Journal of Manufacturing Systems最新文献

{"title":"Cloud-edge-end collaborative multi-process dynamic optimization for energy-efficient aluminum casting","authors":"Weipeng Liu ,&nbsp;Hao Wang ,&nbsp;Pai Zheng ,&nbsp;Tao Peng","doi":"10.1016/j.jmsy.2025.01.013","DOIUrl":"10.1016/j.jmsy.2025.01.013","url":null,"abstract":"<div><div>Casting is a crucial, but energy-intensive aluminum processing technology. To achieve carbon neutrality goals, it is essential to reduce casting energy consumption without compromising productivity. Optimizing operational parameters in aluminum casting is an effective strategy, yet two main challenges remain: understanding the complex relationship between operational parameters and energy consumption, and adapting the optimization process to production dynamics. This paper introduces a cloud-edge-end collaborative predictive-reactive scheduling approach to tackle the second challenge, based on our understanding of the first challenge. Specific dynamic adjustment measures for four common dynamic events, that is, alterations in production plans, fluctuations in pass rates, production interruptions, and deviations from implementation, were proposed. A cloud-edge-end collaborative dynamic adjustment framework is then designed to implement these measures. The proposed approach was tested in a die-casting factory to validate its performance. The results demonstrate that the data-driven approach can generate adjustment measures for detected dynamic events in near-real-time, with the longest response time being less than one minute. These measures significantly reduce casting inventory and energy consumption, achieving a 19.5 % reduction in energy cost during a planned production interruption. The proposed dynamic optimization approach shows promise for energy conservation in the aluminum casting industry.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 217-233"},"PeriodicalIF":12.2,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169027","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":"Manufacturing resource-based self-organizing scheduling using multi-agent system and deep reinforcement learning","authors":"Yuxin Li,&nbsp;Qihao Liu,&nbsp;Xinyu Li,&nbsp;Liang Gao","doi":"10.1016/j.jmsy.2025.01.004","DOIUrl":"10.1016/j.jmsy.2025.01.004","url":null,"abstract":"<div><div>Enterprises are vigorously developing smart factories to meet the approaching mass customization. As a promising control paradigm for smart factories, the self-organizing scheduling mode can build networked manufacturing things. Compared to the global control of traditional scheduling methods, its decentralized control can provide stronger dynamic response and self-regulation capabilities. Therefore, this paper proposes a self-organizing scheduling method based on multi-agent system (MAS) and deep reinforcement learning (DRL) for smart factory. Firstly, a novel MAS with partially decentralized control architecture is established, where the manufacturing resources and cloud are constructed as agents. Then, unlike traditional methods, a self-organizing negotiation mechanism based on contract network protocol is designed for production-logistics collaboration. Considering problem domain knowledge, logistics task bidding of automated guided vehicle agents is based on heuristics, and processing task bidding of machine agents is based on multi-agent DRL. It can ensure the timely delivery of orders, rapid logistics process and efficient production. Finally, machine agents embedded with DRL adopt the centralized training and decentralized execution framework. An action space based on three priorities is designed to ensure the correct bidding of each machine agent and reasonable auction of processing tasks. Experimental results show that compared with scheduling rules, genetic programming and three DRL methods, the proposed method achieves better scheduling performance through reasonable competition of heterogeneous resource agents, and can effectively handle new job arrivals and machine breakdowns.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 179-198"},"PeriodicalIF":12.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170023","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":"Development of an injection molding production condition inference system based on diffusion model","authors":"Joon-Young Kim ,&nbsp;Heekyu Kim ,&nbsp;Keonwoo Nam ,&nbsp;Daeyoung Kang ,&nbsp;Seunghwa Ryu","doi":"10.1016/j.jmsy.2025.01.008","DOIUrl":"10.1016/j.jmsy.2025.01.008","url":null,"abstract":"<div><div>Plastic injection molding is a crucial process for the mass production of various products. However, traditional methods for setting production conditions have heavily relied on skilled operators to adjust parameters through trial and error. This approach is not only inefficient but also results in inconsistent quality control. To address these challenges, this study proposes a new machine learning based model that automatically infers process parameters, enabling real time adaptation to external environmental changes. A surrogate model is first developed to learn the relationship between process parameters, environmental variables, and product quality, predicting whether a given set of parameters will result in a good or defective product. Building on this, a diffusion model, a type of deep generative model, was employed to generate diverse sets of process parameters likely to yield defect free products under specific environmental conditions. The proposed