Journal of Manufacturing Systems最新文献

{"title":"Deep learning-based fault diagnosis of planetary gearbox: A systematic review","authors":"Hassaan Ahmad ,&nbsp;Wei Cheng ,&nbsp;Ji Xing ,&nbsp;Wentao Wang ,&nbsp;Shuhong Du ,&nbsp;Linying Li ,&nbsp;Rongyong Zhang ,&nbsp;Xuefeng Chen ,&nbsp;Jinqi Lu","doi":"10.1016/j.jmsy.2024.10.004","DOIUrl":"10.1016/j.jmsy.2024.10.004","url":null,"abstract":"<div><div>Planetary gearboxes are popular in many industrial applications due to their compactness and higher transmission ratios. With recent developments in the area of machine learning, Deep Learning-based Fault Diagnosis (DLFD) has become the preferred approach over traditional signal processing methods, physics-based models, and shallow machine learning techniques. This paper presents a systematic review that identifies key research questions for fault types, datasets used, challenges addressed, approaches applied to address the challenges and comparison of the methods using diagnosis accuracies, computation load, and model complexity. The review highlights that the researchers have focused on several challenges, including fault diagnosis under varying operating conditions, imbalanced data, noisy data, limited labeled fault samples, and zero faulty samples. To address these issues various methods have been proposed in the literature, such as incorporating signal processing, data augmentation, transfer learning using domain adaptation, adversarial learning, and integrating physics-based models. Enhancing the industrial applicability of DLFD methods requires validating these methods under multi-problem scenarios, improving transfer learning accuracy for cross-machine fault diagnosis, enhancing interpretability and trust, optimizing for lightweight implementation, and utilizing industrial datasets. Addressing these areas will enable DLFD methods to achieve greater reliability and wider adoption in industrial maintenance practices.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 730-745"},"PeriodicalIF":12.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537578","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":"Zero Defect Manufacturing: A complete guide for advanced and sustainable quality management","authors":"Foivos Psarommatis ,&nbsp;Victor Azamfirei","doi":"10.1016/j.jmsy.2024.10.022","DOIUrl":"10.1016/j.jmsy.2024.10.022","url":null,"abstract":"<div><div>Without product quality, companies cannot survive in today’s competitive and regulated environment. Quality affects not only the product, process, and services, but also the true sustainable capability of a company, being economic, social, and environmental. Different Quality Management (QM) paradigms and approaches have been used to plan, assure, control, and improve production processes and product quality. Nevertheless, most such paradigms were conceived before major technological advancements, thus relying heavily on processes and on people’s knowledge. New paradigms such as Digital Lean, Quality 4.0, and Zero-Defect Manufacturing (ZDM), challenge such views and incorporate emerging technologies into the QM umbrella. Through a literature review, this paper analyses the different QM approaches and combines all the best practices of past and present to support sustainable manufacturing. This paper’s findings include (i) a methodological conceptualization of different QM approaches, (ii) an identification of shortcomings, (iii) analysis of the domain of application, (iv) a proposal for a conceptual framework, and (v) proposals for future work consisting of aligning such theoretical findings with empirical results.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 764-779"},"PeriodicalIF":12.2,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554198","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":"A surrogate modeling framework for aircraft assembly deformation using triplet attention-enhanced conditional autoencoder","authors":"Yifan Zhang ,&nbsp;Qiang Zhang ,&nbsp;Ye Hu ,&nbsp;Qing Wang ,&nbsp;Liang Cheng ,&nbsp;Yinglin Ke","doi":"10.1016/j.jmsy.2024.10.009","DOIUrl":"10.1016/j.jmsy.2024.10.009","url":null,"abstract":"<div><div>This paper introduces a framework for surrogating aircraft structural deformation using simulation data. The framework compresses high-dimensional field data into embeddings via Principal Component Analysis (PCA) and advanced deep learning methods. It establishes a mapping from discretized control points to these embeddings, enabling complete surrogation from the parameter space to structural deformation. The approach facilitates simultaneous surrogation of both displacement and stress fields, providing a robust evaluation metric for assessing assembly quality. Furthermore, the performance of the proposed PCA and deep learning-based surrogation methods is evaluated using multiple metrics. Results demonstrate that the proposed Conditional Convolutional Autoencoders, enhanced by Triplet attention (C2AE-Tri), achieve higher accuracy and over 60 % data reduction compared to the PCA baseline. This improvement highlights the framework's scalability and utility, particularly when data acquisition is challenging or costly.