Journal of Manufacturing Systems最新文献

{"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}

{"title":"A methodology for estimating the cost of a digital twin","authors":"Shuo Su ,&nbsp;Aydin Nassehi ,&nbsp;Adam McClenaghan ,&nbsp;Andrew Langridge ,&nbsp;Ben Hicks","doi":"10.1016/j.jmsy.2025.04.004","DOIUrl":"10.1016/j.jmsy.2025.04.004","url":null,"abstract":"<div><div>This paper proposes a methodology for estimating the cost of developing digital twins (DTs) in manufacturing processes. It formulates a cost model that identifies main cost elements and presents the estimation process for establishing an acceptable cost reference based on a given set of physical information entities as DT inputs. To achieve this, six core data activities are derived from the ISO 23247 DT reference framework and the Digital Twin Data concept to characterize the functioning of DTs from a data perspective. These activities are data gathering, data interaction, data storage, data processing, data servitization, and data maintenance. The activity-based costing (ABC) method is applied to allocate six resources in the development of DTs (personnel, machine, equipment, material, facility, and service) to these data-intensive activities. The resultant cost structure comprises 40 cost activities, along with associated quantitative metrics. This work presents a case study on developing a DT for estimating the dimensional accuracy in the MEX process, where thermal and acceleration measurements are considered. For the information set with extruder and build plate temperatures as well as X- and Y-axis acceleration, developing a DT is estimated to cost between £4780 (for one temperature signal) and £39,285 (for two temperature signals and two acceleration signals) for two years of service and one year of data archiving. In addition, the cost distribution across four categories (IT infrastructure, resource, data activity, and investment) are analysed. The derived insights can support cost-related analysis, physical information entity selection, budget control, and standard open databases for DT costs.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 841-858"},"PeriodicalIF":12.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874442","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":"Out-of-order enabled operating system for uncertain planning, scheduling and execution in aviation maintenance","authors":"Fan Yang ,&nbsp;Cheng Ren ,&nbsp;Wei Liu ,&nbsp;Mingxing Li ,&nbsp;Ming Li","doi":"10.1016/j.jmsy.2025.04.011","DOIUrl":"10.1016/j.jmsy.2025.04.011","url":null,"abstract":"<div><div>Maintenance has long been a concern in manufacturing, both in the production and product-service phases. As a type of large product, aviation maintenance produces a collection of services to ensure that aircrafts or aircraft systems, components, and structures meet airworthiness standards. Planning, scheduling, and execution (PSE) is important for maintenance systems to optimize resource utilization and job sequencing through decision-making at different time cycles. However, stochastic uncertainty always exists, affecting the stability of the entire maintenance process. Therefore, in this study, which was inspired by operating systems (i.e., Windows, Android, etc.) for processing uncertain user actions with high efficiency, an out-of-order enabled operation system in aviation maintenance (OoO-AMOS) is designed to mitigate the influence of uncertainties that exist in the PSE procedure. Two key components, namely, thread manager and resource manager, are proposed at the kernel level of the OoO-AMOS. The concept of out-of-order (OoO) is deployed for the thread manager to dynamically select the optimal order sequence based on task dependencies and feasibility. A finite state machine (FSM) model is integrated as the operation validation mechanism to formulize the resource states and their transitions. Finally, a case study is conducted to evaluate the effectiveness of the proposed OoO-AMOS. The results show that OoO-AMOS presents significant advantages over traditional approaches. In uncertain environments, the total setup time was reduced by more than 55 %, whereas the maintenance makespan, average order tardiness, and hangar turnover rate achieved improvements of more than 22 %, 31 %, and 23 %, respectively.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 824-840"},"PeriodicalIF":12.