Journal of Manufacturing Systems最新文献

{"title":"A bi-level supply chain resilience model using cloud manufacturing","authors":"Wei Ye ,&nbsp;Shanshan Yang ,&nbsp;Xingyu Li","doi":"10.1016/j.jmsy.2025.03.020","DOIUrl":"10.1016/j.jmsy.2025.03.020","url":null,"abstract":"<div><div>Globalization has heightened supply chain vulnerability to disruptions such as pandemics and natural disasters. Emerging digital transformation technologies, including digital supply chain and cloud manufacturing, offer a promising approach to mitigate disruptions and improve supply chain resilience by connecting manufacturers through shared information; however, it is often hindered by data security and privacy concerns. This study introduces a bi-level supply chain resilience model incorporating cloud manufacturing and a three-tier data privacy classification to balance efficiency, resilience, and privacy preservation. At the network level, <em>share-aggregated</em>, <em>safe-to-share</em> data optimizes task assignment; at the node level, suppliers locally schedule operations based on <em>confidential</em> data. Through case studies leveraging NSGA-II and Mixed-Integer Programming (MIP) for optimization, the model demonstrates a trade-off between resilience and operational efficiency. Results show that the bi-level approach enables dynamic supply chain adaptation while protecting sensitive supplier data, reducing lead times and transportation costs while maintaining supply chain resilience. These findings highlight the potential of cloud manufacturing as a scalable and privacy-preserving solution for enhancing supply chain resilience.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 662-672"},"PeriodicalIF":12.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823566","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 deep reinforcement learning approach with graph attention network and multi-signal differential reward for dynamic hybrid flow shop scheduling problem","authors":"Youshan Liu,&nbsp;Jiaxin Fan,&nbsp;Weiming Shen","doi":"10.1016/j.jmsy.2025.03.028","DOIUrl":"10.1016/j.jmsy.2025.03.028","url":null,"abstract":"<div><div>In real-life manufacturing systems, production management often faces uncertainty due to urgent demands and dynamic job insertions. Such uncertain environments pose significant challenges for scheduling, particularly in minimizing delivery delays and improving overall efficiency. Deep reinforcement learning (DRL) brings potential for rapid real-time production decisions, but scheduling in these environments with the objective of reducing delivery delays remains a challenging problem. This paper investigates a hybrid flow-shop dynamic scheduling problem with job insertions for minimizing the total weighted tardiness (TWT). An end-to-end DRL based method, the proximal policy optimization with graph attention network (PPO-GAT), is proposed to address the problem. First, a multi-agent system is established to simulate the actual manufacturing system and serve as a foundation for implementing intelligent production scheduling. Then, a novel graph-based state representation is developed to observe instantaneous states for the hybrid flow-shop. Two graph models are designed to represent system features and job features, and are extracted and fused by graph attention networks (GAT) to form the global feature. Afterwards, a multi-signal differential reward (MSDR) function is designed to address the intractable reward sparsity caused by the TWT objective. Finally, ablation experiments are conducted to validate all the proposed algorithmic components, and the PPO-GAT is compared with benchmark methods. Experimental results demonstrate the superiority of the proposed GAT, MSDR, and PPO-GAT. Moreover, the PPO-GAT has been proven to make real-time scheduling decisions for hybrid flow-shops with any scale, which can be considered as a promising solution for extensive industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 643-661"},"PeriodicalIF":12.2,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823565","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 method for intelligent reasoning of machining step sequences based on deep reinforcement learning","authors":"Biao Xiao ,&nbsp;Zhengcai Zhao ,&nbsp;Baode Xu ,&nbsp;Yao Li ,&nbsp;Wei Zhang ,&nbsp;Haixiang Huan ,&nbsp;Honghua Su","doi":"10.1016/j.jmsy.2025.04.005","DOIUrl":"10.1016/j.jmsy.2025.04.005","url":null,"abstract":"<div><div>High-quality and efficient process planning methods are crucial for ensuring product manufacturing quality. However, traditional methods have several drawbacks, namely, they are time-consuming, highly dependent on expert experience, and involve considerable repetitive workloads. To overcome these limitations and enhance the efficiency and intelligence of process planning for complex structured parts, this study proposes a machining step sequence reasoning method based on deep reinforcement learning. First, historical process data are preprocessed to convert the knowledge stored in the process files into structured and