aircraft component assembly. A machine learning–based surrogate model is developed to approximate component deformation, enabling the construction of a realistic and computationally efficient MARL training environment. Within this environment, multiple agents rapidly learn strategies to optimize both individual component deformations and inter-component coordination. The surrogate model compresses the high-dimensional displacement fields into lower-dimensional representations, significantly reducing the complexity of the state space. The reward function combines both local and coordination rewards, where the local reward evaluates manufacturing accuracy at the component level, and the coordination reward assesses alignment accuracy between components. By exchanging local state information during training, agents enhance cooperation, accelerate convergence, and improve overall assembly performance. The effectiveness of the proposed method is demonstrated through a fuselage panel assembly case study, achieving average reductions of 94.91 % in panel deformation and 95.02 % in inter-panel gaps. This framework offers a promising solution for coordinating deformable structures, substantially enhancing both assembly quality and efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 46-64"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145010819","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":"IM-Chat: A multi-agent LLM framework integrating tool-calling and diffusion modeling for knowledge transfer in injection molding industry","authors":"Junhyeong Lee ,&nbsp;Joon-Young Kim ,&nbsp;Heekyu Kim ,&nbsp;Inhyo Lee ,&nbsp;Seunghwa Ryu","doi":"10.1016/j.jmsy.2025.11.007","DOIUrl":"10.1016/j.jmsy.2025.11.007","url":null,"abstract":"<div><div>The injection molding industry faces critical challenges in preserving and transferring field knowledge, particularly as experienced workers retire and multilingual barriers hinder effective communication. This study introduces IM-Chat, a multi-agent framework based on large language models (LLMs), designed to facilitate knowledge transfer in injection molding. IM-Chat integrates both limited documented knowledge (e.g., troubleshooting tables, manuals) and extensive field data modeled through a data-driven process condition generator that infers optimal manufacturing settings from environmental inputs such as temperature and humidity, enabling robust and context-aware task resolution. By adopting a retrieval-augmented generation (RAG) strategy and tool-calling agents within a modular architecture, IM-Chat ensures adaptability without the need for fine-tuning. Performance was assessed across 100 single-tool and 60 hybrid tasks for GPT-4o, GPT-4o-mini, and GPT-3.5-turbo by domain experts using a 10-point rubric focused on relevance and correctness, and was further supplemented by automated evaluation using GPT-4o guided by a domain-adapted instruction prompt. The evaluation results indicate that more capable models tend to achieve higher accuracy, particularly in complex, tool-integrated scenarios. In addition, compared with the fine-tuned single-agent LLM, IM-Chat demonstrated superior accuracy, particularly in quantitative reasoning, and greater scalability in handling multiple information sources. Overall, these findings demonstrate the viability of multi-agent LLM systems for industrial knowledge workflows and establish IM-Chat as a scalable and generalizable approach to AI-assisted decision support in manufacturing. To support reproducibility and practical adoption, <span><span>supplementary materials</span></span> including prompts, evaluation data, and video demonstrations are made available.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 839-855"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145516966","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":"Invertible transfer function informed neural ODE to learn stable latent dynamics for degradation process modeling and remaining useful life prediction","authors":"Zheng Zhou ,&nbsp;Yasong Li ,&nbsp;Ruqiang Yan","doi":"10.1016/j.jmsy.2025.10.002","DOIUrl":"10.1016/j.jmsy.2025.10.002","url":null,"abstract":"<div><div>Modeling degradation processes is essential for estimating industrial system performance and guiding maintenance decisions. Machine learning methods, due to the adaptability for diverse data types, show promise to model temporal evolution of degradation in an embedding space known as latent dynamics, especially for neural ordinary differential equations (NODE) with continuous-time property. However, inferring system states from observation data is an inverse problem, and NODEs often inherit ill-posedness further from their complex optimization landscape. We propose an invertible transfer function informed NODE to ensure stable latent dynamics, making the model robust to perturbations in observation data. First, a NODE describes the hidden degradation process, while an invertible Fourier neural operator maps between latent dynamics and observations. Error analysis reveals that stability is governed by data fidelity and the Lipschitz constant of the inverse mapping, forming the basis for our regularization technique. Additionally, we demonstrate that without ground truth degradation data, latent dynamics lack uniqueness, leading to infinite equivalent solutions. Tests on turbofan engine and battery datasets confirm improved robustness and performance in fault diagnosis and prognosis.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 494-504"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145266331","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":"Variational diffusion representation learning framework for semi-supervised industrial soft sensing application","authors":"Hengqian Wang,&nbsp;Lei Chen,&nbsp;Kuangrong Hao,&nbsp;Bing Wei","doi":"10.1016/j.jmsy.2025.11.013","DOIUrl":"10.1016/j.jmsy.2025.11.013","url":null,"abstract":"<div><div>In process manufacturing industries, existing semi-supervised soft sensor methods often exhibit compromised performance under low label rates due to poor generalization, underscoring the urgent need for extracting generalized and transferable features from unlabeled data. Recently, diffusion model-based self-supervised learning (SSL) has shown great potential in addressing this challenge by leveraging powerful generative mechanisms to capture complex data distributions and distill informative representations without extensive supervision. Motivated by this, this paper proposes a variational diffusion representation learning (VDRL) framework for semi-supervised soft sensing modeling under low label rates. First, we propose a self-supervised parametric terminal variational diffusion (PTVD) model for generalized feature extraction. By defining the forward process terminal of the diffusion model as parameterized learnable latent distributions and attempting to recover the original input