diffusion model outperforms existing generative models such as generative adversarial network (GAN) and variational autoencoder (VAE) in both accuracy and diversity of generated parameters. Notably, the diffusion model achieved an error rate of 1.63%, significantly outperforming GAN and VAE, which exhibited error rates of 23.42% and 44.54%, respectively. Additionally, the applicability of the proposed diffusion model was experimentally validated in a real world testbed. Several experiments conducted under various external environmental conditions demonstrated that the quality of the products produced using the process parameters generated by the diffusion model matched the quality predicted by the model. This study introduces a novel approach to improving both the efficiency and quality of injection molding processes and holds promise for broader applications in manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 162-178"},"PeriodicalIF":12.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170022","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 technology in modern machining: A comprehensive review of research on machining errors","authors":"Xiangfu Fu ,&nbsp;Hongze Song ,&nbsp;Shuo Li ,&nbsp;Yuqian Lu","doi":"10.1016/j.jmsy.2025.01.005","DOIUrl":"10.1016/j.jmsy.2025.01.005","url":null,"abstract":"<div><div>With the evolution of intelligent manufacturing, high-precision CNC machining has become a crucial driver of industrial advancement. Modern manufacturing demands increasingly precise machining, yet traditional methods lack effective means for visualizing and compensating for machining errors in real-time. Digital twin technology offers a breakthrough solution by creating real-time mappings between physical and digital spaces, enabling visualized monitoring and intelligent prediction throughout the machining process. This technology serves five essential functions in machining errors management: real-time identification of high-precision errors, data-driven error modeling using multiple sources, error traceability and decoupling through causal reasoning, real-time error prediction and interaction, and closed-loop adaptive error control and compensation. This study provides a systematic review of digital twin technology's current applications in machining error management, focusing on machining errors identification, modeling, traceability, decoupling, control, and compensation. Future trends in digital twins focus on intelligent error prediction, real-time adaptive control, and multi-source data fusion for CNC machining.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 134-161"},"PeriodicalIF":12.2,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated assembly, measurement, and adjustment method of reconfigurable flexible fixture for aircraft panels based on augmented reality and human-computer interaction","authors":"Xiangrong Zhang ,&nbsp;Shuang Meng ,&nbsp;Binbin Wang ,&nbsp;Lianyu Zheng ,&nbsp;Rui Zhang ,&nbsp;Xufei Li","doi":"10.1016/j.jmsy.2025.01.003","DOIUrl":"10.1016/j.jmsy.2025.01.003","url":null,"abstract":"<div><div>Owing to the characteristics of reconfigurable flexible fixtures (RFFs) for aircraft panels, the automation of their assembly is limited by technology and cost. As a result, manual assembly remains the predominant method. During the manual assembly, workers are affected by several challenges, such as difficulty in understanding the process documents and drawings, distinguishing the assembly positions of the similar components, inefficient data transmission, and determining the adjustment direction of contour board locators (CBLs). These issues arise because of inadequate digital assistance to workers. This paper proposes an integrated assembly, measurement, and adjustment (AMA) method for RFFs based on augmented reality (AR) and human–computer interaction (HCI) to assist workers. First, an information model based on core process elements for the assembly process is constructed. This model clarifies the correlations among multi-source data. Then, measured data is transformed into six-dimensional (6D) parameters to assist in the adjustment of CBLs. Based on the model and 6D parameters, the multiple visual assembly guidance is established by AR virtual-reality fusion technology. Subsequently, the HCI technology is introduced, to adaptively provide guidance via the hand-free head pointer. Workers use the AR head-mounted devices (HMDs) as a medium to interact with the laser tracker, which enables to quickly and accurately obtain the measurement data. Finally, AR and HCI technology are combined to establish an integrated process of AMA of RFFs. This method significantly improves the collaboration between workers and information during the assembly process of RFF. Field-assembly validation demonstrates that, compared with conventional methods, the proposed method achieves a positioning accuracy of ± 0.12 mm and enhances assembly efficiency by 32.87 %.