vectorized data. Second, the process routes and feature step sets serve as inputs, and a proximal policy optimization algorithm is employed to train the historical process instances. The sequencing patterns discovered during training are then integrated with advanced sorting strategies to efficiently generate the machining step sequences. To evaluate the effectiveness of the proposed method, 50 complex structured parts were tested, with 25 representative parts selected for detailed comparative analysis. The training performance of the proposed algorithm was evaluated against those of the advantage actor-critic and soft actor-critic algorithms. In addition, the reasoning results of various state-of-the-art algorithms were analyzed using these test cases. Experimental results demonstrate that the proposed method is effective and competitive for process planning of complex structural parts. Therefore, this study provides practical guidance for enhancing the efficiency and intelligent automation of process planning of complex structural parts.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 626-642"},"PeriodicalIF":12.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815126","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 key process identification framework for aircraft assembly production based on the network with physical attributes","authors":"Jin-Hua Hu ,&nbsp;Yan-Ning Sun ,&nbsp;Wei Qin","doi":"10.1016/j.jmsy.2025.03.024","DOIUrl":"10.1016/j.jmsy.2025.03.024","url":null,"abstract":"<div><div>Accurate identification of aircraft assembly key processes plays an important role in aircraft production management. However, due to complex processes, multiple attributes, and the aggregation phenomenon of the aircraft assembly process, identifying the key processes faces huge challenges. Therefore, a network-based key process identification framework is proposed in this paper. Firstly, according to assembly processes and vital physical attributes, an aircraft assembly network and the node attribute matrix are constructed. Then, the SC-<em>Q</em>-walktrap algorithm is designed to adaptively identify the aircraft assembly network community structure. Subsequently, the network-based influential node identification algorithm is proposed to recognize key process nodes, which consists of two steps. Within the community, local influence is evaluated based on node entropy and network topology. Between the communities, global influence is measured based on neighboring nodes in different communities. Finally, the proposed framework is compared with the traditional centrality measurements on the datasets from PSPLIB and commercial aircraft assembly datasets. The experiment results demonstrate that the network-based influential process identification algorithm can effectively identify the key processes.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":""},"PeriodicalIF":12.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815387","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":"Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg games","authors":"Steve Yuwono ,&nbsp;Ahmar Kamal Hussain ,&nbsp;Dorothea Schwung ,&nbsp;Andreas Schwung","doi":"10.1016/j.jmsy.2025.03.025","DOIUrl":"10.1016/j.jmsy.2025.03.025","url":null,"abstract":"<div><div>In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-SbSG enhances cooperative decision-making among self-learning agents within production systems by integrating State-based Potential Games (SbPG) with Stackelberg games. This hierarchical structure assigns more important modules of the manufacturing system a first-mover advantage, while less important modules respond optimally to the leaders’ decisions. This decision-making process differs from typical multi-agent learning algorithms in manufacturing systems, where decisions are made simultaneously. We provide convergence guarantees for the novel game structure and design learning algorithms to account for the hierarchical game structure. We further analyse the effects of single-leader/multiple-follower and multiple-leader/multiple-follower scenarios within a Mod-SbSG. To assess its effectiveness, we implement and test Mod-SbSG in an industrial control setting using two laboratory-scale testbeds featuring sequential and serial–parallel processes. The proposed approach delivers promising results compared to the vanilla SbPG, which reduces overflow by 97.1%, and in some cases, prevents overflow entirely. Additionally, it decreases power consumption by 5%–13% while satisfying the production demand, which significantly improves potential (global objective) values.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 578-594"},"PeriodicalIF":12.