samples in the reverse process, we successfully achieve a transformation in the role of the diffusion model from the original sample generator to the desired feature extractor. The designed pretext task of patch-level mask reconstruction further improves its capability on temporal data and allows the PTVD model to be trained in a self-supervised manner without the involvement of real labels. Subsequently, we propose a temporal-diffusion joint representation (TDJR) model for the prediction of quality variables based on the extracted generalized multi-granular features. In order to fully exploit the joint dynamic information of multi-granular features in different sequence dimensions, we innovatively extract joint representation information from both time and diffusion dimensions simultaneously and complementarily, and perform supervised training under limited label guidance. A series of experimental evaluations on two real industrial processes validate the framework’s effectiveness and stability in soft sensing applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 923-936"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568612","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":"Intelligent disassembly scenario understanding for human behavior and intention recognition towards self-perception human-robot collaboration system","authors":"Jinhua Xiao ,&nbsp;Bo Wang ,&nbsp;Kaile Huang ,&nbsp;Sergio Terzi ,&nbsp;Wei Wang ,&nbsp;Marco Macchi","doi":"10.1016/j.jmsy.2025.11.012","DOIUrl":"10.1016/j.jmsy.2025.11.012","url":null,"abstract":"<div><div>The recycling of end-of-life (EOL) products poses significant challenges due to inefficient and unsafe disassembly processes. To address this, we propose a novel self-perception human-robot collaboration (HRC) system that enhances disassembly efficiency and safety through multi-modal human intention recognition. Our core methodological innovation lies in the real-time fusion of three key perception modules: action recognition using a Spatial-Temporal Graph Convolutional Network (ST-GCN), disassembly tool detection based on an enhanced YOLO algorithm, and facial angle recognition for operator awareness inference. A dedicated dataset for retired power battery disassembly was constructed to support this research, encompassing human skeletal data for action recognition, labeled images for tool detection, and facial expression detection. The proposed system was validated on a physical HRC disassembly platform. Experimental results demonstrate a marked improvement, with our integrated intention recognition method achieving an accuracy of approximately 85 %, significantly outperforming traditional single-modality approaches. Furthermore, the HRC disassembly operation was completed in 238 s, which is 60 s (or 20 %) faster than purely manual disassembly. This work provides a robust and efficient HRC disassembly framework for intelligent disassembly scenario understanding, contributing to advancing circular manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 937-962"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568611","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":"Efficient human–robot collaborative manipulation of planar deformable objects","authors":"Enrico Villagrossi ,&nbsp;Paolo Franceschi ,&nbsp;Giorgio Nicola ,&nbsp;Nicola Pedrocchi","doi":"10.1016/j.jmsy.2025.11.011","DOIUrl":"10.1016/j.jmsy.2025.11.011","url":null,"abstract":"<div><div>This paper presents an efficient method to improve the productivity and the accuracy of human–robot collaboration in transporting large, planar, deformable objects, specifically during the production of parts made of advanced composite materials. The proposed approach utilises an industrial robot to assist operators in transporting and handling carbon fibre and fibreglass plies during draping. A consumer vision system feeds a data-driven model that estimates the material’s deformation from depth images. This deformation data, transformed into force/torque information via a virtual spring, informs a Human–Robot Role Arbitration (RA) algorithm that dynamically adjusts leadership between humans and robots based on context, enhancing safety and efficiency. Inspired by game theory, the approach adapts to cooperative and non-cooperative scenarios, demonstrating significant productivity gains over traditional algorithms used for the same scope. The paper also compares the use of the RA algorithms with the current industrial practice, which relies entirely on manual production. Company operators, working in a production site, performed the experimental comparison producing a real boat propeller.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 963-975"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568610","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":"Virtual reconstruction-based digital twin shop floor: theoretical methodology, industrial software, and applications","authors":"Guofu Ding ,&nbsp;Mingyuan Liu ,&nbsp;Haojie Chen ,&nbsp;Jian Zhang ,&nbsp;Shuying Wang ,&nbsp;Jiaxiang Xie","doi":"10.1016/j.jmsy.2025.09.019","DOIUrl":"10.1016/j.jmsy.2025.09.019","url":null,"abstract":"<div><div>Digital Twin (DT) has become a key enabling technology for intelligent upgrading in discrete manufacturing shops. However, existing research primarily focuses on specific production applications, lacking consideration of the continuity across the overall production operation cycle. Such decentralized research leads to model fragmentation, data silos, and logical inconsistencies among multiple application scenarios such as pre-production planning, in-production execution, and post-production analysis, making effective integration and collaboration difficult. To address these challenges, this paper proposes a Seven-Element virtual reconstruction theory that enables consistent modeling of production elements, organizational forms, and execution logic. Based on this theory, a DT shop construction and operation framework centered on unified production logic is developed to support seamless integration and collaboration of various production applications. Additionally, operation methods for three core production applications of DT shop execution, simulation, and monitoring are systematically developed, establishing an overall technical system throughout the entire production cycle driven by a unified model. Corresponding DT industrial software systems are developed to support engineering implementation of the proposed methods. Validation through an actual shop floor demonstrates that the proposed method effectively achieves model unification and data fusion across multiple application scenarios, enhances both effectiveness and consistency of DT shop construction and operation.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 424-444"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220749","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}