Journal of Manufacturing Systems最新文献

{"title":"Controlled assembly of random threads based on large language models","authors":"Liping Ma ,&nbsp;Zhengjie Yang ,&nbsp;Hongjuan Yan ,&nbsp;Dehu Gao ,&nbsp;Xurong Gong","doi":"10.1016/j.jmsy.2025.09.014","DOIUrl":"10.1016/j.jmsy.2025.09.014","url":null,"abstract":"<div><div>In precision assembly scenarios such as aerospace and automotive engineering, the random starting positions of internal and external threads pose a significant challenge. While achieving specified tightening torque ranges is critical for sealing integrity, precisely controlling the final orientation of threaded connections remains difficult for varying thread pairings. This study proposes a framework integrating visual feature extraction with pre-trained large language models (LLMs) to enable controlled assembly of randomly aligned threads. Using the directional tightening process of hydraulic cylinder barrels and pipe fittings as a case study, the method’s feasibility is validated: First, computer vision techniques extract thread assembly features; then, servo-driven tightening devices perform directional tightening experiments on different fittings, with results recorded. Through structured prompt engineering, assembly parameters, visual features, and experimental outcomes are input into the LLM, the gasket thickness and thread phase are regarded as the controlled input variables, while the collaborative condition judgment of tightening torque and end orientation serves as the output variables. Results demonstrate that pre-trained LLMs, unlike traditional deep learning methods, not only adapt to raw data but also accurately predict directional tightening outcomes for randomly selected shims without requiring additional training. This work provides a novel approach for applying LLMs in precision assembly.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 392-404"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220736","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":"Using a digital twin and smart services to enable automatic generation of context-sensitive instructions","authors":"Karl Lossie ,&nbsp;Jan Hendrik Hellmich ,&nbsp;Junjie Liang ,&nbsp;Jonas Baum ,&nbsp;Amon Göppert ,&nbsp;Dennis Grunert ,&nbsp;Robert H. Schmitt","doi":"10.1016/j.jmsy.2025.10.007","DOIUrl":"10.1016/j.jmsy.2025.10.007","url":null,"abstract":"<div><div>The increasing diversity and shorter life cycles of technical products pose significant challenges for manufacturing companies, particularly in the context of providing specific and context-sensitive instructions to employees, especially in domains including maintenance, assembly and disassembly. This challenge holds significant importance in the context of the current skilled worker shortage. This paper proposes a solution by leveraging digital twin technology and smart services to automate the generation of context-sensitive instructions. The research outlines the development of a smart service system that uses real-time data from digital twins to create and deliver adaptive and user-specific instructions via smart devices. A conceptual design of the smart service system, a prototypical implementation using a rolling mill maintenance task, and the verification and validation of the developed system were carried out. The results indicate that the proposed system effectively addresses the challenges of traditional manual instructions, enhancing efficiency, accuracy, and user satisfaction.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 652-674"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465405","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":"RGB video and inertial sensing fusion method for human action recognition in human-robot collaborative manufacturing","authors":"Lili Dong ,&nbsp;Tianliang Hu ,&nbsp;Tianyi Sun ,&nbsp;Junrui Li ,&nbsp;Songhua Ma","doi":"10.1016/j.jmsy.2025.09.007","DOIUrl":"10.1016/j.jmsy.2025.09.007","url":null,"abstract":"<div><div>Human action recognition (HAR), as a prerequisite for robotic dynamic decision-making, is crucial for achieving efficient human-robot collaborative manufacturing (HRCM). Compared with single modality, multi-modality provides a more comprehensive understanding of human actions. However, it is a challenge to effectively integrate this information to fully leverage the advantages of multi-modality for HAR in HRCM. Therefore, in this paper, the RGB video and inertial sensing fusion method for HAR in HRCM is proposed, presenting the systematic exploration of this multi-modality in industrial contexts. Two fusion strategies of two modalities are studied: decision-level fusion and feature-level fusion. Secondly, taking the rotary vector (RV) reducer assembly as an example, a multi-modal human assembly action dataset for HAR (HAAD-SDU) is designed, filling the gap in the HRCM field where publicly representative datasets are scarce. This dataset synchronously introduces RGB video and inertial sensing data containing human assembly information. Finally, the feasibility and effectiveness of the proposed approach are verified by the designed dataset and public dataset, demonstrating superior performance over baseline methods. The experimental results demonstrate that the proposed fusion approach integrating RGB video and inertial sensing modalities not only overcomes the limitations of the single modality but also exhibits strong cross-domain generalizability, proving effective for both industrial tasks and daily activity recognition. In the HRCM scenario specifically, both decision-level and feature-level fusion strategies demonstrate superior recognition capabilities. The decision-level fusion provides a higher recognition accuracy of 95.71 %, while the feature-level fusion achieves competitive accuracy at 94.42 % with low recognition latency of 1.67 s. Notably, the proposed fusion model can accurately recognize human behaviors at least 2 s before they are completed, providing sufficient leftover time for the robotic system to complete collaborative tasks.