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 117-133"},"PeriodicalIF":12.2,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169567","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":"Systematic AR-based assembly guidance for small-scale, high-density industrial components","authors":"Junhao Geng ,&nbsp;Yuntao Wang ,&nbsp;Yu Cheng ,&nbsp;Xin Zhang ,&nbsp;Yingpeng Xu ,&nbsp;Wenjie Lv","doi":"10.1016/j.jmsy.2025.01.002","DOIUrl":"10.1016/j.jmsy.2025.01.002","url":null,"abstract":"<div><div>Small-scale high-density industrial components (SHIC) are widely used in high-end electromechanical equipment with higher requirements for functional integration. SHIC assembly operation is a typical manual fine operation, which is labor-intensive, time-consuming, and error-prone and seriously affects assembly efficiency and quality consistency. The current augmented reality (AR) technology cannot realize the complete, accurate, and stable cognition and guidance for SHIC because of the single use of visual cognition. Meanwhile, manual preparation of prior knowledge dramatically reduces AR applications’ automation level and practicability. Addressing this research gap, this paper develops a technical framework of AR-based assembly guidance for SHIC, which integrates several novel intelligent methods and deeply combines prior knowledge reasoning and computer vision cognition in a simple and interpretable way. First, computer vision and deep learning are used to automatically generate prior knowledge from the three-dimensional (3D) model of SHIC. Then, 3D tracking, visual recognition, virtual-real matching, and rule reasoning are combined to complete, sequence, and locate the assembly targets in the case of insufficient visual information. Finally, based on the adaptive threshold, two kinds of guidance modes, i.e., position-based precise guidance and region-based heuristic guidance, are automatically switched to realize the AR guidance of the whole assembly process. The case study of complex electrical connectors shows that this framework with novel intelligent methods can meet the industrial requirements of performance, availability, and effectiveness and improve the robustness and efficiency of SHIC assembly operation by 92.95% and 87.06%, respectively. This study realizes AR guidance technology’s systematic and practical application in SHIC assembly operation. Also, it provides a practicable technology architecture for intelligent AR assistance in the fine assembly field.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 86-100"},"PeriodicalIF":12.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169565","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: Real-time full-field deformation twin perception method for large components under parametric load","authors":"Jiacheng Cui,&nbsp;Yang Zhang,&nbsp;Yongkang Lu,&nbsp;Pengbo Yin,&nbsp;Qihang Chen,&nbsp;Lei Han,&nbsp;Wei Liu","doi":"10.1016/j.jmsy.2024.12.013","DOIUrl":"10.1016/j.jmsy.2024.12.013","url":null,"abstract":"<div><div>Enhancing information perception capabilities during the manufacturing and assembly of large-scale components is pivotal for advancing intelligent systems, particularly in the aerospace industry. This paper presents a perceptive assembly approach utilizing a full-field deformation twin perception method based on parametric loads, enabling real-time and accurate reconstruction of deformation fields in large components through a binocular vision system. This method centers around parametric load definitions, proposing the PPOD (Parametric Proper Orthogonal Decomposition) technique for deformation reconstruction, followed by an in-depth analysis of the factors contributing to perception errors. To meet the demands of online deployment, a comprehensive framework is established, deeply integrating measurement instruments, measurement data, and physical models to enhance measurement efficiency and perception robustness. Extensive simulations and experimental results demonstrate that this approach reduces perception errors by over 70% compared to traditional methods, achieving real-time, high-precision, and robust monitoring of deformation in large components. This perceptive assembly framework holds significant promise as a foundational infrastructure for real-time state perception in the intelligent manufacturing of large-scale components.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 101-116"},"PeriodicalIF":12.2,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169566","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 path planning algorithm system for printed display manufacturing using graph convolutional neural network and reinforcement learning","authors":"Jiacong Xiong ,&nbsp;Jiankui Chen ,&nbsp;Wei Chen ,&nbsp;Xiao Yue ,&nbsp;Ziwei Zhao ,&nbsp;Zhouping Yin","doi":"10.1016/j.jmsy.2024.12.016","DOIUrl":"10.1016/j.jmsy.2024.12.016","url":null,"abstract":"<div><div>Inkjet printing technology is considered one of the core components of next-generation display technologies for manufacturing organic light-emitting diode (OLED). However, the patterning process for novel display inkjet printing involves diverse characteristics across different dimensions, such as varying printing scales and resolutions. Existing patterning modules using a single planning algorithm for all inkjet printing scenarios often result in long planning times and unstable planning quality. Therefore, a more comprehensive algorithm system is needed to evaluate inkjet planning problems and select the most suitable planning algorithm. This paper proposes a multi-algorithm integrated online patterning intelligence planning system, which includes three patterning algorithms specific to the inkjet display field and an algorithm selection network based on Proximal Policy Optimization (PPO). We first identify the core metrics of the inkjet planning problem as planning time and solution quality, analyzing how different characteristics of the planning problem affect these metrics. We then propose three algorithms suited to different performance needs: an integer programming method based on graph convolutional neural networks, a binary greedy algorithm, and a maximum contiguous interval search algorithm, each corresponding to high overall performance, high solution quality, and short solution time, respectively, to address complex inkjet planning scenarios. Additionally, the PPO-based algorithm selection network refines the features of the inkjet planning problem to achieve intelligent algorithm selection. Finally, we validate the multi-algorithm integrated online patterning intelligence planning system using the self-developed NEJ-PRG4.5 inkjet equipment.