Journal of Manufacturing Systems最新文献

{"title":"Deposition sequence optimization for minimizing substrate plate distortion using the simplified WAAM simulation","authors":"Xiao Fan Zhao,&nbsp;Hannes Panzer,&nbsp;Avelino Zapata,&nbsp;Felix Riegger,&nbsp;Siegfried Baehr,&nbsp;Michael F. Zaeh","doi":"10.1016/j.jmsy.2025.05.010","DOIUrl":"10.1016/j.jmsy.2025.05.010","url":null,"abstract":"<div><div>Wire arc additive manufacturing (WAAM) is a viable alternative to conventional machining or other additive manufacturing technologies, especially for the production of large, thin-walled components. However, finding the optimal deposition sequence for a minimal substrate plate distortion is challenging due to the vast number of possible sequences. The present study tackles this challenge by exploring three distinct objective functions for predicting distortion using the simplified WAAM simulation (SWS) – a semi-analytical model for the time-efficient estimation of thermal histories in WAAM parts. Using the SWS together with three temperature-based objective functions, distortion scores were calculated for each deposition sequence of a four-sectioned wall geometry. A subset of deposition sequences was then simulated using an experimentally validated thermomechanical finite element (FE) simulation. The correlation between the simulated distortion from the FE model and the distortion score from each objective function was analyzed. The results implied a strong and definitive statistical correlation between the substrate plate distortion and one particular objective function which considers the thermal eccentricity. Subsequently, the wall geometry, together with an additional A-shaped geometry, was manufactured using the best, the worst, and a third deposition sequence. After the WAAM process, the substrate plate distortions were measured using a 3D scanner. The scan results validated the prior optimization, indicating the highest distortion for the worst sequence, the lowest distortion for the best sequence, and a level of distortion in between those extremes for the third sequence. The findings of this article can be utilized for the preliminary selection of deposition sequences of WAAM parts.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"81 ","pages":"Pages 103-116"},"PeriodicalIF":12.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital twin modeling of the robotic gluing system for predicting the quality of glue lines and optimizing gluing parameters","authors":"Ruihao Kang ,&nbsp;Junshan Hu ,&nbsp;Mingyu Li ,&nbsp;Qi Zhang ,&nbsp;Xingtao Su ,&nbsp;Zhengping Li ,&nbsp;Wei Tian","doi":"10.1016/j.jmsy.2025.05.008","DOIUrl":"10.1016/j.jmsy.2025.05.008","url":null,"abstract":"<div><div>Digital twin (DT) technology is changing the current pattern of intelligent manufacturing, it makes up for the shortcomings of process parameter optimization methods to improve real-time and predictability. This paper developed DT models for the robotic gluing system to predict the quality (width and thickness) of glue lines and optimize gluing parameters (trajectory and extrusion speeds). The DT framework based on the geometric, physical, behavioral, and rule models is constructed to monitor and optimize the gluing parameters in real-time. An improved backpropagation neural network (BPNN) prediction model based on whale optimization algorithm (WOA) is established to predict the width and thickness of glue lines from historical and real-time data, while simultaneously enabling real-time calculation of the cross-sectional area of glue lines. A multi-objective optimization model constructed using non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the gluing parameters. The DT prototype of the robotic gluing system has been developed and verified experimentally. The position calibration of the geometric model is used to correct the gluing trajectory before gluing, and the position errors of the gluing points are within ± 0.5 mm. The gluing trajectory is designed to test the effectiveness of the adaptive optimization of gluing parameters. The prediction errors of the width and thickness of the glue line are controlled between ± 0.5 mm and ± 0.3 mm, individually. After parameter optimization, the width and thickness of the glue line at the corner are reduced by 4.53 % and 7.54 %, respectively, thus avoiding glue accumulation. This reduction solves the problem of poor consistency in the quality of glue lines and verifies the feasibility of integrated monitoring, prediction, and optimization based on the DT model.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 1074-1092"},"PeriodicalIF":12.