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 216-234"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096555","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 constraint programming-based lexicographic-Pareto approach for balancing two-sided robotic disassembly lines with 7-axis robots","authors":"Yu Zhang ,&nbsp;Zeqiang Zhang ,&nbsp;Feng Chu ,&nbsp;Yanqing Zeng ,&nbsp;Lei Guo ,&nbsp;Zongxing He","doi":"10.1016/j.jmsy.2025.09.006","DOIUrl":"10.1016/j.jmsy.2025.09.006","url":null,"abstract":"<div><div>Robotic disassembly lines play a pivotal role in remanufacturing by enabling automated operations. In two-sided disassembly scenarios involving large-scale products such as automobiles, their high load capacity significantly reduces the labor intensity of manual disassembly and eliminates the need for lifting equipment, thereby streamlining the process flow. When equipped with mobility systems, 7-axis robots can flexibly switch between multiple workstations, facilitating both rapid adaptation to process changes and precise execution of spatially heterogeneous disassembly tasks. However, despite these advantages, systematic research on the integration of mobile disassembly robots within disassembly line applications remains limited. To address this gap, this study integrates 7-axis mobile robots into two-sided disassembly lines and models the system using both mixed-integer programming and constraint programming approaches. The proposed models aim to minimize construction costs and ensure balanced workload distribution across stations. A novel constraint programming-based lexicographic-Pareto approach is developed to solve the resulting multi-objective optimization problem, this method is capable of generating verified Pareto frontiers for small-scale instances and providing high-quality approximate Pareto solution sets for large-scale problems. In the numerical experiments, a sensitivity analysis of key algorithm parameters is first conducted to achieve a balance between computational efficiency and solution quality. Subsequently, the proposed method is benchmarked against nine existing algorithms across twenty datasets to validate its effectiveness. Its practical feasibility is further demonstrated through an application to the disassembly of drum washing machines. The results show that, compared to conventional fixed-robot disassembly lines without cross-station coordination, the mobile robot configuration achieves a 10.7% reduction in total cost and a 66.7% improvement in robot workload balance, offering a promising pathway for advancing remanufacturing practices.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 235-251"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096558","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-centric allocation and visualization of primary manufacturing process life cycle inventory data","authors":"Teodor Vernica ,&nbsp;Badrinath Veluri ,&nbsp;Devarajan Ramanujan","doi":"10.1016/j.jmsy.2025.10.006","DOIUrl":"10.1016/j.jmsy.2025.10.006","url":null,"abstract":"<div><div>Detailed machine-specific data are critical for accurate sustainability assessment and for supporting design decisions to reduce environmental impacts from manufacturing. However, obtaining, analyzing, and interpreting such fine-grained measurements can be challenging and inefficient. Existing methods for the above are time-consuming, do not fully capture process variability over time, and do not relate primary manufacturing data back to design decision-making. In this work, we propose a methodology to programmatically disaggregate process-level life cycle inventory data measurements, and relate it to both operations (i.e., activities or sub-processes) within the process and the geometric features created or affected by the process. We do this by leveraging the underlying machine code used to manufacture the part, in this case G-code, and by providing a scalable definition scheme for the corresponding operations, geometric features, and the relationship between them. Results can be used to generate targeted, actionable insights into process setup and product design improvements to address environmental impacts of manufacturing processes.