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 708-729"},"PeriodicalIF":12.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535595","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 digital twin commissioning method for machine tools based on scenario simulation","authors":"Xuehao Sun,&nbsp;Fengli Zhang,&nbsp;Xiaotong Niu,&nbsp;Jinjiang Wang","doi":"10.1016/j.jmsy.2024.10.017","DOIUrl":"10.1016/j.jmsy.2024.10.017","url":null,"abstract":"<div><div>Commissioning machine tools before machining is crucial for improving efficiency and performance. Current virtual commissioning technologies have limitations, such as detachment from operation scenarios, which can reduce commissioning effect. This paper presents a digital twin commissioning method for machine tools based on scenario simulation. The method takes into account the machining conditions to build virtual machining scenarios and carries out virtual machining commissioning based on a twin model. The digital twin model of the machine tool is constructed using the unified multi-domain modelling language to ensure consistent response to machining conditions, control effect, and mapping effect of real and virtual parameter changes. Secondly, the machining scenario simulation strategy is formulated and the decoupling analysis for the machining process is carried out to achieve the parametric representation of the working conditions and the simulation of the machining loads. Finally, the parameter adjustment and optimization are investigated under variable machining conditions and variable parameters. The experimental results demonstrate that the proposed method reduces the commissioning time of the spindle machining system of machine tools, decreases the response time by approximately 12 %, and reduces the steady-state error by about 52 %. These findings confirm the effectiveness of the proposed method and its feasibility for field application.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 697-707"},"PeriodicalIF":12.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535589","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":"Interoperable information modelling leveraging asset administration shell and large language model for quality control toward zero defect manufacturing","authors":"Dachuan Shi ,&nbsp;Philipp Liedl ,&nbsp;Thomas Bauernhansl","doi":"10.1016/j.jmsy.2024.10.011","DOIUrl":"10.1016/j.jmsy.2024.10.011","url":null,"abstract":"<div><div>In the era of Industry 4.0, Zero Defect Manufacturing (ZDM) has emerged as a prominent strategy for quality improvement, emphasizing data-driven approaches for defect prediction, prevention, and mitigation. The success of ZDM heavily depends on the availability and quality of data typically collected from diverse and heterogeneous sources during production and quality control, presenting challenges in data interoperability. Addressing this, we introduce a novel approach leveraging Asset Administration Shell (AAS) and Large Language Models (LLMs) for creating interoperable information models that incorporate semantic contextual information to enhance the interoperability of data integration in the quality control process. AAS, initiated by German industry stakeholders, shows a significant advancement in information modeling, blending ontology and digital twin concepts for the virtual representation of assets. In this work, we develop a systematic, use-case-driven methodology for AAS-based information modeling. This methodology guides the design and implementation of AAS models, ensuring model properties are presented in a unified structure and reference external standardized vocabularies to maintain consistency across different systems. To automate this referencing process, we propose a novel LLM-based algorithm to semantically search model properties within a standardized vocabulary repository. This algorithm significantly reduces manual intervention in model development. A case study in the injection molding domain demonstrates the practical application of our approach, showcasing the integration and linking of product quality and machine process data with the help of the developed AAS models. Statistical evaluation of our LLM-based semantic search algorithm confirms its efficacy in enhancing data interoperability. This methodology offers a scalable and adaptable solution for various industrial use cases, promoting widespread data interoperability in the context of Industry 4.0.