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144088906","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":"Feature points classification of computerized numerical control finishing tool path based on graph neural network","authors":"Jiejun Xie ,&nbsp;Pengcheng Hu ,&nbsp;Yingbo Song ,&nbsp;Xin Liu","doi":"10.1016/j.jmsy.2025.05.015","DOIUrl":"10.1016/j.jmsy.2025.05.015","url":null,"abstract":"<div><div>In Computer Numerical Control (CNC) machining, surface defects occur due to inaccurate recognition of feature points in the finishing tool path and unsmooth feed rate planning by the CNC system. Therefore, accurately identifying feature points of tool path is crucial for high-speed and high-precision CNC machining. Existing algorithms often rely on simple, manually set thresholds and do not consider cross directional geometric information of tool path, leading to poor performance in feature point recognition. This paper presents a new method for identifying and classifying feature points using a graph neural network (GNN) by aggregating geometric features from both the feed and cross directions of the tool path to automatically and accurately identify feature points in finishing tool paths. The method begins by creating a graph-based representation of the tool path, which provides detailed geometric information for Cutter Location (CL) points. It also introduces an algorithm for identifying cross directional points related to CL points and a spatial convolution method that combines feed and cross directional geometric features, based on which a Feature Point-Graph Neural Network (FP-GNN) is constructed. Extensive testing shows that the FP-GNN model performs exceptionally well in classifying tool path feature points, surpassing existing methods. As a direction application of the proposed method, physical machining examples are conducted, demonstrating that optimizing feed rates in the cross direction—based on identified feature points—improves the continuity of the feed rate in both directions, enhancing the surface machining quality.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"81 ","pages":"Pages 75-102"},"PeriodicalIF":12.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099867","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 survey on XR remote collaboration in industry","authors":"Peng Wang ,&nbsp;Huan Yang ,&nbsp;Mark Billinghurst ,&nbsp;Shuang Zhao ,&nbsp;Yao Wang ,&nbsp;Zhou Liu ,&nbsp;Yi Zhang","doi":"10.1016/j.jmsy.2025.05.016","DOIUrl":"10.1016/j.jmsy.2025.05.016","url":null,"abstract":"<div><div>This paper provides a survey of eXtended Reality (XR) remote collaboration in industry. The field of XR remote collaboration is currently at a critical point as collaborative XR systems are becoming more prevalent in industry. Moreover, research on XR remote collaboration in industry is a compelling and evolving field of study, especially because XR technology has recently reached a level of maturity that enables researchers and practitioners to use its capabilities to enhance remote collaboration, rather than focusing solely on the development of the underlying technology. However, to our knowledge, there has yet to be a comprehensive survey on XR remote collaboration in industry. Thus, this paper presents a systematic review of literature published between 2019 and 2024 in this domain. We identified a total of 161 papers, with more than 57 % published in the last three years, and all relevant studies are discussed in detail from fundamental requirements, industrial applications, toolkits/platforms, and system evaluations. Our findings indicate that XR remote collaboration holds great potential across various industrial domains, including design (e.g., design review and co-design), manufacturing (e.g., assembly and inspection), service (e.g., maintenance and monitoring), and training. Since XR remote collaboration is being implemented in real industrial environments, this paper aims to provide a comprehensive academic roadmap and valuable insights into the cutting-edge of XR remote collaboration in industry. This work will also serve as a resource for both current and future researchers who are interested in collaborative XR systems and applications in industry.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"81 ","pages":"Pages 49-74"},"PeriodicalIF":12.2,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099650","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":"SFRGNN-DA: An enhanced graph neural network with domain adaptation for feature recognition in structural parts machining","authors":"Xiaohu Zheng ,&nbsp;Hongbo Chen ,&nbsp;Fangzhou He ,&nbsp;Xiaojia Liu","doi":"10.1016/j.jmsy.2025.05.005","DOIUrl":"10.1016/j.jmsy.2025.05.005","url":null,"abstract":"<div><div>Optimizing the recognition of machining features in structural parts is vital for enhancing the efficiency of NC machining planning and ensuring quality control. However, the inherent complexity and stringent precision requirements of these parts often render existing feature recognition methods inadequate for accurately identifying model features. To address this challenge, a novel graph neural network model (SFRGNN) is introduced. The methodology begins with a specialized feature extraction module that captures both geometric and topological properties of the parts, providing a comprehensive basis for further analysis. Following this, SFRGNN integrates a graph neural network with a Spatial Self-Attention (SSA) module, a configuration designed to enhance the extraction of high-level semantic information crucial for accurately distinguishing machining features. This network architecture allows SFRGNN to interpret complex feature relationships with improved precision. Additionally, an enhanced domain adaptation module (DA) is incorporated to improve SFRGNN’s generalization capabilities and performance in machining feature recognition. Numerous experiments on different data sets confirmed that SFRGNN achieved excellent accuracy in identifying real-world structural part features and demonstrated enhanced performance, which will be helpful for subsequent process planning for part features in real-world scenarios.