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143868907","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 Q-learning improved differential evolution algorithm for human-centric dynamic distributed flexible job shop scheduling problem","authors":"Xixing Li ,&nbsp;Ao Guo ,&nbsp;Xiyan Yin ,&nbsp;Hongtao Tang ,&nbsp;Rui Wu ,&nbsp;Qingqing Zhao ,&nbsp;Yibing Li ,&nbsp;XiVincent Wang","doi":"10.1016/j.jmsy.2025.04.001","DOIUrl":"10.1016/j.jmsy.2025.04.001","url":null,"abstract":"<div><div>Traditional scheduling less account of human-related dynamic events: worker skill degradation and worker mandatory rest. However, in actual production, workers experience fatigue accumulation that decreases work efficiency, thereby decreasing the precision of jobs, increasing rework rates, and even elevating processing risks. It conflicts with the idea of industrial resilience and human well-being for Industry 5.0. Therefore, a human-centric dynamic distributed flexible job shop scheduling problem (HDDFJSP) has been researched in this paper. Firstly, a multi-objective mathematical model of HDDFJSP is proposed to minimize makespan, worker fatigue, and scheduling deviation. Secondly, a Q-learning improved differential evolution (QLIDE) is designed to solve the HDDFJSP. In the QLIDE, a new four-layer encoding method and two initialization strategies are proposed to generate a high-quality initial population and a novel mutation strategy and two auxiliary mutation methods are designed to enhance the algorithm's exploitation capabilities. Furthermore, three neighborhood search strategies are introduced and combined with mutation operations as part of the Q-learning action phase to improve population convergence and diversity. Thirdly comparative test with four other well-known algorithms has been conducted and the results demonstrate the significant superiority of the QLIDE. Finally, the QLIDE is applied to solve a real case of a labor intensive hydraulic cylinder manufacturing enterprise. The results indicate that considering rescheduling can effectively help production managers to handle dynamic event of humans during the intelligent manufacturing systems.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 794-823"},"PeriodicalIF":12.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143864774","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 multi-level root cause analysis method for production anomalies in manufacturing workshops","authors":"Shengbo Wang ,&nbsp;Yu Guo ,&nbsp;Shaohua Huang ,&nbsp;Qunfen Qi ,&nbsp;Sai Geng ,&nbsp;Weiwei Qian","doi":"10.1016/j.jmsy.2025.04.008","DOIUrl":"10.1016/j.jmsy.2025.04.008","url":null,"abstract":"<div><div>Production anomalies, being one of the main causes of disrupted production schedules and product quality issues, have driven the manufacturing industry to focus on real-time monitoring and effective management, as these measures undoubtedly ensure production continuity and enhance efficiency. The complexity of discrete manufacturing workshops—characterized by diverse products, complex process routes, and frequent disturbances—leads to a corresponding complexity in the occurrence and evolution of production anomalies. Unlike point-to-point models for root cause analysis of production anomalies, this paper proposes a multi-level root cause analysis model for production anomalies to reveal the key influencing factors in their evolution process. First, to address the challenge of single-dimensional manufacturing data failing to effectively represent complex production states, production state representation models of manufacturing elements are built based on a first-order graph model of manufacturing elements, enabling consistent expression of production states. Second, a production anomaly evolution pattern analysis model based on a nonlinear Granger model is proposed to answer the questions of how production anomalies arise and evolve. Then, considering the imbalance in production anomalies, a meta-learning Transformer model is designed to learn evolution patterns of production anomalies and enable root cause analysis. Finally, using a real discrete manufacturing workshop as an example, the proposed method can accurately analyze the evolution patterns of production anomalies. In the evolution pattern learning task, it achieves better learning performance and is at least 69.5 % higher than the baseline models on the root mean square error. Additionally, the method achieves an accuracy of 81.67 % in identifying the top three root cause states of production anomalies. The research results demonstrate that nonlinear networks can effectively analyze the complex evolution processes of production anomalies and enhance the Granger model's accuracy in identifying the evolution patterns. The meta-learning framework improves the generalization ability of the evolution pattern learning model, enabling more precise root cause identification. Consequently, the proposed method offers a new perspective for evolution analysis and root cause analysis of production anomalies.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 776-793"},"PeriodicalIF":12.