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 678-696"},"PeriodicalIF":12.2,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536176","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":"Long-term average throughput-utilization utility maximization in platform-aggregated manufacturing service collaboration","authors":"Yanshan Gao ,&nbsp;Ying Cheng ,&nbsp;Lei Wang ,&nbsp;Fei Tao ,&nbsp;Qing-Guo Wang ,&nbsp;Jing Liu","doi":"10.1016/j.jmsy.2024.10.005","DOIUrl":"10.1016/j.jmsy.2024.10.005","url":null,"abstract":"<div><div>Enhancing capacity utilization of manufacturing resources is of utmost importance in tackling the current challenges of meeting customized and small-batch market demands. Given the research highlights on platform-based manufacturing service collaboration (MSC) offering high-quality service solutions, efficient service scheduling strategies are urgently needed to maximize overall utility amidst great computational complexity and unpredictable task arrivals. To address this issue, this paper proposes a novel distributed online task dispatch and service scheduling (DOTDSS) strategy in platform-aggregated MSC. What sets our method apart is its goal to optimize a long-term average utility performance with considering queuing dynamics of manufacturing services in multi-task processing, thereby maintaining sustainable platform operations. Firstly, we jointly consider task dispatch and service scheduling decisions into the formulation of a quality-of-service aware (QoS) stochastics optimization problem. The newly constructed logarithmic utility function effectively strikes a trade-off between the throughput and capacity utilization of manufacturing services with diverse capabilities. By incorporating the goal of reducing queue lengths, we then transform the optimization problem into a form with less computational complexity and guaranteed optimality using Lyapunov optimization. We further propose a DOTDSS strategy that relies solely on the current system state and queue information to generate scalable MSC solutions. It does not need to predict task arrival statistics in advance, and it exhibits great adaptability to uncertainties in task arrivals and service availabilities. Finally, numerical results based on simulation data and real workload traces demonstrate the effectiveness of our method. It also shows that the aggregation collaboration pattern among a group of candidates can achieve better performance than that by the optimal candidate alone.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 662-677"},"PeriodicalIF":12.2,"publicationDate":"2024-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535594","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":"Deep expert network: A unified method toward knowledge-informed fault diagnosis via fully interpretable neuro-symbolic AI","authors":"Qi Li,&nbsp;Yuekai Liu,&nbsp;Shilin Sun,&nbsp;Zhaoye Qin,&nbsp;Fulei Chu","doi":"10.1016/j.jmsy.2024.10.007","DOIUrl":"10.1016/j.jmsy.2024.10.007","url":null,"abstract":"<div><div>In recent years, intelligent fault diagnosis (IFD) based on Artificial Intelligence (AI) has gained significant attention and achieved remarkable breakthroughs. However, the black-box property of AI-enabled IFD may render it non-interpretable, which is essential for safety-critical industrial assets. In this paper, we propose a fully interpretable IFD approach that incorporates expert knowledge using neuro-symbolic AI. The proposed approach, named Deep Expert Network, defines neuro-symbolic node, including signal processing operators, statistical operators, and logical operators to establish a clear semantic space for the network. All operators are connected with trainable weights that decide the connections. End-to-end and gradient-based learning are utilized to optimize both the model structure weights and parameters to fit the fault signal and obtain a fully interpretable decision route. The transparency of model, generalization ability toward unseen working conditions, and robustness to noise attack are demonstrated through case study of rotating machinery, paving the way for future industrial applications.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 652-661"},"PeriodicalIF":12.2,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535588","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":"Semi-supervised adaptive network for commutator defect detection with limited labels","authors":"Zhenrong Wang,&nbsp;Weifeng Li,&nbsp;Miao Wang,&nbsp;Baohui Liu,&nbsp;Tongzhi Niu,&nbsp;Bin Li","doi":"10.1016/j.jmsy.2024.09.016","DOIUrl":"10.1016/j.jmsy.2024.09.016","url":null,"abstract":"<div><div>Deep learning-based surface defect detection methods have obtained good performance. However, customizing architectures for specific tasks is a complex and laborious process. Neural architecture search (NAS) offers a promising data-driven adaptive design approach. Yet, deploying NAS in industrial applications presents challenges due to its reliance on supervised learning paradigm. Hence, we propose a mixed semi-supervised adaptive network for commutator surface defect detection, even with limited labeled samples. In the proposed framework, we employ a multi-branch network with complementary perturbation flows, leveraging consistency regularization, pseudo-labeling, and contrastive learning. First, a confidence-guided directional consistency regularization strategy aligns features in high-quality directions. Second, confidence-aware hybrid pseudo-labeling improves the pseudo-supervision quality. Finally, foreground/background contrast awareness encourages the model to more sensitively identify defect regions. The detection backbone is data-driven generated through a neural architecture search process, replacing manual design strategies. Experimental results show our method automatically generates optimal commutator detection networks using limited labels, outperforming existing state-of-the-art methods. Our work paves the way for adaptive defect detection networks with limited labels and can extend to surface defect detection in various production lines.