Journal of Manufacturing Systems最新文献

{"title":"Sustainability assessment for machining processes based on progressive analysis of critical sources","authors":"Junjie Hu ,&nbsp;Wei Zhao ,&nbsp;Liang Li ,&nbsp;Ning He ,&nbsp;Muhammad Jamil ,&nbsp;Aqib Mashood Khan ,&nbsp;Chao Wang ,&nbsp;Xiaowei Zheng","doi":"10.1016/j.jmsy.2025.12.002","DOIUrl":"10.1016/j.jmsy.2025.12.002","url":null,"abstract":"<div><div>The machining processes must achieve sustainability due to growing ecological concerns and energy crises. As an effective tool, sustainability assessment guides the implementation of sustainable strategies in machining processes. However, the complex resource consumption across machining processes and the coupling effects among machining parameters have greatly hindered its implementation. To address this challenge, this study proposes a sustainability assessment method based on progressive analysis of critical sources, enabling the identification and evaluation of key factors influencing sustainability. First, critical sources are identified through quantification of contribution degrees and sensitivity analysis. Progressive analysis is employed to focus resources on in-depth research into the fundamental characteristics and operational mechanisms of critical sources, establishing specialized indicators such as specific embodied energy and specific carbon emissions for cutters. Subsequently, a sustainable soft sensor is developed to enable efficient and cost-effective sustainability assessment. Finally, a milling case study incorporating various tool types and cooling-lubrication strategies demonstrates the method’s effectiveness in comprehensively capturing the coupling effects inherent in machining processes. The results confirm the method’s reliability and clearly validate its capability to evaluate sustainability performance in machining. This study not only provides technical support for sustainability assessments but also delivers actionable insights to facilitate the implementation of sustainable machining strategies.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 289-313"},"PeriodicalIF":14.2,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786667","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":"Resilience-enhancing multi-strategy decision-making for dynamic scheduling in manufacturing systems","authors":"Xin Guo ,&nbsp;Mingyue Yang ,&nbsp;Pai Zheng ,&nbsp;Jiewu Leng ,&nbsp;Chong Chen ,&nbsp;Kai Zhang ,&nbsp;Jun Li ,&nbsp;Zechuan Huang","doi":"10.1016/j.jmsy.2025.12.009","DOIUrl":"10.1016/j.jmsy.2025.12.009","url":null,"abstract":"<div><div>High-impact disruptions can cause significant performance degradation and even failures in manufacturing systems. Resilient manufacturing systems can absorb such disruptions, adapt to changing environments, and accelerate recovery through strategy scheduling based on real-time performance data. However, the nonlinear nature of degradation processes can lead to deviations from expected recovery outcomes and delays in strategy scheduling, which makes strategy scheduling for repairing manufacturing systems a difficult decision-making problem. Therefore, a resilience-enhancing multi-strategy decision-making for dynamic scheduling model in manufacturing systems is proposed, aiming to determine the optimal strategy and reduce performance anomaly duration. First, a component-based evaluation method is proposed to measure the absorption, adaptation, and recovery capabilities of the system, achieving real-time analysis of resilience levels. Then, a dynamic strategy scheduling method based on Markov chains is proposed to plan strategies and predict trajectories based on the real-time performance status, disruption, and resilience level, which solves the nonlinearity changes of performance state. Finally, a multi-strategy decision-making method based on fuzzy-BWM is proposed to achieve the resilient-oriented multi-objective discrete strategy decision-making, considering cost, recovery time, and recovery degree. The die forging press is used to demonstrate the effectiveness of the proposed model. The results show that the strategy decided by the model enables the system to recover quickly to its expected state with an acceptable cost compared to other strategies.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 269-288"},"PeriodicalIF":14.2,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A deep reinforcement learning approach driven by temporal knowledge graph for dynamic scheduling in discrete manufacturing workshops","authors":"Xinyu Liu ,&nbsp;Yunlei Zan ,&nbsp;Honghui Wang ,&nbsp;Xu Han ,&nbsp;Guijie Liu","doi":"10.1016/j.jmsy.2025.12.008","DOIUrl":"10.1016/j.jmsy.2025.12.008","url":null,"abstract":"<div><div>Against the background of low-carbon and efficient sustainable manufacturing, production scheduling must not only focus on efficiency and delivery times but also balance energy consumption and carbon emission constraints. However, modern manufacturing workshops commonly adopt a model where batch production and trial production coexist. Production orders and equipment load evolve over time, with frequent occurrences of order insertion, maintenance. At the same time, workshop data exhibits multisource, heterogeneous, and time-varying characteristics. It leads to underutilized production information and delayed scheduling decisions, resulting in efficiency losses and redundant energy consumption. To address the above issues, a deep reinforcement learning approach driven by temporal knowledge graph is proposed in this paper. Firstly, a knowledge graph-based workshop scheduling