Journal of Manufacturing Systems最新文献

{"title":"An improved large language model and knowledge graph integration method for automated machining process base construction","authors":"Fu Yan ,&nbsp;Jie Liu ,&nbsp;Liang Guo ,&nbsp;Li Liu ,&nbsp;XiangYu Geng","doi":"10.1016/j.jmsy.2026.01.016","DOIUrl":"10.1016/j.jmsy.2026.01.016","url":null,"abstract":"<div><div>The Machining Process Knowledge Base (MPKB) is foundational to intelligent process decision-making, directly impacting manufacturing efficiency and quality. While Large Language Models (LLMs) have shown promise in automated MPKB construction, they face a critical challenge in manufacturing: industrial knowledge graph (KG) schemas often exceed the context windows of lightweight LLMs deployable by small and medium-sized enterprises (SMEs). This limitation forces the construction process to operate with incomplete schema information, leading to missed entity relationships, semantic heterogeneity, and conceptual ambiguities in the MPKB. This study proposes an improved LLM-KG collaborative framework that overcomes these limitations through: (1) employing a staged, schema-decoupled extraction strategy, which enables open triple collection without injecting the full schema; (2) introducing a Code-Style knowledge representation method that efficiently encodes complex machining schemas, reducing the semantic load while maintaining structural integrity; and (3) constructing a retrieval-driven pipeline for semantic standardization that integrates dynamic schema segmentation and bidirectional validation, utilizing LLMs to achieve interpretable synonym merging and eliminate heterogeneity. This study empirically validated the proposed approach using machining process data provided by an aviation enterprise. Experimental results demonstrate that our framework achieves at least a 3.3% improvement in MPKB construction quality and a 25% increase in machining process quality metrics compared to the other baseline models. The implementation and data have been made available on GitHub to facilitate reproducibility and further research.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 318-337"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078235","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":"Hybrid digital twins for smart manufacturing: Architectures, fusion paradigm, and implementation challenges","authors":"Xi Zhang ,&nbsp;Yiqun Kou ,&nbsp;Xin Zhang ,&nbsp;Qi Shi ,&nbsp;Youmin Hu ,&nbsp;Huapeng Wu ,&nbsp;Shimin Liu ,&nbsp;Pai Zheng","doi":"10.1016/j.jmsy.2025.12.029","DOIUrl":"10.1016/j.jmsy.2025.12.029","url":null,"abstract":"<div><div>As a high-fidelity representation of physical objects, the digital twin (DT) emerges as a crucial enabling tool supporting intelligent monitoring, prediction, and decision-making for smart manufacturing. To achieve reliable, accurate, and explainable DT modeling under dynamic conditions, it is necessary to integrate multiple models, including first-principles knowledge, data-driven algorithms, and simulation. Furthermore, with the emergence of state-of-the-art artificial intelligence (AI) technologies, such as Generative AI and Large Language Models, new drivers for DT modeling can be provided. However, the specific paradigm for hybridizing these models varies significantly depending on the application scenario, the object, and the critical requirements. This diversity poses a significant challenge for systematically selecting and combining modeling techniques in smart manufacturing. This review addresses this gap by providing a systematic exploration of the Hybrid Digital Twin (HDT) modeling paradigm, which focuses on the integration of multiple heterogeneous models. Therefore, this paper aims to: (1) clarify the architecture and core characteristics of HDT; (2) categorize critical technologies and fusion paradigms for HDT implementation; and (3) outline potential future research directions. It is hoped that this paper will serve as a systematic reference for researchers and engineers seeking to apply HDT to build more accurate, reliable, and adaptive DT applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 51-71"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928459","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":"Spatial information bottleneck graph structure learning based multivariate time series prediction for industrial processes","authors":"Xun Shi ,&nbsp;Kuangrong Hao ,&nbsp;Xianyi Zeng ,&nbsp;Lei Chen ,&nbsp;Haijian Li","doi":"10.1016/j.jmsy.2026.01.017","DOIUrl":"10.1016/j.jmsy.2026.01.017","url":null,"abstract":"<div><div>Prediction-based graph structure learning enhances both prediction accuracy and interpretability by identifying the underlying causes of prediction fluctuations, making it particularly valuable for industrial process monitoring. However, industrial data often exhibit strong spatio-temporal heterogeneity due to the presence of diverse physical measurements and redundant sensor placements, posing significant challenges for effective graph structure learning. Furthermore, when increasing the look-back window length to improve prediction accuracy, the heterogeneity of time series introduces more noise, making it difficult for graph structure learning methods to establish effective edge connections. Meanwhile, homogeneous time series provide redundant spatial features, causing prediction-based graph structure learning methods to fail. This paper is the first to study how to control the learned graph structure density in a multivariate time series prediction model to achieve a reasonable balance between prediction accuracy and structural