Journal of Manufacturing Systems最新文献

{"title":"A digital twin calibration for an automated material handling system in a semiconductor fab","authors":"Bonggwon Kang ,&nbsp;Chiwoo Park ,&nbsp;Haejoong Kim ,&nbsp;Soondo Hong","doi":"10.1016/j.jmsy.2025.04.015","DOIUrl":"10.1016/j.jmsy.2025.04.015","url":null,"abstract":"<div><div>To address the complex, dynamic, and stochastic nature of an automated material handling system (AMHS) in a semiconductor fabrication facility (fab), practitioners have used a high-fidelity discrete-event simulation as its digital twin model for decision-making over several decades. Previous studies have focused on fast digital twin-based decision-making in AMHSs under the assumption that their digital twin models are credible enough to prescribe decisions. However, parameter uncertainty and intrinsic bias in an AMHS digital twin model can lead to an inaccurate representation of its field system. To address the challenge, this paper introduces the Bayesian calibration, which modularly estimates a digital twin outcome and its discrepancy using Gaussian process priors. A calibration framework for digital twin-based decision-making is also presented using an AMHS example. Our experimental results with various AMHS operating scenarios demonstrate that: (1) a sophisticated digital twin calibration is necessary, especially when AMHSs operate under heavy-workload scenarios; and (2) exploring model bias considerably decreases the prediction error of an AMHS digital twin within a limited number of field observations. Moreover, we discuss the applicability of the approach to digital twins in various fields.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 1013-1028"},"PeriodicalIF":12.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923341","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 LLM-guided SD-LDM Digital Twin Construction Strategy (LSDT) for multi-industrial scenarios: Enhancing adaptability and efficiency","authors":"Feixiang Wang ,&nbsp;Xiaojun Liu ,&nbsp;Feng Lv ,&nbsp;Chongxin Wang ,&nbsp;Jin Shi ,&nbsp;Xiaotian Zheng ,&nbsp;Chao Li","doi":"10.1016/j.jmsy.2025.04.019","DOIUrl":"10.1016/j.jmsy.2025.04.019","url":null,"abstract":"<div><div>In the rapidly evolving landscape of Industry 5.0, Digital Twin (DT) have emerged as a transformative technology across various industrial sectors. However, as DT theory and practice progress, a critical issue arises: the prolonged building time associated with implementing DTs. To address this challenge, this paper proposes a rapid DT construction method: LLM-Guided SD-LDM Digital Twin Construction Strategy (LSDT) for multi-scenarios. Firstly, we introduce a cross-modal generation framework. This framework leverages Large Language Model (LLM)-Guided Stable Diffusion- Latent Diffusion Model (SD-LDM) technology, which is capable of swiftly constructing high-quality 3D models based on limited multimodal data. Subsequently, the generated models are transferred into the Digital twin construction framework. This framework incorporates both the DT construction method and the assembly and fusion method, enabling the realization of a multi-scale, multi-level DT construction. Finally, we conducted case study in R&amp;D laboratories, prototype warehouses, and packaging units. The multi-dimensional scoring results showed that the model construction efficiency improved significantly, with peak values reaching 39 % (across models) and 73 % (single model), while usability scores peaked at 13.84. Furthermore, the constructed DT successfully met the core Ss requirements of the scenarios. These results indicate that the LSDT method accelerates the efficiency for DT construction and offers good adaptability.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 995-1012"},"PeriodicalIF":12.2,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143916622","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":"Integrated scheduling for ring layout multi-station multi-robot welding system with dual function robots and jump stations operations","authors":"Ye Wang ,&nbsp;Xuewu Wang ,&nbsp;Sanyan Chen ,&nbsp;Yi Hua ,&nbsp;Xingsheng Gu","doi":"10.1016/j.jmsy.2025.04.012","DOIUrl":"10.1016/j.jmsy.2025.04.012","url":null,"abstract":"<div><div>Robotic automated production is the best choice for large-scale manufacturing in the modern automotive industry. Optimizing robotic welding system in an integrated manner is crucial to achieving efficient production. Current research primarily addresses the limited integration of sub-problems for basic production lines. The integrated scheduling of complex coupling challenges in multi-station multi-robot production line is explored in this paper. Tightly coupled sub-problems such as robot allocation, task allocation, dual-function robot scheduling, human–robot cooperative work and welding sequence planning are comprehensively studied and modeled, accounting for numerous constraints in production line composition and parts assembly. Meanwhile, the issue of robot jumping stations operate is also investigated. These complex coupled problems with numerous constraints are incorporated into a unified and novel comprehensive