framework is established to perform unified semantic modeling of production factors and the scheduling relationships. It constructs a multidimensional information matrix with temporal rule constraints. Then, a temporal knowledge-driven LSTM-TD3 algorithm is proposed by replacing the original policy network with an LSTM and employing dual Q-networks with policy delay updates to improve training convergence in continuous action spaces. On this basis, an adaptive weighting mechanism for reward functions targeting different production events is defined to achieve a reasonable balance and dynamic decision-making among multiple objectives. Finally, a case study is conducted with a boiler screen tube production line, and a prototype system is built. The results indicate that compared to historical production data, the average energy consumption of production line equipment decreased by 4.10 %, and the overall efficiency of the workshop increased by 14.29 %, validating the effectiveness of this approach.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 242-258"},"PeriodicalIF":14.2,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A cradle-to-cradle life cycle assessment framework linking machining parameters, tool life and part durability","authors":"I. Rodriguez ,&nbsp;P.J. Arrazola ,&nbsp;M. Mori ,&nbsp;G. Ortiz-de-Zarate ,&nbsp;A. Madariaga ,&nbsp;M. Cuesta ,&nbsp;F. Pušavec","doi":"10.1016/j.jmsy.2025.11.021","DOIUrl":"10.1016/j.jmsy.2025.11.021","url":null,"abstract":"<div><div>Machining operations influence sustainability not only through immediate energy and resource use but also through their effects on tool longevity and part durability. Conventional life cycle assessments (LCAs) of machining typically adopt gate-to-gate boundaries, overlooking how machining parameters affect surface integrity, fatigue life, and the frequency of cutting-tool and machined component replacement. This study develops a novel cradle-to-cradle LCA framework that incorporates machining-induced tool wear and part durability into system-level environmental impacts. The approach is demonstrated through a real case study of drilling the CFRP/Ti6Al4V stacks used for the Boeing 787 fuselage, comparing dry drilling with liquid carbon dioxide combined with minimum quantity lubrication (LCO₂+MQL). Experimental tests quantified energy demand, resource consumption, tool life, and hole quality, while part durability was evaluated via fatigue testing and finite element simulations. Results show that the cutting tool production was the dominant contributor to the overall environmental impact. Despite requiring additional auxiliary inputs, LCO₂+MQL drilling reduced overall environmental impacts by 60–70 % relative to dry drilling, due to extended tool life and improved component service life. These findings demonstrate that coolant-assisted machining, when durability and tool-life effects are considered, yields a net environmental benefit. The proposed framework provides a transferable method to expand LCAs beyond gate-to-gate boundaries by integrating the influence of machined part quality on durability and in-service repairs, enabling cradle-to-cradle assessments that better capture the system-level implications of machining decisions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 207-222"},"PeriodicalIF":14.2,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Frequency-aware and bionic-aligned collaborative modeling for cross-domain tool wear monitoring under small-sample conditions","authors":"Yezhen Peng ,&nbsp;Weimin Kang ,&nbsp;Qirui Hu ,&nbsp;Fengwen Yu ,&nbsp;Wenhong Zhou ,&nbsp;Xinhua Yao ,&nbsp;Congcong Luan ,&nbsp;Songyu Hu ,&nbsp;Jianzhong Fu","doi":"10.1016/j.jmsy.2025.11.025","DOIUrl":"10.1016/j.jmsy.2025.11.025","url":null,"abstract":"<div><div>Tool wear monitoring is crucial for optimizing CNC machining processes in next-generation intelligent manufacturing systems. However, existing methods struggle to capture the dynamic relationship between high-frequency features and wear evolution. Small-sample training and the uneven distribution of labels across the domain exacerbate bias in feature migration, limiting model generalizability and adaptability. To address this, a frequency domain-aware and bionic-aligned collaborative modeling approach for domain shift mitigation is proposed. Firstly, a smoothed wavelet convolution feature extraction method is introduced, enhancing the capture of sensitive frequency bands and stabilizing gradient propagation through a Softplus smoothing mechanism. The method’s ability to suppress domain offset during the initial feature extraction stage is validated by comparing feature activation distributions across two domains. Inspired by bat echolocation, an attention mechanism is proposed that integrates energy guidance, echo alignment, and time-frequency focusing modules to enhance high-frequency signal mapping and mitigate domain shift. The method's effectiveness in high-frequency feature response is validated through enhancement metrics and variance distribution within the attention focus region. Additionally, interpretability of dual-domain feature alignment is improved by calculating working condition similarity, integrating a priori knowledge, and optimizing the MMD loss function. Systematic ablation experiments demonstrate that the proposed method achieves average RMSE, MAE, and R² values of 0.078, 0.063, and 0.817, respectively. It outperforms all ablation models, yielding average reductions of 31.6 % and 32.5 % in RMSE and MAE, and an average improvement of 42.7 % in R². Furthermore, the proposed method