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854793","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":"Collaborative optimization for multirobot manufacturing system reliability through integration of SysML simulation and maintenance knowledge graph","authors":"Jian Zhou ,&nbsp;Lianyu Zheng ,&nbsp;Yiwei Wang","doi":"10.1016/j.jmsy.2025.04.010","DOIUrl":"10.1016/j.jmsy.2025.04.010","url":null,"abstract":"<div><div>In the rapidly advancing field of industrial automation, the reliability and maintenance of multirobot manufacturing systems are crucial. This paper proposes a collaborative optimization method for the reliability of multirobot system, combining SysML (System Modeling Language) model simulation with an operational and maintenance knowledge graph, aiming to ensure the reliable operation of multirobot manufacturing systems. The SysML model provides a comprehensive framework to represent the system architecture, workflows, and key parameters, identify critical components and potential bottlenecks, and perform detailed reliability analysis. Simultaneously, by embedding intelligent algorithms, the operational and maintenance knowledge graph enables automatic detection of operational anomalies and intelligent generation of maintenance strategies for industrial robots. By integrating the SysML model with the operational and maintenance knowledge graph, a collaborative optimization framework for the reliability of multirobot system is constructed. This framework not only dynamically adjusts key parameters in the simulation model, enhancing the accuracy and real-time performance of system reliability assessments, but also optimizes maintenance strategies based on system simulation indicators to ensure the reliable operation of multirobot system. Case studies validate that the proposed method improves the reliability of multirobot manufacturing systems, demonstrating that the combination of SysML simulation and the operational and maintenance knowledge graph can effectively address the complexity of modern manufacturing systems, offering significant reference value.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 749-775"},"PeriodicalIF":12.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143854792","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":"Physics-informed symbolic regression for tool wear and remaining useful life predictions in manufacturing","authors":"Seulki Han,&nbsp;Utsav Awasthi,&nbsp;George M. Bollas","doi":"10.1016/j.jmsy.2025.03.023","DOIUrl":"10.1016/j.jmsy.2025.03.023","url":null,"abstract":"<div><div>Prognostics and Health Management (PHM) plays a crucial role in enhancing the reliability and safety of engineering systems. Recently, physics-informed machine learning (PIML) methods have gained significant attention for their ability to incorporate domain-specific knowledge into data-driven models. This paper proposes a novel approach that integrates symbolic regression with recursive modeling to develop a robust framework for PHM of dynamic processes. Our framework was applied to a manufacturing process to build a generic model for tool wear prognostics across various machining scenarios. The proposed method integrates domain knowledge of milling processes under different conditions with recursive models using symbolic regression to achieve accurate and robust tool wear predictions. A recursive feature model and a recursive tool wear model were developedto accurately predict future tool wear, taking into consideration the strong correlation between features extracted from sensor signals and tool wear. The Genetic Programming-based Toolbox for Identification of Physical Systems (GPTIPS) was employed for symbolic regression. The results illustrate that the proposed framework can capture the dynamics of tool wear by recursively updating predictions with new data and can derive simple, interpretable mathematical expressions that represent the physical characteristics of the tool wear process. Benchmarking analysis demonstrated the effectiveness of the proposed approach, achieving lower root-mean-square error (RMSE) compared to other tool wear prognostic models in the literature.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 734-748"},"PeriodicalIF":12.2,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850684","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 synthesis augmented TimeGAN and adaptive temperature control for microwave heating","authors":"Jinhai Xu,&nbsp;Kuangrong Hao,&nbsp;Chengyang Meng,&nbsp;Yan Cheng,&nbsp;Xiaoyan Liu,&nbsp;Bing Wei","doi":"10.1016/j.jmsy.2025.03.026","DOIUrl":"10.1016/j.jmsy.2025.03.026","url":null,"abstract":"<div><div>Microwave ovens are valued for their convenience and efficiency; however, many models still face issues with heating accuracy. While simulation analyses have made progress in addressing these challenges, the complexity and time requirements of multi-scenario data collection remain a challenge, as the lack of sufficient real-world data hinders the effective evaluation of model performance. To address this issue, we propose the Dynamic Synthesis Augmentation-TimeGAN (DSA-TGAN), which integrates a Discriminative Guided Warping (DGW) module to generate data that captures both the primary features of the heating process and additional perturbation information, effectively simulating the variations in microwave heating. The generated data serves as a pseudo-training set