scheduling framework. On this basis, an integrated scheduling model considering robots accessibility, welding accessibility, welding integrity and process feasibility constraints is established, along with an algorithm is proposed to optimize the problems in the model. A five-layer chromosome, featuring two hidden layers, is designed to represent the decision space of the multi-station multi-robot welding system integrated scheduling (MSMRWS-IS) problem. To ensure robot accessibility and welding completeness during evolution, a chromosome correction method is devised. Finally, the proposed STNSGA-DFC is compared with five multi-objective evolutionary algorithms (MOEAs) across four test instance groups. The experimental results demonstrate that STNSGA-DFC outperforms the comparison algorithms in terms of overall performance. The model and optimization method presented in this paper offer significant potential for improving mass production efficiency in industrial environments and hold significant practical value for the complex coupled welding system integrated optimizing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 976-994"},"PeriodicalIF":12.2,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143905875","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":"Total slack transmission graph-based robust scheduling for flexible job shop scheduling under machine breakdowns","authors":"Lingling Lv ,&nbsp;Wenbing Song ,&nbsp;Weiming Shen","doi":"10.1016/j.jmsy.2025.04.007","DOIUrl":"10.1016/j.jmsy.2025.04.007","url":null,"abstract":"<div><div>In actual manufacturing systems, machine failures happen intermittently due to different types of faults. Therefore, it is important to generate a robust schedule. This paper investigates a flexible job shop scheduling problem under machine breakdowns whereby makespan and the robustness of a schedule have to be considered. The concept of a total slack transmission graph is defined to describe the chain reactions of slack consumption between operations. A total slack transmission algorithm is proposed to update the values of the nodes and edges in the graph. Accordingly, a quality robustness surrogate measure and a solution surrogate measure are derived to introduce the objective of robustness. A two-stage hybrid genetic algorithm is adopted by combining the proposed robustness surrogate measures to generate robust schedules. Six robustness surrogate measures in the existing literature are used for comparisons against the proposed surrogate measures. The experimental results show the superiority of the proposed robustness surrogate measure concerning the deviation of makespan and the completion times of operations between the rescheduled solution and preschedule.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 963-975"},"PeriodicalIF":12.2,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902079","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 novel digital twins-driven mutual trust framework for human–robot collaborations","authors":"Junfei Li ,&nbsp;Enshen Zhu ,&nbsp;Wenjun Lin ,&nbsp;Simon X. Yang ,&nbsp;Sheng Yang","doi":"10.1016/j.jmsy.2025.04.009","DOIUrl":"10.1016/j.jmsy.2025.04.009","url":null,"abstract":"<div><div>Trust plays an important role and significantly influences human–robot collaborations (HRC). However, most previous research on trust only emphasizes the human attitude toward robots. There needs more understanding of human uncertainties that may also cause disruptions of trust in collaborations. This paper presents a novel mutual trust framework to provide a relatable vision for future development in HRC from an integrated perspective via the integration of human and robotic digital twins. More specifically, a comprehensive review of current trust research in HRC is first provided, including trust factors and state-of-the-art trust models. Second, a novel human–robot mutual trust framework based on 5-layer digital twins models is introduced. The mutual trust framework highlights the interactions amongst modules of artificial intelligence, simulation, and operation, which can provide wide services in HRC (e.g., task allocation and motion planning). A case study of solving a path planning problem is exemplified to evaluate the performance of the proposed mutual trust framework. Compared with singular trust models, the proposed framework enables robotic systems with real-time response and adaptation to human behavior. Some limitations and future work of the mutual trust framework are elaborated in the end.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 948-962"},"PeriodicalIF":12.