outperforms the best-performing method among the four mainstream methods, reducing RMSE and MAE by 13.3 % and 2.5 %, and improving R² by 5.1 %. This method effectively suppresses domain bias in feature extraction, mapping, and training under small sample conditions, providing critical technical support for intelligent manufacturing in complex, variable working environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 223-241"},"PeriodicalIF":14.2,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey for real-world industrial surface defect detection: Challenges, approaches, and prospects","authors":"Yuqi Cheng ,&nbsp;Yunkang Cao ,&nbsp;Haiming Yao ,&nbsp;Wei Luo ,&nbsp;Cheng Jiang ,&nbsp;Hui Zhang ,&nbsp;Weiming Shen","doi":"10.1016/j.jmsy.2025.11.022","DOIUrl":"10.1016/j.jmsy.2025.11.022","url":null,"abstract":"<div><div>Industrial surface defect detection is vital for upholding product quality across contemporary manufacturing systems. As the expectations for precision, automation, and scalability intensify, conventional inspection approaches are increasingly found wanting in addressing real-world demands. Notable progress in computer vision and deep learning has substantially bolstered defect detection capabilities across both 2D and 3D modalities. A significant development has been the pivot from closed-set to open-set defect detection frameworks, which diminishes the necessity for extensive defect annotations and facilitates the recognition of novel anomalies. Despite such strides, a cohesive and contemporary understanding of industrial defect detection remains elusive. Consequently, this survey delivers an in-depth analysis of both closed-set and open-set defect detection strategies within 2D and 3D modalities, charting their evolution in recent years and underscoring the rising prominence of open-set techniques. We distill critical challenges inherent in practical detection environments and illuminate emerging trends, thereby providing a current and comprehensive vista of this swiftly progressing field.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 152-172"},"PeriodicalIF":14.2,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep unsupervised learning-based supplier selection and ranking for assembly manufacturing","authors":"Suyoung Park,&nbsp;Shreyes N. Melkote","doi":"10.1016/j.jmsy.2025.12.004","DOIUrl":"10.1016/j.jmsy.2025.12.004","url":null,"abstract":"<div><div>Conventional supplier selection methods for assembled products have primarily relied on qualitative or business-level assessments of supplier capabilities, since manufacturing-related metrics such as product geometry, cost, time, and tolerance are heterogeneous and difficult to integrate into a unified evaluation. This reliance makes the identification of suppliers with adequate manufacturing capability particularly challenging as global supply chains grow increasingly complex. To address this gap, we propose the Deep Unsupervised Assembly Supplier Matcher (DU-ASM), an integrated data-driven framework that jointly embeds geometry, topology, and quantitative manufacturing attributes into a unified latent space for assembly-level supplier selection and ranking. Leveraging a graph autoencoder, DU-ASM reconstructs manufacturing attributes and supports robust supplier selection even with incomplete inputs. Experimental validation across multiple case studies demonstrates that DU-ASM achieves over 95 % supplier selection accuracy under complete requirements and over 90 % with partially masked inputs, while attaining mean normalized Discounted Cumulative Gain scores at top-k positions (nDCG@k) exceeding 0.99 in ranking tasks. By linking geometric, topological, and quantitative data, DU-ASM demonstrates both methodological novelty and strong quantitative performance, providing a scalable foundation for supplier matching at the assembly level and supporting multi-tier decision-making in future manufacturing supply networks.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 173-188"},"PeriodicalIF":14.2,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786758","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":"Process mining-driven modeling and simulation to enhance fault diagnosis in cyber–physical systems","authors":"Francesco Vitale ,&nbsp;Nicola Dall’Ora ,&nbsp;Sebastiano Gaiardelli ,&nbsp;Enrico Fraccaroli ,&nbsp;Nicola Mazzocca ,&nbsp;Franco Fummi","doi":"10.1016/j.jmsy.2025.12.005","DOIUrl":"10.1016/j.jmsy.2025.12.005","url":null,"abstract":"<div><div>Cyber–Physical Systems (CPSs) tightly interconnect digital and physical operations within production environments, enabling real-time monitoring, control, optimization, and autonomous decision-making that directly enhance manufacturing processes and productivity. The inherent complexity of these systems can lead to faults that require robust and interpretable diagnoses to maintain system dependability and operational efficiency. However, manual modeling of faulty behaviors requires extensive domain expertise and cannot leverage the low-level sensor data of the CPS. Furthermore, although powerful, deep learning-based techniques produce black-box diagnostics that lack interpretability, limiting their practical adoption. To address these challenges, we set forth a method that performs unsupervised characterization of system states and state transitions from low-level sensor data, uses several process mining techniques to model faults through interpretable stochastic Petri nets, simulates such Petri nets for a comprehensive understanding of system behavior under faulty conditions, and performs Petri net-based