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799403","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":"Intelligent configuration management in modular production systems: Integrating operational semantics with knowledge graphs","authors":"Hamood Ur Rehman ,&nbsp;Fan Mo ,&nbsp;Jack C. Chaplin ,&nbsp;Leszek Zarzycki ,&nbsp;Mark Jones ,&nbsp;Antonio Maffei ,&nbsp;Svetan Ratchev","doi":"10.1016/j.jmsy.2025.03.017","DOIUrl":"10.1016/j.jmsy.2025.03.017","url":null,"abstract":"<div><div>This paper presents an innovative approach to integrating data-driven strategies into intelligent manufacturing systems, specifically targeting the challenges of configuration management in modular production environments. To address the distinct and evolving requirements of customized products, we propose a dynamic configuration management methodology that automatically adjusts system settings in real-time. This approach utilizes operational semantics to formalize the interactions between production modules, capturing essential operational information for intelligent decision-making. A novel control mechanism is developed, using knowledge graphs to semantically represent and manage the relationships between production system components and settings. By mapping these, the system can determine optimal configurations based on real-time data and specific operational requirements. The interaction between the control mechanism and the knowledge graph ensures continuous adaptability, enabling the system to reconfigure dynamically in response to changes. This method was validated in an industrial dry-air leak testing scenario, demonstrating its effectiveness in adaptability.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 610-625"},"PeriodicalIF":12.2,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799404","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":"Domain constrained cascadic multireceptive learning networks for machine health monitoring in complex manufacturing systems","authors":"Yadong Xu ,&nbsp;Sheng Li ,&nbsp;Ke Feng ,&nbsp;Ruyi Huang ,&nbsp;Beibei Sun ,&nbsp;Xiaolong Yang ,&nbsp;Zhiheng Zhao ,&nbsp;George Q. Huang","doi":"10.1016/j.jmsy.2025.03.021","DOIUrl":"10.1016/j.jmsy.2025.03.021","url":null,"abstract":"<div><div>Precise condition monitoring of manufacturing systems is crucial for maintaining efficient industrial production. In practical manufacturing applications, typical components of manufacturing system such as gearboxes and bearings mainly operate under fluctuating conditions, resulting in obvious nonlinear characteristics in the monitored vibration signals. Nonetheless, numerous extant algorithms are crafted based on the stationary presumption that the signal’s amplitude and frequency remain constant, failing to reflect the real-world scenarios prevalent in industrial environments. In this research, we propose a domain constrained cascadic multirepetive learning network as a response to this challenge. Initially, we leverage cascadic multireceptive learning modules, multiscale feature aggregation modules, and an adaptive filtering module to establish the feature extractor for acquiring multireceptive and multilevel features from monitored signals. Next, a conditional label regulation loss is devised as the loss function to enhance the model’s robustness in complex scenarios. Finally, a domain constrained label adjuster is designed to align the actual labels based on the input data, thereby guiding the feature extractor in learning the domain-invariant feature. Three case studies demonstrate that the DC-CMLN model outperforms seven state-of-the-art algorithms, particularly when applied to mechanical datasets collected under nonstationary conditions.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 563-577"},"PeriodicalIF":12.2,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143785048","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":"H2R Bridge: Transferring vision-language models to few-shot intention meta-perception in human robot collaboration","authors":"Duidi Wu ,&nbsp;Qianyou Zhao ,&nbsp;Junming Fan ,&nbsp;Jin Qi ,&nbsp;Pai Zheng ,&nbsp;Jie Hu","doi":"10.1016/j.jmsy.2025.03.016","DOIUrl":"10.1016/j.jmsy.2025.03.016","url":null,"abstract":"<div><div>Human–robot collaboration enhances efficiency by enabling robots to work alongside human operators in shared tasks. Accurately understanding human intentions is critical for achieving a high level of collaboration. Existing methods heavily rely on case-specific data and face challenges with new tasks and unseen categories, while often limited data is available under real-world conditions. To bolster the proactive cognitive abilities of collaborative robots, this work introduces a Visual-Language-Temporal approach, conceptualizing intent recognition as a multimodal learning problem with HRC-oriented prompts. A large model with prior knowledge is fine-tuned to acquire industrial domain expertise, then enables efficient rapid transfer through few-shot learning in data-scarce scenarios. Comparisons with state-of-the-art methods across various datasets demonstrate the proposed approach achieves new benchmarks. Ablation studies confirm the efficacy of the multimodal framework, and few-shot experiments further underscore meta-perceptual potential. This work addresses the challenges of perceptual data and training costs, building a human–robot bridge (H2R Bridge) for semantic communication, and is expected to facilitate proactive HRC and further integration of large models in industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":""},"PeriodicalIF":12.