for TimeGAN, which is trained through an adaptive framework to produce sufficient experimental data. Additionally, we demonstrate that fine-tuning the pre-trained DSA-TGAN with a small amount of data from different microwave models enables successful transfer learning. Leveraging the synthetic data and feature analysis algorithms, we developed a process-adaptive temperature control method that enhances the accuracy and stability of microwave heating. Experimental results confirm that the DSA-TGAN model achieves the goals of high-quality data synthesis and effective transfer learning, significantly enhancing microwave heating performance. In addition, the proposed data augmentation model can be widely used in other microwave heating fields such as chemical processing and material synthesis.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 723-733"},"PeriodicalIF":12.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838645","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":"Challenges of randomness in tool wear with small samples: A physics-informed cross-domain monitoring method","authors":"Delin Liu ,&nbsp;Zhanqiang Liu ,&nbsp;Bing Wang ,&nbsp;Qinghua Song ,&nbsp;Liangliang Li ,&nbsp;Aisheng Jiang","doi":"10.1016/j.jmsy.2025.04.002","DOIUrl":"10.1016/j.jmsy.2025.04.002","url":null,"abstract":"<div><div>Cutting tool wear monitoring is crucial for enabling predictive maintenance in machining processes. However, uncertainties in tool degradation during small-batch personalized machining present significant challenges to achieving accurate monitoring. This study addresses the randomness of tool wear through a dual-level approach: data and model. At the data level, an empirical tool wear model is developed based on nonlinear wear mechanisms, which is integrated with a domain-discriminative generative adversarial network to construct a target domain tool wear data generation framework. At the model level, a feature extractor tailored for transfer learning is designed using nonlinear relationships inherent in tool wear mechanisms, complemented by fine-tuning the classifier with the generated target domain tool life cycle data to handle domain shifts caused by randomness. The proposed method is validated using both public datasets and workshop experiments under both fixed and variable cutting conditions. Compared with baseline models, ablation models, and several state-of-the-art data generation and transfer learning models, the proposed approach demonstrates superior adaptability and robustness in handling the randomness in tool wear, even with highly imbalanced and small datasets. The results confirm the effectiveness of the proposed method in tool wear monitoring for small-batch personalized machining processes.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 694-722"},"PeriodicalIF":12.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830074","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":"Efficient ship pipeline routing with dual-strategy enhanced ant colony optimization: Active behavior adjustment and passive environmental adaptability","authors":"Xin Wang ,&nbsp;Fengfeng Ning ,&nbsp;Zemin Lin ,&nbsp;Zhinan Zhang","doi":"10.1016/j.jmsy.2025.04.003","DOIUrl":"10.1016/j.jmsy.2025.04.003","url":null,"abstract":"<div><div>The ship pipeline system is crucial as the transmission pathway for water, oil and gas, of which the layout design directly affects system efficiency, cost and safety. However, multiple objectives and constraints are involved in the large-scale ship pipe routing design problem, so the traditional ant colony algorithm is difficult to fully meet the requirements in terms of search efficiency and solution quality. This research proposes a Dual-Strategy Enhanced Ant Colony Optimization (DEACO) algorithm enhanced by both active and passive strategies. The active strategy, inspired by the behavior patterns of natural ant colonies, includes an adaptive greedy adjustment mechanism, heterogeneous pheromone deposition rule, and self-regulating pheromone secretion mechanism to enhance searching flexibility and efficiency. The passive strategy incorporates endpoint guidance enhancement and dynamic pheromone limits to adjust algorithm response, achieving fast path routing. Cases with two different environment settings show that DEACO outperforms traditional ACO, two latest ACOs and improved PSO in terms of key metrics such as pipe lengths and numbers of bends with faster computation speed. The algorithm achieves high stability within the same scenarios and strong robustness across various scenarios, yielding consistently favorable results despite randomness in searching and condition variations. Therefore, the proposed algorithm demonstrates effectiveness and superiority in ship pipeline automated routing.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 673-693"},"PeriodicalIF":12.2,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825628","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}