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 639-651"},"PeriodicalIF":12.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142536178","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":"Leveraging the usage of blockchain toward trust-dominated manufacturing systems","authors":"Philip Samaha ,&nbsp;Fadi El Kalach ,&nbsp;Ramy Harik","doi":"10.1016/j.jmsy.2024.10.010","DOIUrl":"10.1016/j.jmsy.2024.10.010","url":null,"abstract":"<div><div>Smart manufacturing has transformed the role of data in manufacturing, with a significant focus on secure data infrastructure. As factories engage with external data sources, cybersecurity becomes crucial. Blockchain technology is introduced to safeguard this infrastructure, ensuring secure and transparent data flow, which is vital for industries like pharmaceutical, aerospace, automotive, and electronics manufacturing. This review provides a comprehensive taxonomy of blockchain architectures, analyzing their working modes, strengths, and weaknesses while identifying appropriate use cases. It also examines consensus algorithms, categorizing them as either crash fault tolerant (CFT) or Byzantine fault tolerant (BFT) and further classifies them based on whether they are proof-based or voting-based. The review explores the intrinsic limitations of blockchain systems and highlights specific manufacturing challenges where blockchain can be instrumental. It also discusses the synergy between blockchain and cybersecurity, emphasizing how they work together to enhance security and accountability. The paper concludes by identifying private blockchain as the most suitable architecture for certain manufacturing applications, particularly in supply chain management and machinery control. A SWOT analysis is conducted on this architecture to provide a detailed understanding of its potential and challenges. The review suggests that while no single consensus algorithm is best universally, each has its own merits depending on the application. Lastly, the SWOT analysis serves as a catalyst for future research, guiding efforts to maximize blockchain’s strengths and mitigate its weaknesses in industrial contexts.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 612-638"},"PeriodicalIF":12.2,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535596","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":"Anomaly detection in smart manufacturing: An Adaptive Adversarial Transformer-based model","authors":"Moussab Orabi ,&nbsp;Kim Phuc Tran ,&nbsp;Philipp Egger ,&nbsp;Sébastien Thomassey","doi":"10.1016/j.jmsy.2024.09.021","DOIUrl":"10.1016/j.jmsy.2024.09.021","url":null,"abstract":"<div><div>In Industry 5.0, smart manufacturing brings additional intricacies and novel data processing challenges. Given the evolving nature of manufacturing processes and the inherent complexity of data, including noise and missing entries, achieving accurate anomaly detection becomes even more intricate. Conventional methods often miss nuanced anomalies, especially when dealing with high-dimensional, multivariate, non-stationary data. These data types are typical of smart manufacturing environments. Hence, many recent approaches have embraced deep learning to confront these challenges, making use of diverse attention mechanisms to acquire data representations. However, in manufacturing, where the dynamics of time series data change over time, methods relying solely on pointwise or pairwise representations often fall short. Thus, ensuring product quality and operational integrity calls for even more advanced methodologies. The deficiency lies in the capability of state-of-the-art models to effectively capture abnormal patterns while considering both local and global contextual information. This challenge is compounded by the rarity of anomalies, making it exceedingly challenging to establish substantial associations between individual abnormal points and the entire time series. To tackle these challenges, we introduce the “<strong>A</strong>daptive <strong>A</strong>dversarial <strong>T</strong>ransformer” as a novel deep learning technique that combines Transformer architecture with an anomaly attention mechanism and Adversarial Learning. Our Model effectively captures intricate temporal patterns, distinguishes normal and anomalous behaviors, and dynamically adjusts thresholds to align with the evolving dynamics of time-series data. Empirical validation on four benchmark datasets and three real-world manufacturing datasets demonstrates our model’s effectiveness compared to the state-of-the-art, as evidenced by the F1-Score.</div></div>","PeriodicalId":16227,"journal":{"name":"Journal of Manufacturing Systems","volume":"77 ","pages":"Pages 591-611"},"PeriodicalIF":12.2,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142535593","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}