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven hierarchical multi-policy deep reinforcement learning framework for multi-objective multiplicity dynamic flexible job shop scheduling","authors":"Linshan Ding ,&nbsp;Zailin Guan ,&nbsp;Dan Luo ,&nbsp;Lei Yue","doi":"10.1016/j.jmsy.2025.03.019","DOIUrl":"10.1016/j.jmsy.2025.03.019","url":null,"abstract":"<div><div>In the context of Industry 4.0, manufacturers face pressure to personalize products and accelerate the supply chain. This requires rapid response to volatile production schedules, ensuring a balance between operational efficiency and product quality. Moreover, the rapid development and convergence of the cloud computing, Internet of Things (IoT), and big data have expanded the need for real-time tracking and adaptive scheduling to address uncertainties, such as equipment downtime, supply variation, and ongoing product revisions. The capability of IoT has significantly improved the continuous monitoring and data analysis, emphasizing the importance of developing effective real-time scheduling solutions in the manufacturing system. In response to these evolving industrial requirements, and driven by objectives to reduce the makespan, total tardiness, and energy consumption, we study the multi-objective multiplicity dynamic flexible job shop scheduling problem (MOMDFJSP), to cope with the challenges of new order arrivals and machine breakdowns in the IoT-enabled manufacturing system. This study proposes a novel hierarchical multi-policy deep reinforcement learning framework for IoT-infused manufacturing environments, aiming to integrate these diverse requirements and uncertainties into a coherent and responsive scheduling framework. The proposed framework comprises an upper-level control policy network and three lower-level objective policy networks. The upper-level and lower-level networks are respectively responsible for selecting temporary optimization objectives and specific dispatching rules. Based on the proposed framework, we design a two-stage training approach named the hierarchical multi-policy soft actor-critic (HMPSAC) algorithm to train multiple policy networks. In addition, we develop a fluid model to design the state features and dispatching rules that act as inputs and outputs, respectively, for the deep reinforcement learning (DRL) policy network. The comparative analysis with well-known dispatching rules and DRL-based methods reveals the superior performance of HMPSAC algorithm.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 536-562"},"PeriodicalIF":12.2,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768879","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":"IIoT-enabled digital twin for legacy and smart factory machines with LLM integration","authors":"Anuj Gautam ,&nbsp;Manish Raj Aryal ,&nbsp;Sourabh Deshpande ,&nbsp;Shailesh Padalkar ,&nbsp;Mikhail Nikolaenko ,&nbsp;Ming Tang ,&nbsp;Sam Anand","doi":"10.1016/j.jmsy.2025.03.022","DOIUrl":"10.1016/j.jmsy.2025.03.022","url":null,"abstract":"<div><div>Recent advancements in Large Language Models (LLMs) have significantly transformed the field of natural data interpretation, translation, and user training. However, a notable gap exists when LLMs are tasked to assist with real-time context-sensitive machine data. The paper presents a multi-agent LLM framework capable of accessing and interpreting real-time and historical data through an Industrial Internet of Things (IIoT) platform for evidence-based inferences. Real-time data is acquired from several legacy machine artifacts (such as seven-segment displays, toggle switches, and knobs), smart machines (such as 3D printers), and building data (such as sound sensors and temperature measurement devices) through MTConnect data streaming protocol. Further, a multi-agent LLM framework that consists of four specialized agents – a supervisor agent, a machine-expertise agent, a data visualization agent, and a fault-diagnostic agent is developed for context-specific manufacturing tasks. This LLM framework is then integrated into a digital twin to visualize the unstructured data in real time. The paper also explores how LLM-based digital twins can serve as real time virtual experts through an avatar, minimizing reliance on traditional manuals or supervisor-based expertise. To demonstrate the functionality and effectiveness of this framework, we present a case study consisting of legacy machine artifacts and modern machines. The results highlight the practical application of LLM to assist and infer real-time machine data in a digital twin environment.</div><div>Peer-review under responsibility of the scientific committee of the NAMRI/SME.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"80 ","pages":"Pages 511-523"},"PeriodicalIF":12.2,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759768","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}