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"81 ","pages":"Pages 16-33"},"PeriodicalIF":12.2,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144072253","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":"Puzzle mode graph learning with pattern composition relationships reasoning for defect detection of printed products","authors":"Zixun Zhu ,&nbsp;Jie Zhang ,&nbsp;Junliang Wang ,&nbsp;Peng Zhang ,&nbsp;Jiacheng Li","doi":"10.1016/j.jmsy.2025.05.013","DOIUrl":"10.1016/j.jmsy.2025.05.013","url":null,"abstract":"<div><div>Patterns are designs composed of specific elements and are widely present in various printed products, representing particular design intentions. However, due to printing errors, pattern defects are extremely common in these products, significantly impacting their visual quality and market price, especially in high-value customized products like luxury apparel, premium wallpapers and decorative tiles. Traditional detection methods struggle to provide effective judgments with conventional visual cues purely and frequently fall short due to the intricate nature of the pattern composition. To overcome this challenge, we propose a puzzle mode graph learning method capable of reasoning about pattern composition relationships. This novel detection framework simulates the logical reasoning ability of humans in assembling unordered puzzle pieces into a complete pattern, thus surpassing spatial structure limitations and enabling structural defect detection in patterns. Specifically, a parametric representation function is integrated into convolutional layers to enhance the segmentation accuracy of shape masks. Then, cross-graph semantic matching rules are developed to dynamically re-encode the adjacency matrix, enabling the construction of an attribute relationship graph that explicitly describes pattern attributes, including pattern elements, color sequences and shape positions. Moreover, the defective reasoning mechanism calculates puzzle-mode scores to decouple semantic relationships of defect features, inferring anomalous node and edge weights affecting the graph structure, thereby facilitating more precise judgments of pattern defects. Comparative experiments conducted on a real printed defect dataset validate this method. Results demonstrate its effectiveness and robustness in identifying complex pattern defects, providing essential support for appearance quality control in high-end industrial products.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"81 ","pages":"Pages 34-48"},"PeriodicalIF":12.2,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071820","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":"Data-physics collaborativige fusion prediction method for tool remaining useful life based on Mamba state space and physical description","authors":"Caixu Yue,&nbsp;Yiyuan Qin,&nbsp;Xianli Liu,&nbsp;Hao Gu,&nbsp;Shaocong Sun","doi":"10.1016/j.jmsy.2025.05.012","DOIUrl":"10.1016/j.jmsy.2025.05.012","url":null,"abstract":"<div><div>Tool wear is an inherent phenomenon of the metal cutting process, the traditional replacement strategy relies on experience or a fixed cycle easily leads to waste of resources or workpiece damage, accurate prediction of the tool's remaining useful life (RUL) has become a key issue in the field of intelligent manufacturing urgently need to break through. Aiming at the problems of insufficient nonlinear processing capability of physical models and weak interpretability of data-driven models in the existing RUL prediction, this study proposes a data-physics collaborative fusion prediction method for tool remaining useful life based on Mamba state space and physical description. The method breaks through the traditional single-model paradigm and achieves in-depth characterization of the cutting process through a dual modeling mechanism: firstly, a time-series feature extraction network based on the Mamba state space is constructed, and a selective memory mechanism is adopted to achieve the screening of degradation features and non-linear characterization; secondly, a two-stage piecewise physical degradation model is established. The explicit mathematical expressions are deduced based on the geometrical features of the tool wear curve, and the prior distributions of the model parameters are estimated from historical data. The Particle Filter (PF) algorithm is introduced to establish a collaborative optimization mechanism for the dual models, and the physical parameters are dynamically updated through importance sampling to achieve tool RUL prediction under Data-physics collaborative fusion (DPCF). The experimental results show that the method can achieve accurate monitoring of tool RUL and has a certain reference value for efficient tool change in the metal-cutting process.