fault diagnosis. The method begins with detecting collective anomalies involving multiple samples in low-level sensor data. These anomalies are then transformed into structured event logs, enabling the data-driven discovery of interpretable Petri nets through process mining. By enhancing these Petri nets with timing distributions, the approach supports the simulation of faulty behaviors. Finally, faults can be diagnosed online by checking collective anomalies with the Petri nets and the corresponding simulations. The method is applied to the Robotic Arm Dataset (RoAD), a benchmark collected from a robotic arm deployed in a scale-replica smart manufacturing assembly line. The application to RoAD demonstrates the method’s effectiveness in modeling, simulating, and classifying faulty behaviors in CPSs. The modeling results demonstrate that our method achieves a satisfactory interpretability-simulation accuracy trade-off with up to 0.676 arc-degree simplicity, 0.395 R<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, and 0.088 RMSE. In addition, the fault identification results show that the method achieves an F1 score of up to 98.925%, while maintaining a low conformance checking time of 0.020 s, which competes with other deep learning-based methods.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 189-206"},"PeriodicalIF":14.2,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786757","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":"PI-KAF: A physics-informed constrained online interpretable monitoring method for tool wear","authors":"Liguo Zhang ,&nbsp;Qinghua Song ,&nbsp;Haifeng Ma ,&nbsp;Zhanqiang Liu","doi":"10.1016/j.jmsy.2025.11.024","DOIUrl":"10.1016/j.jmsy.2025.11.024","url":null,"abstract":"<div><div>Deep learning-based tool wear monitoring methods often suffer from poor interpretability, parameter redundancy, and limited cross-domain generalization, especially under variable operating conditions and when only small sample sizes are available. These limitations hinder their practical deployment in practical manufacturing environments. To overcome these challenges, this study proposes a highly interpretable and lightweight tool wear monitoring framework tailored for small-sample, variable-condition scenarios. The method randomly extracts fixed-length segments from vibration signals to construct small-sample datasets, utilizing both signal envelope spectrum and cutting time as input features. Guided by the degradation law of tool wear, explicit physical constraints are imposed on the solution space of a Kolmogorov‑Arnold Fourier neural network, yielding a physics‑informed data‑driven model. SHAP analysis is employed to quantify the contribution of each feature, enhancing model transparency. Validation on public datasets under both single‑ and multi‑condition settings demonstrates that the proposed method delivers excellent performance across diverse operating conditions, achieving a stable prediction R² of up to 95 %, an inference latency of only 2 ms, and a reduction of approximately 90 % in model parameters. This solution can be integrated into the edge computing platform of CNC systems, making it particularly suitable for machining scenarios with high real-time requirements. It offers a lightweight, precise, and efficient monitoring capability for smart factories, contributing simultaneously to improvements in product quality and manufacturing efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 135-151"},"PeriodicalIF":14.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786760","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":"Towards perceptive assembly: An edge vision network-enabled augmented reality (AR) monitoring method for global shape and mechanical information in large aerospace components","authors":"Yang Zhang,&nbsp;Xu Wang,&nbsp;Jiacheng Cui,&nbsp;Yulin Jin,&nbsp;Qihang Chen,&nbsp;Yongkang Lu,&nbsp;Wei Liu","doi":"10.1016/j.jmsy.2025.11.017","DOIUrl":"10.1016/j.jmsy.2025.11.017","url":null,"abstract":"<div><div>Real-time, high-fidelity, and interactive monitoring of global mechanical responses during the assembly of large-scale, flexible aerospace structures remains a critical and unresolved challenge. Here, we present a perceptive assembly framework that integrates a distributed edge vision network, physics-informed sparse sensing, and immersive augmented reality (AR) visualization to enable full-field structural state monitoring. A modular edge sensing system is deployed to achieve fast, high-precision measurement of distributed displacements across meter-scale components. To overcome view discontinuities, a hierarchical coordinate transformation pipeline is introduced for global registration under non-overlapping camera views. Building on sparse displacement data, we develop a constrained sensor optimization strategy that enables real-time reconstruction of global displacement and strain fields. Through HoloLens 2, the system provides intuitive AR overlays that deliver immersive, in-situ mechanical feedback during assembly. Validation experiments on composite panels demonstrate sub-millimeter reconstruction accuracy and real-time performance, significantly enhancing transparency and decision-making in the assembly process. This work establishes a scalable AR-based perception infrastructure for next-generation intelligent manufacturing of large aerospace structures.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"84 ","pages":"Pages 117-134"},"PeriodicalIF":14.2,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145786759","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}