accuracy. This paper proposes a Spatial Information Bottleneck (SIB) method to simultaneously address the aforementioned two challenges. The SIB method introduces the spatial feature prioritization principle, whereby the prediction model preferentially utilizes neighborhood node features for forecasting in homogeneous time series pairs, thereby enabling graph structure learning to establish connections between homogeneous time series pairs. Second, SIB performs independent information compression on each time series feature, which suppresses prediction-irrelevant noise in heterogeneous time series to varying degrees, thereby mitigating the impact of noise on prediction accuracy under long-sequence inputs. Experiments on industrial process data with accessible ground truth graph structures show that the model based on this method not only enhances prediction accuracy but also generates graph structures that align with physical processes for result interpretation.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 441-454"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187919","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":"Explicating visual tacit knowledge in industrial welding inspection with context-aware cognitive pathway graph","authors":"Ji Wang,&nbsp;Weibin Zhuang,&nbsp;Xing Wu,&nbsp;Congmao Chen,&nbsp;Jinsong Bao,&nbsp;Xinyu Li","doi":"10.1016/j.jmsy.2026.02.001","DOIUrl":"10.1016/j.jmsy.2026.02.001","url":null,"abstract":"<div><div>The profound reliance of industrial smart manufacturing on human expert experience has emerged as a critical bottleneck, as traditional methods struggle to effectively computationalize the deep, contextualized tacit knowledge inherent in expert visual intuition. To address this challenge, this paper proposes a systematic methodology for the explicitation and contextualized modeling of expert Visual Tacit Knowledge. First, to address the foundational challenge of formalizing expert intuition, this work defines Visual Tacit Knowledge and its transformation pathway from tacit intuition to explicit rules, and introduces Weld-VTK, a multimodal dataset for welding inspection that provides a solid data foundation. Second, an explicit analysis method is proposed to distill structured attention cues from unstructured raw visual behavior, providing the critical structured input needed for establishing contextual associations. Finally, to model the expert’s cognitive process, a Visual Chain-of-Thought is introduced, leveraging Large Language Models to establish contextual semantic associations between cues. These chains are then aggregated to construct a hierarchical Context-Aware Cognitive Pathway Graph, completely reconstructing the expert’s cognitive strategy. Quantitative results demonstrate that the proposed method outperforms baseline models, and expert evaluations confirm its exceptional performance in causal validity and diagnostic precision. This methodology provides a new paradigm for the contextualized modeling and structured explicitation of expert Visual Tacit Knowledge.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 366-385"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187923","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":"Integrating data and domain knowledge for predictive intelligence: A comprehensive review of DKF-DPM in intelligent manufacturing","authors":"Zheng Ren ,&nbsp;Yutao Chen ,&nbsp;Zihao Zhu ,&nbsp;Linhuhu Nong ,&nbsp;Wenyu Yang ,&nbsp;Junyong Qiu ,&nbsp;Tianhua Ling","doi":"10.1016/j.jmsy.2026.02.003","DOIUrl":"10.1016/j.jmsy.2026.02.003","url":null,"abstract":"<div><div>Data and knowledge fusion-driven predictive model (DKF-DPM) has garnered significant attention for their ability to achieve high accuracy and robustness in complex manufacturing scenarios. By integrating data-driven learning with physical and domain knowledge, DKF-DPM is capable of more reliable modeling and prediction of nonlinear, multi-source and highly uncertain processes. This review systematically surveys recent advances of DKF-DPM in intelligent manufacturing, focusing on their modeling frameworks, representative applications in failure and fatigue life, cutting force and residual stress, machining quality and optimal processing parameters. In addition, current limitations and future research directions are discussed to highlight key challenges and opportunities. Overall, this study concludes that the integration of data-driven methods with domain knowledge is a critical pathway toward developing more reliable, interpretable, and adaptive predictive systems for intelligent manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 338-365"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive framework for computationally efficient system-level design optimization of machine tools","authors":"Deniz Bilgili ,&nbsp;Erhan Budak ,&nbsp;Jasmin Jelovica","doi":"10.1016/j.jmsy.2026.02.005","DOIUrl":"10.1016/j.jmsy.2026.02.005","url":null,"abstract":"<div><div>Mass reduction of machine tool components is a crucial task that can improve performance, accuracy, and energy efficiency. System-level optimization, where multiple components are simultaneously optimized, is noted in the literature as a challenging necessity for complete performance improvement of machine tools. Existing methods focus on optimizing the machine tool components individually, neglecting the critical effects of simultaneous modification on the machine performance and thorough exploration of the design space. To the authors’ knowledge, for the first time in the literature, this paper presents a comprehensive framework for system-level machine tool