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"81 ","pages":"Pages 1-15"},"PeriodicalIF":12.2,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070792","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":"Advances and innovations in manufacturing systems research 2025","authors":"Xun Xu,&nbsp;Stefania Bruschi,&nbsp;Robert X. Gao","doi":"10.1016/j.jmsy.2025.04.020","DOIUrl":"10.1016/j.jmsy.2025.04.020","url":null,"abstract":"","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 1072-1073"},"PeriodicalIF":12.2,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942452","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":"Multi-view fully connected graph to fuse multi-sensor signals for mechanical equipment remaining useful life prediction","authors":"Jinxin Wu ,&nbsp;Deqiang He ,&nbsp;Zhenzhen Jin ,&nbsp;Ming Zhao ,&nbsp;Xianwang Li ,&nbsp;Yanjun Chen","doi":"10.1016/j.jmsy.2025.05.009","DOIUrl":"10.1016/j.jmsy.2025.05.009","url":null,"abstract":"<div><div>Accurate remaining useful life prediction is essential for enhancing equipment reliability and optimizing maintenance strategies. However, existing methods struggle to effectively integrate multi-sensor data while quantifying uncertainty. To address these challenges, a multi-view fully connected graph neural network is proposed for multi-sensor mechanical equipment remaining useful life prediction. Firstly, local fully connected graphs and global graphs are constructed to comprehensively characterize the multi-view spatial correlations from global and local views. Meanwhile, the graph convolution operations are performed on local and global graphs to extract the intricate spatial dependencies within multi-sensor signals. Then, the learned multi-view spatial representations are fed into the temporal convolutional network to capture the temporal dependencies across sensor timestamps. Finally, a joint optimization network is developed to simultaneously predict the remaining useful life and its associated prediction interval, enabling uncertainty quantification. Extensive experiments on two multi-sensor monitoring degradation datasets demonstrate the superior performance of the proposed model, offering valuable technical support for predictive maintenance.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 1029-1052"},"PeriodicalIF":12.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935156","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":"An explainable multi-layer graph attention network for product completion time prediction in aircraft final assembly lines","authors":"Bolin Chen ,&nbsp;Jie Zhang ,&nbsp;Jun Xiong ,&nbsp;Wenbin Tang ,&nbsp;Shoushan Jiang","doi":"10.1016/j.jmsy.2025.04.018","DOIUrl":"10.1016/j.jmsy.2025.04.018","url":null,"abstract":"<div><div>Predicting product completion time (PCT) is a critical challenge in aircraft manufacturing systems, especially for make-to-order production. This necessitates manufacturers to comprehensively analyze operational state features, including task completion, resource allocation, and material supply, to estimate delivery dates effectively. With the increasing availability of production site perception, data-driven methods for PCT prediction have gained significant attention. However, the coupled interactions among various manufacturing elements, combined with the demand for real-time scheduling in digital twin scenarios, have limited the accuracy and explainability of traditional black-box predictive models. To address these challenges, this paper proposes an explainable multi-layer heterogeneous graph attention network (M-HGAT) customized for predicting PCT in the aircraft final assembly line (AFAL). First, a heterogeneous graph representation method is introduced to model the aircraft assembly status, focusing on the interactions among assembly tasks, materials, and workers. Then, a two-layer state feature aggregation neural network is designed to learn the mapping relationship between the target PCT and input features, incorporating logical and demand constraints among various elements inherent in the aircraft assembly process. Finally, the accuracy and explainability of the proposed model have been validated through an industrial case study focused on PCT prediction. Compared to four benchmark predictive models, the proposed model achieves superior predicted results, reducing the root mean square error by 48 % compared to the best benchmark. Furthermore, the explainability of the M-HGAT is demonstrated through its ability to identify key manufacturing elements and bottleneck assembly stations by analyzing attention weights within the neural network, which provides valuable insights for production managers to optimize AFAL operations and enhance production efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 1053-1071"},"PeriodicalIF":12.2,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935157","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}