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 557-576"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416703","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 multitasking workforce-constrained flexible job shop scheduling problem: An application from a real-world workshop","authors":"Jiahang Li ,&nbsp;Xinyu Li ,&nbsp;Liang Gao ,&nbsp;Cuiyu Wang ,&nbsp;Haojie Chen","doi":"10.1016/j.jmsy.2025.09.004","DOIUrl":"10.1016/j.jmsy.2025.09.004","url":null,"abstract":"<div><div>The flexible job shop scheduling problem with worker constraints plays a vital role in production scheduling because of workforce flexibility. Existing research assumes that single tasking workers can process one operation simultaneously. However, multitasking workers can be observed in a real-world enterprise, where they can handle multiple operations simultaneously. There is a lack of research on the effect of multitasking workers in flexible job shops. This paper investigates a flexible job-shop scheduling problem considering multitasking workers (FJSP-MW). First, a mixed-integer linear programming model with multitasking worker constraints is constructed to minimize total weighted tardiness (TWT). Second, an operation-sequence and resource-sequence (OSRS) encoding method is proposed to represent the solution space using the OS and RS vectors. Besides, a multitasking decoding method is introduced to decode the OS and RS vectors as a feasible schedule in the objective space. Third, a hybrid algorithm (IGA4MW) is proposed and consists of two aspects: (1) three modified genetic operators are designed for the OS and RS vectors to enhance the exploration ability, and (2) a resource-balanced local search method is presented to improve the exploitation ability. Finally, experiments are conducted on medium and large instances to demonstrate the effectiveness and efficiency of IGA4MW. The case study illustrates that the TWT and makespan of the obtained scheduling solution from IGA4MW are reduced by 15.43% and 41.16%, compared to the original scheduling solution. Furthermore, the performance gain of the resource-balanced local search method increases as the solution space is expanded.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 196-215"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096554","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":"Dynamic data driven uncertain physical information self-awareness method for the aircraft composite component assembly system","authors":"Pengbo Yin,&nbsp;Yang Zhang,&nbsp;Jiacheng Cui,&nbsp;Jiangtao Zhao,&nbsp;Yulin Jin,&nbsp;Qihang Chen,&nbsp;Yongkang Lu,&nbsp;Wei Liu","doi":"10.1016/j.jmsy.2025.11.001","DOIUrl":"10.1016/j.jmsy.2025.11.001","url":null,"abstract":"<div><div>High-precision assembly of large composite components is crucial for aircraft structural safety. To address geometric deviation of components caused by anisotropic material behaviors and time-varying process constraints during dynamic assembly, this paper proposes a dynamic data driven uncertain physical information self-awareness (DDDPIA) method. This approach accurately updates physical models of components by integrating dynamic data of the manufacturing process containing displacement information, load configurations, and model information through three key innovations: (1) A simplified affine mapping method from model parameters to system stiffness that decouples material properties from process constraints in deformation modeling. (2) A multi-source prior data-driven model parameter optimization framework enabling efficient identification of material parameters and process constraints while quantifying measurement uncertainty impacts and maintaining high-precision performance with measurement errors below 0.3 mm. (3) An industrial application-oriented shape regulation platform that leverages the updated physical model for precise load inversion to achieve specified shapes of composite components. Experimental and simulation results verify over 64% displacement error reduction relative to uncalibrated static modeling, while load inversion with sub-0.2 N solution accuracy achieves geometric deviations correction of components. This establishes a closed-loop measurement-data-model-assimilation paradigm, enhancing decision autonomy in aviation intelligent manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 1009-1023"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680786","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-objective chaotic evolutionary-based cell configuration and load balancing for reconfigurable production lines","authors":"Jiaming Zhang ,&nbsp;Jiewu Leng ,&nbsp;Xuming Lai ,&nbsp;Libin Lin ,&nbsp;Linshan Ding ,&nbsp;Lei Yue","doi":"10.1016/j.jmsy.2025.09.017","DOIUrl":"10.1016/j.jmsy.2025.09.017","url":null,"abstract":"<div><div>With the continuous advancement of intelligent manufacturing, the reconfigurable manufacturing system (RMS) has become an important development direction for modern manufacturing industry by virtue of its high degree of flexibility and reconfigurable characteristics. As a concrete realization form of RMS, reconfigurable automated production line (RAPL) provides an effective technical path to cope with diversified and individualized market demands. In this study, a multi-constraint mathematical model is constructed around the cell configuration and balance optimization problem in RAPL, taking into account the different production line organization methods and cell service modes. Multi-objectives are established involving the minimization of the