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 73-85"},"PeriodicalIF":12.2,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143170024","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":"Application and trends of point cloud in intelligent welding: State of the art review","authors":"Hui Wang ,&nbsp;Youmin Rong ,&nbsp;Jiajun Xu ,&nbsp;Yu Huang ,&nbsp;Guojun Zhang","doi":"10.1016/j.jmsy.2025.01.001","DOIUrl":"10.1016/j.jmsy.2025.01.001","url":null,"abstract":"<div><div>Point cloud is an important data format for expressing 3D scenes. With the development of digitalization and intelligence in welding, the application of point cloud in welding is gradually increasing. This paper provides a comprehensive overview of the application of point clouds in intelligent welding, divided into pre-welding stage, in-welding stage, and post-welding stage according to the welding sequence. A detailed analysis was conducted on the pre-welding stage of intelligent welding operations, including the point cloud construction of the workpiece, weld seam detection in the point cloud, path and posture planning, automatic programming, and weld seam positioning. The in-welding stage was divided into welding pool monitoring, weld seam tracking and adjustment, and a comparative analysis was conducted on point cloud processing algorithms and control methods. The post-welding stage is divided into weld seam identification, defect detection, and quality detection, which are analyzed and discussed separately. This paper introduces the application of point clouds in the previous stages, and compares in detail the selection of sensors, application scenarios, point cloud processing algorithms, and processing effects. Furthermore, in-depth analysis was conducted on the effects and limitations achieved in various application scenarios. Finally, this paper provides an outlook on future work, summarizes the relevant applications of machine learning in welding point cloud processing, and analyzes future development trends.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 48-72"},"PeriodicalIF":12.2,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169036","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 monitoring system for production lines in smart factories: A hybrid method integrating Transformer and Kalman filter","authors":"Xiaohui Fang ,&nbsp;Qinghua Song ,&nbsp;Zhenyang Li ,&nbsp;Xiaojuan Wang ,&nbsp;Haifeng Ma ,&nbsp;Zhanqiang Liu","doi":"10.1016/j.jmsy.2024.12.014","DOIUrl":"10.1016/j.jmsy.2024.12.014","url":null,"abstract":"<div><div>Intelligent monitoring systems for production lines in smart factories are used to ensure production efficiency, quality control and fault warning, promoting the optimization of production processes and resource allocation. Tool wear monitoring (TWM) bridges the gap between the perception of machining state information and accurate health management of tools. However, signal features undergo significant and abnormal changes during the late-stage of tool wear, posing substantial challenges to the development of an accurate tool wear intelligent monitoring model. In this paper, a hybrid TWM model integrating the Transformer and Kalman filter is proposed, with a state-space model of tool wear constructed and dynamically updated to address the gap in late-stage prediction accuracy of existing TWM methods. Specifically, the Transformer model is designed to describe the observation model of early tool wear states based on monitoring data. A system model is developed based on the actual tool wear mechanism to describe the relationship between the tool wear rate and tool-workpiece contact load over time. The Kalman filter is used to estimate the parameters of the mechanism model and track the evolution of wear. Within the Bayesian inference framework, measurement noise in the monitoring data is accounted for to optimize and update state estimation deviations and mechanism model parameters, enabling late-stage wear prediction through posterior estimation. The effectiveness and generalization of the proposed method are validated through milling experiments on both thin-walled and rectangular block parts. The experimental results indicate that the average <em>RMSE</em> error of tool wear prediction for thin-walled parts is 6.02, while for rectangular block parts, it is 4.70. The average <em>RMSE</em> errors of the proposed method are reduced by 16.34 % and 11.31 %, respectively, with respect to the single model. More importantly, the proposed method for TWM demonstrates strong predictive performance in the late-stage of tool wear while quantifying the uncertainty of wear prediction.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"79 ","pages":"Pages 27-47"},"PeriodicalIF":12.2,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169564","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}