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900079","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":"Automatic 3D inspection method for AR-assisted assembly based on virtual-to-real registration","authors":"Zhang Yan ,&nbsp;Fu Hongyong ,&nbsp;Jiang Xinyuan ,&nbsp;Qi Xinlong ,&nbsp;Cui Enze ,&nbsp;Zhang Lu","doi":"10.1016/j.jmsy.2025.04.013","DOIUrl":"10.1016/j.jmsy.2025.04.013","url":null,"abstract":"<div><div>Augmented reality (AR) has been widely employed in intelligent assembly tasks to improve assembly efficiency by layering virtual instructions onto real assemblies, providing operators with step-by-step guidance. However, current AR-assisted assembly systems are limited to being visualization tools, requiring manual control of the guide program by operators and potentially causing distractions and increased operational load. Furthermore, these systems lack the ability to detect incorrect assembly during operation, leading to assembly failures without manual inspection. To address these issues, we propose an automatic 3D inspection method based on virtual-to-real registration that leverages cross-domain texture registration and 6D pose registration to align real assembly images with virtual 3D CAD models. This method conducts a 3D assembly inspection by assessing the similarities between real assembly and its virtual CAD instruction, not only from texture but also from spatial pose relations, improving inspection accuracy while retaining 2D real-time computing. By integrating the inspection results, the AR system can automatically verify assembly correctness and proceed to the next guide program only when a successful assembly is confirmed, eliminating any need for extra instructions from the operator. In case of assembly failure, the computed results are fed back to the operator to assist in correcting errors during assembly, thereby improving assembly efficiency.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 933-947"},"PeriodicalIF":12.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143895658","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":"Automated pallet loading of irregularly shaped objects: A deep reinforcement learning and multi-criteria optimization method","authors":"Nikolaos Theodoropoulos,&nbsp;Dionisis Andronas,&nbsp;Emmanouil Kampourakis,&nbsp;Sotiris Makris","doi":"10.1016/j.jmsy.2025.04.014","DOIUrl":"10.1016/j.jmsy.2025.04.014","url":null,"abstract":"<div><div>Palletizing in manufacturing presents a formidable challenge, particularly when dealing with irregularly shaped objects. This paper introduces a novel approach to optimizing pallet loading scenarios integrating Deep Reinforcement Learning (DRL) and heuristic methods with stability assessment and constraint satisfaction within an automated palletization pipeline. The proposed solution consists of four key components. First, each object undergoes preprocessing, involving shape extraction from data files and the generation of metrics to evaluate stability and palletization suitability. Subsequently, object selection is performed using either a DRL agent—trained to predict optimal loading sequences—or a rule-based prioritization strategy, enabling a comparative analysis of selection methods. Constraint satisfaction techniques are then applied to narrow down the search space for candidate placement positions. Finally, optimal object placement is determined using a Multi-Criteria Decision-Making (MCDM) approach that evaluates candidate positions and orientations based on weighted performance criteria. The proposed framework is validated through a case study in the architectural aluminum industry, demonstrating its pivotal role in automating a production line responsible for sorting and packaging customer orders.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 916-932"},"PeriodicalIF":12.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892216","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":"Enhancing IC substrate manufacturing through differential geometry and lightweight networks for etching defect detection","authors":"Yongxing Yu ,&nbsp;Dan Huang ,&nbsp;Yueming Hu","doi":"10.1016/j.jmsy.2025.04.006","DOIUrl":"10.1016/j.jmsy.2025.04.006","url":null,"abstract":"<div><div>With the advancement of high-density interconnect technology in semiconductor manufacturing, the precision and complexity of integrated circuit (IC) substrates have significantly increased, placing higher demands on quality control. Efficient and accurate detection of complex etching defects, which often occur during manufacturing, has become critical to preventing potential product defects. A defect detection method is proposed that combines a lightweight network with differential geometry tools to address the issue of etching defects in IC substrates. First, an improved deformable model is used to rapidly extract regular circuit trace contours from complex metallographic images, and morphological processing is applied to enhance the details, achieving precise image segmentation. For under-etching defects between circuit traces, differential processing of the original and segmented images is performed to locate abnormal regions. Subsequently, an optimized lightweight network based on MobileNet, termed OMNet, is designed to achieve the rapid identification of under-etching defects in these regions. For etching defects occurring on circuit traces, the DGEtch method employs a high-precision discrete curvature calculation based on the Frenet frame to evaluate angular discontinuities in contours, enabling accurate detection of etching defects. Experimental results demonstrate that the proposed method achieves an average recall rate of over 95% and maintains a precision above 90%. It exhibits high accuracy and stability in detecting etching defects and consistently outperforms existing models, particularly in handling complex mixed defects. This study provides an effective solution for detecting complicated defects in high-density IC substrate manufacturing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 902-915"},"PeriodicalIF":12.