design optimization considering the most significant multi-objective performance indicators for the machining process. Static and dynamic stiffness, thermal and dynamic stability, and fatigue life are evaluated as performance indicators using a multi-objective finite element response set that includes coupled thermal-structural, modal, and frequency response analyses. A minimal parameter set approach is proposed which uses the linear guide joints to minimize the number of design variables, addressing the challenge of increased computational cost in system-level modeling. Machine responses during optimization iterations are predicted by a machine learning model trained on the machine tool’s multi-objective finite element response set, achieving higher accuracy than commonly used polynomial-based methods. A constraint relaxation method is proposed that permits limited degradation relative to the base design, yielding designs that substantially outperform those obtained from unconstrained optimization while avoiding over-constraining. Up to 20 % mass reduction is achieved across the machine tool components while the performance indicators are either improved or maintained with negligible degradation.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 419-440"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146188651","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":"Real-time dynamic integrated process planning and scheduling with reconfigurable manufacturing cells via multi-agent reinforcement learning","authors":"Liang Zheng ,&nbsp;Xiaodi Chen ,&nbsp;Jianhua Liu ,&nbsp;Cunbo Zhuang","doi":"10.1016/j.jmsy.2026.01.004","DOIUrl":"10.1016/j.jmsy.2026.01.004","url":null,"abstract":"<div><div>Amid the transformation driven by Industry 4.0 and 5.0, manufacturing is rapidly advancing toward greater intelligence and flexibility. Reconfigurable Matrix-structured Manufacturing Systems (RMMS) improve adaptability through dynamic structural and resource reconfiguration, while Integrated Process Planning and Scheduling (IPPS) jointly optimizes process routes and scheduling for optimal resource allocation and responsiveness. This study focuses on Dynamic IPPS with Reconfigurable Manufacturing Cells (DIPPS-RMC) in RMMS, and proposes a real-time scheduling approach based on multi-agent Proximal Policy Optimization (PPO) to reduce average tardiness and enhance system efficiency. A Mixed Integer Linear Programming model is established to address the complexity of process flows and dynamic scheduling, providing a solid theoretical foundation. The scheduling problem is further formulated as a Partially Observable Markov Decision Process to capture the uncertainty and partial observability of real manufacturing environments. To alleviate the credit assignment problem and enhance inter-agent coordination, a delayed reward-sharing mechanism is designed. A multi-agent PPO algorithm with centralized training and decentralized execution is introduced, leveraging parallel environment sampling to improve training efficiency and generalization. Extensive experiments on 270 cases across 27 scenarios show that the proposed method outperforms state-of-the-art multi-agent reinforcement learning algorithms in training speed, generalization, and scheduling performance. Its application to real-world cases further demonstrates effective handling of dynamic job arrivals and RMC breakdowns, validating its robustness and practical utility. These results confirm the method’s effectiveness and applicability in dynamic, complex manufacturing environments, offering an innovative solution for real-time scheduling in RMMS.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 127-154"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978606","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":"The SHOP4CF modular reference architecture for flexible process-oriented, data-driven smart manufacturing","authors":"Paul Grefen ,&nbsp;Michał Zimniewicz ,&nbsp;Irene Vanderfeesten ,&nbsp;Kostas Traganos ,&nbsp;Pieter Becue ,&nbsp;Anders Pedersen ,&nbsp;Genessis Perez Rivera","doi":"10.1016/j.jmsy.2026.01.010","DOIUrl":"10.1016/j.jmsy.2026.01.010","url":null,"abstract":"<div><h3>Context</h3><div>Organizations in the smart industry domain face an increasing complexity of their functions and processes, both in the intra- and inter-organizational scopes. This has a direct effect on the digital systems that support their operations: they grow more complex too. At the same time, the organizations need to increase their agility: they must be able to flexibly adapt their processes to market changes. Especially SMEs in the manufacturing domain get lost in this combination of complexity and changeability.</div></div><div><h3>Objectives</h3><div>To help SME organizations in the smart manufacturing domain with their digital transformation, we develop the SHOP4CF modular reference architecture for digital manufacturing support.</div></div><div><h3>Methods</h3><div>We develop the SHOP4CF base architecture in an iterative way by application and evaluation in 36 real-world industrial cases, organized in three waves. We base our design partly on successful existing work, specifically the outcomes of the HORSE EU project, and align it with main manufacturing standards like ISA-95 and RAMI 4.0. We next distill the SHOP4CF reference architecture by abstracting the SHOP4CF base architecture, based on explicit design principles. We then specialize the reference architecture for process-oriented and data-driven manufacturing.