cycle time, the smoothing index among the manufacturing cells, and the total number of machines of the RAPL. Recognizing the collaborative interaction between mobile robots and machines, a specific theoretical cycle time derivation method is proposed for this RAPL system, and a general-purpose simulation model is designed to support the evaluation and optimization of multiple configuration schemes, thereby verifying the accuracy of the derivation model (with an error of only 1.5 %). To overcome the inefficiency and trial-and-error nature of manual methods, a multi-objective chaotic evolutionary algorithm (MOCEO) is developed. MOCEO demonstrates superior performance and stability, achieving high-quality solutions in a single run and outperforming classical algorithms such as NSGA-II and SPEA2 in hypervolume (HV), distance (GD) and other metrics. The proposed approach provides reliable decision-making support, enabling efficient and effective configuration and balancing of RAPL systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 445-469"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220748","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":"AI-aided Automated AR-Assisted Assembly Instruction Authoring and Generation method","authors":"Junjian Lin,&nbsp;Jianjian Wang,&nbsp;Pingfa Feng,&nbsp;Xiangyu Zhang,&nbsp;Dingwen Yu,&nbsp;Jianfu Zhang","doi":"10.1016/j.jmsy.2025.08.019","DOIUrl":"10.1016/j.jmsy.2025.08.019","url":null,"abstract":"<div><div>While Augmented Reality (AR) offers the potential to provide real-time guidance, one of the barriers to its adoption in industrial assembly is the lack of fast, no-code, intelligent methods for generating AR-assisted assembly programs. This paper proposes an AI-aided AR-Assisted Assembly Instruction Authoring and Generation method (ARAIAG) to address these challenges. ARAIAG allows engineers, without coding expertise, to intuitively design AR-assisted assembly instructions based on assembly demonstrations captured through RGBD cameras. Based on ARAIAG, we propose a new algorithm considering hand manipulation and model characteristics to achieve spatial registration for models, virtual-physical fusion, and assembly direction recognition. Additionally, we employed a novel human–computer interaction method and Large Language Model (LLM)-assisted content generation to achieve the automatic creation of interactive and instructive AR-assisted assembly programs. Through this approach, we streamline program development and enable more efficient AR-assisted assembly in dynamic manufacturing environments.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 405-423"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220750","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":"Neural surface partitioner: A physics-informed neural network for five-axis machining by a non-spherical cutting tool","authors":"Jiancheng Hao ,&nbsp;Haokun Chen ,&nbsp;Pengcheng Hu ,&nbsp;Dong He ,&nbsp;Yamin Li ,&nbsp;Xiaoke Deng ,&nbsp;Tak Yu Lau ,&nbsp;Fan Shi ,&nbsp;Yanglong Lu","doi":"10.1016/j.jmsy.2025.11.015","DOIUrl":"10.1016/j.jmsy.2025.11.015","url":null,"abstract":"<div><div>The machining performance of five-axis machining can be significantly improved by partitioning the part surface into subregions, each of which is machined using an adaptive iso-scallop height method. This approach is particularly beneficial when employing non-spherical cutting tools, as it allows for a diverse range of effective cutting radii that can be optimally tailored to the geometry of each subregion. Current surface partition-based five-axis machining methods begin by partitioning the surface into patches based on geometric constraints, followed by generating the tool path for each partitioned patch. However, this stepwise approach fails to incorporate critical objectives such as machining efficiency, smooth tool orientation, and interference avoidance during the surface partitioning process. Consequently, both the surface partitioning results and the subsequently generated tool paths tend to be sub-optimal. In this research, we introduce a novel Physics-Informed Neural Network (PINN) called the Neural Surface Partitioner (NSP) designed to jointly and near-optimally partition surfaces while generating tool orientations for non-spherical cutting tools. By directly integrating comprehensive issues into the NSP’s loss function, we create a surface partitioning matrix that enables effective partitioning of freeform surfaces. Simultaneously, the NSP generates a tool orientation scalar field, which is employed to produce iso-scallop Cutter Contact (CC) curves while ensuring smooth and interference-free tool orientation. To validate the advantages of the tool paths generated by the proposed NSP, we conducted both computer simulations and physical cutting experiments. The results demonstrate that the average cutting width achieved by our method significantly exceeds that of two benchmark methods, leading to drastically reduced path lengths of up to 42.9 % and machining times of up to 44.8 %.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"83 ","pages":"Pages 976-991"},"PeriodicalIF":14.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145568615","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}