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892215","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 of large language model-augmented knowledge graphs for advanced complex product design","authors":"Xinxin Liang,&nbsp;Zuoxu Wang,&nbsp;Jihong Liu","doi":"10.1016/j.jmsy.2025.04.016","DOIUrl":"10.1016/j.jmsy.2025.04.016","url":null,"abstract":"<div><div>In the Human-AI collaboration rapid development era, the design and development of knowledge-intensive complex products should enable the design process with the help of advanced AI technology, and enhance the reasoning and application of design domain knowledge. Extracting and reusing domain knowledge would greatly facilitate the success of complex product design. Knowledge graphs (KGs), a powerful knowledge representation and storage technology, have been widely deployed in advanced complex product design because of their advantages in mining and applying large-scale, complex, and specialized domain knowledge. But merely KG and its related reasoning approaches still cannot fully support the ill-defined product design tasks. In the future complex product design, Human-AI collaboration will become a mainstream prevention trend. Large language models (LLMs) have outstanding performance in natural language understanding and generation, showing promising potential to collaborate with KGs in complex product design and development. Till 2024/03/04, only a few studies have systematically reviewed the current status of LLM and KG applications in the engineering field, not to mention a further detailed review in the complex product design field, leaving many issues not covered or fully examined. To fill this gap, 100 articles published in the last 4 years (i.e., 2021–2024) were screened and surveyed. This study provides a statistical analysis of the screened research articles, mainstream techniques of LLM &amp; KG, and LLM &amp; KG applications were analyze. To understand how KG and LLM could support complex product design, a framework of LLMs-augmented KG in advanced complex product design was proposed, which contains data layer, KG &amp; LLM collaboration layer, enhanced design capability layer, and design task layer. Furthermore, we also discussed the challenges and future research directions of the LLM-KG-collaborated complex product design paradigm. As an exploratory review paper, it provides insightful ideas for implementing more specialized domain KGs in product design field.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 883-901"},"PeriodicalIF":12.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143882806","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":"Automatic simulation models generation in industrial systems: A systematic literature review and outlook towards simulation technology in the Industry 5.0","authors":"Antonio Cimino ,&nbsp;Mohaiad Elbasheer ,&nbsp;Francesco Longo ,&nbsp;Giovanni Mirabelli ,&nbsp;Vittorio Solina ,&nbsp;Pierpaolo Veltri","doi":"10.1016/j.jmsy.2025.03.027","DOIUrl":"10.1016/j.jmsy.2025.03.027","url":null,"abstract":"<div><div>Simulation models are a crucial enabling technology for decision support in the ongoing industrial digitalization hype. Within Industry 4.0, simulations are extensively utilized, providing insights into industrial behavior and responses. As we progress towards Industry 5.0, simulation models continue to play a pivotal role in achieving sustainable, resilient, and human-oriented industrial systems. However, a persistent challenge within Industry 4.0/5.0 is the substantial dynamism of industrial environments. This dynamic and complex landscape necessitates the development of adaptive solutions capable of swiftly responding to the volatile process requirements of modern industrial systems. To this end, Automatic Simulation Model Generation (ASMG) offers an innovative methodological framework to address this practical challenge in the development of industrial simulation models. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this research systematically reviews the current state-of-the-art in ASMG. Complemented by a bibliometric and content analysis of 61 articles spanning more than two decades (from 2000 to 2023), the paper evaluates ASMG’s progression and application in manufacturing through four research questions focusing on ASMG development strategies, objectives, essential data, and developing environments. Ultimately, this article provides valuable insights into ASMG perspective for industrial simulation specialists and offers guidelines for future developments in the era of Industry 5.0.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 859-882"},"PeriodicalIF":12.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874499","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}