</div></div><div><h3>Results</h3><div>The result of our work is a modular, flexible software reference architecture for smart manufacturing solutions. To facilitate its use, the reference architecture is coupled with manufacturing software life cycle models. Centered on a component marketplace, the life cycle for functional module developers is linked to the life cycle for module users, including explicit attention to the role of technology integrators. To illustrate its applicability, we describe three application cases in this paper.</div></div><div><h3>Conclusion</h3><div>The reference architecture provides a demonstrated point of departure for SMEs in the manufacturing domain to design their digital support in a complex and dynamic industry ecosystem. The modularity of the architecture and its coupling to software life cycles provide a new level of flexibility.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 227-247"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035034","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":"A4PS: Agentic AI-assisted advanced planning and scheduling with large language models for smart manufacturing","authors":"Mingxing Li ,&nbsp;Qu Zhou ,&nbsp;Wanshan Li ,&nbsp;Ting Qu ,&nbsp;Maolin Yang ,&nbsp;Pingyu Jiang","doi":"10.1016/j.jmsy.2026.01.003","DOIUrl":"10.1016/j.jmsy.2026.01.003","url":null,"abstract":"<div><div>Advanced Planning and Scheduling (APS) for manufacturing systems is becoming more complex against the backdrop of intelligent transformation and increasing demand for customisation. In real-world APS applications subject to multi-source dynamics, objective alterations, constraints removals/additions, algorithm upgrades are inevitable. Such structural changes of APS, requiring seamless coordination among experts such as production managers, modelling engineers, algorithm developers, are often lengthy and less flexible. This poses new challenges in cross-domain/inter-process coordination and rapid multi-disciplinary knowledge integration/reuse. This paper proposes a novel <strong><em>A</em></strong><em>gentic</em> <strong><em>A</em></strong><em>I-</em><strong><em>A</em></strong><em>ssisted</em> <strong><em>APS</em></strong> (A⁴PS) framework, utilising Large Language Models (LLMs) and agents to assist modification/update processes of APS. Firstly, a multi-agentic AI-enabled workflow is designed following standard operating procedure of APS to facilitate the cross-domain agent coordination. Secondly, a multi-step knowledge augmentation method is proposed to endow LLM agents with specialised APS knowledge. Thirdly, a Retrieval-Augmented Generation (RAG) and Chain of Thought (CoT)-enhanced method is developed for knowledge use and interaction. Experiments are conducted with an APS dataset which is created based on classical APS cases and manufacturing researchers. Compared with basic LLMs, A⁴PS exhibited substantially superior performance across both basic and complex cases in metrics such as modelling task success rate, absolute percentage error of solution results, optimisation algorithm code logic completion rate, and code executability rate. Case study demonstrates that A⁴PS enables LLMs to coordinate, learn APS knowledge, and imitate experts in the reasoning process, and ultimately realise APS assistance using natural language. This work proposes a novel solution that uses LLMs and agentic AI to assist APS modification/update process, contributing to AI-driven smart manufacturing in Industry 4.0.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 207-226"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978607","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 collaborative process parameter recommender system for fleets of networked manufacturing machines — with application to 3D printing","authors":"Sicong Guo ,&nbsp;Weishi Wang ,&nbsp;Chenhuan Jiang ,&nbsp;Mohamed Elidrisi ,&nbsp;Myungjin Lee ,&nbsp;Harsha V. Madhyastha ,&nbsp;Raed Al Kontar ,&nbsp;Chinedum E. Okwudire","doi":"10.1016/j.jmsy.2025.12.028","DOIUrl":"10.1016/j.jmsy.2025.12.028","url":null,"abstract":"<div><div>Fleets of networked manufacturing machines of the same type, that are collocated or geographically distributed, are growing in popularity. An excellent example is the rise of 3D print farms, which consist of multiple networked 3D printers operating in parallel, enabling faster production and efficient mass customization. However, optimizing process parameters across a fleet of manufacturing machines, even of the same type, remains a challenge due to machine-to-machine variability. Traditional trial-and-error approaches are inefficient, requiring extensive testing to determine optimal process parameters for an entire fleet. In this work, we introduce a machine learning-based collaborative recommender system that optimizes process parameters for each machine in a fleet by modeling the problem as a sequential matrix completion task. Our approach leverages spectral clustering and alternating least squares to iteratively refine parameter predictions, enabling real-time collaboration among the machines in a fleet while minimizing the number of experimental trials. We validate our method using a mini 3D print farm consisting of ten 3D printers for which we optimize acceleration and speed settings to minimize surface roughness and printing time, thus maximizing print quality and productivity. Our approach achieves significantly faster convergence to optimal process parameters relative to a comparable non-collaborative technique.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"85 ","pages":"Pages 22-33"},"PeriodicalIF":14.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145882953","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}