Computers in Industry最新文献

{"title":"A lightweight metal surface defect detection network with hierarchical multi-branch feature extraction and group decision attention","authors":"Meng Zhang,&nbsp;Yanzhu Hu,&nbsp;Lisha Luo,&nbsp;Binbin Xu,&nbsp;Song Wang,&nbsp;Yingjian Wang","doi":"10.1016/j.compind.2025.104390","DOIUrl":"10.1016/j.compind.2025.104390","url":null,"abstract":"<div><div>Metal materials are widely used in aerospace, bridge construction, and other critical applications. Surface defects such as cracks and scratches can severely undermine structural integrity and material performance, making defect detection on metal surfaces an essential industrial task. Unlike general object detection, metal surface defect detection faces unique challenges including significant scale diversity, pronounced feature ambiguity, severe data imbalance, and stringent computational resource constraints in industrial environments. To address these challenges, this study introduces LHMB-Net, a novel detection algorithm built around four key components including a hierarchical multi-branch feature extraction (HMBFE) module, a partial group decision attention (PGDA) mechanism, an HMB-head detection head, and a BoundaryIoU loss function. The lightweight hierarchical multi-scale architecture of the HMBFE module captures defect features across scales, mitigating scale diversity and feature ambiguity. The PGDA mechanism applies adaptive weighting and group decision strategies to emphasize critical features and substantially alleviate the impact of data imbalance. The HMB-Head replaces conventional convolutional structures with the HMB module to reduce model complexity while enhancing feature representation. Finally, the BoundaryIoU loss introduces boundary point distance constraints for precise localization across scales. Experimental results demonstrate that LHMB-Net outperforms current state-of-the-art methods in both detection accuracy and computational efficiency, highlighting its strong potential for practical industrial deployment.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104390"},"PeriodicalIF":9.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Operation time and rack stability optimisation in tier-to-tier shuttle-based storage and retrieval systems with multiple retrieval locations","authors":"Zhun Xu ,&nbsp;Liyun Xu ,&nbsp;Huan Shao ,&nbsp;Timo Lehmann ,&nbsp;Andrea Matta","doi":"10.1016/j.compind.2025.104385","DOIUrl":"10.1016/j.compind.2025.104385","url":null,"abstract":"<div><div>Tier-to-tier shuttle-based storage and retrieval systems (t-SBS/RSs) are increasingly recognised in the industry for their enhanced flexibility, reduced costs, and improved shuttle utilisation. However, the ineffectiveness of task scheduling (TSc) and retrieval location assignment (LA) schemes diminishes retrieval efficiency and compromises rack stability, thereby elevating retrieval costs and jeopardising system operational safety. Moreover, a potential conflict has been identified between total operation time and rack stability during retrieval operations. Consequently, this study proposes a joint optimisation approach for the batch retrieval TSc and LA challenges in t-SBS/RS, accommodating multiple location distributions for identical cargo types. This issue is modelled as a multi-objective optimisation problem aimed at minimising total operation time while maximising rack stability. To address this, a non-dominated sorting genetic algorithm II (NSGA-II) enhanced by a prior knowledge-based initialisation strategy was proposed. Employing a real case study, several benchmark scenarios were established to assess the effectiveness of the proposed algorithm against five other algorithms, highlighting the superiority of NSGA-II. Additionally, the effectiveness of this method is validated by comparison with classical scheduling policies. The derived Pareto fronts verify the inherent conflict between the two objectives, suggesting that a trade-off is necessary. Finally, managerial insights are offered to highlight the practical application of these findings for warehouse managers.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104385"},"PeriodicalIF":9.1,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An industrial defect detection method based on mixed noise synthesis","authors":"Aihua Ke ,&nbsp;Jian Luo ,&nbsp;Yaoxiang Yu ,&nbsp;Le Li ,&nbsp;Bo Cai","doi":"10.1016/j.compind.2025.104388","DOIUrl":"10.1016/j.compind.2025.104388","url":null,"abstract":"<div><div>Deep learning-based methods have significantly reduced the cost of traditional manual quality inspection while enhancing accuracy and efficiency in industrial defect detection. As a result, these methods have become a prominent research focus in computer vision for intelligent manufacturing. They are increasingly applied in various production and operational contexts, including automated inspection, intelligent monitoring, and quality control. This paper presents a novel method called mixed noise synthesized defect detection, designed to identify multiple types of defects in industrial products. The proposed method employs a generative adversarial network architecture composed of a defect synthesizer, a defect discriminator, a feature extractor, and a multi-scale patch adaptor. By leveraging the feature extractor and multi-scale adaptor, the method effectively captures normal feature distributions and synthesizes realistic defect features through mixed noise synthesis, thereby significantly reducing reliance on labeled data. In addition, the defect discriminator uses a dual evaluation strategy that combines adversarial loss with Kullback–Leibler divergence to assess input features and quantify defect severity. Comprehensive experiments on benchmark anomaly detection datasets demonstrate that the method achieves high performance, with image-level and pixel-level area under the receiver operating characteristic curve scores of 99.8% and 99.4% for texture categories, and 96.7% and 98.3% for object categories, substantially outperforming state-of-the-art methods. The source code is publicly available at <span><span>https://github.com/ah-ke/MNS-Defect.git</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104388"},"PeriodicalIF":9.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-powered heating, ventilation, and air conditioning compressor fault diagnosis facing unseen domains and class imbalances","authors":"Hong Wang ,&nbsp;Jun Lin ,&nbsp;Zijun Zhang","doi":"10.1016/j.compind.2025.104386","DOIUrl":"10.1016/j.compind.2025.104386","url":null,"abstract":"<div><div>Reliable fault diagnosis of compressors in heating, ventilation, and air conditioning (HVAC) systems is essential for enhancing their service reliability and energy conservation. However, heterogeneous working environments of HVAC compressors pose significant challenges for applying data-driven fault diagnosis methods. Domain generalization techniques have been developed to address data distribution discrepancies in cross-domain fault diagnosis. Yet, most existing methods assume that source domains have equal sizes and balanced class distributions. These assumptions limit their applicability to real-world scenarios that can encounter multiple levels of imbalance in both domain size and health status. Therefore, this work proposes a novel Adaptive Invariant Representation learning-based domain generalization Network (AIRNet) to enable a better HVAC compressor fault diagnosis performance in handling unseen domains and class imbalances. Specifically, AIRNet employs a probabilistic sampling strategy to adaptively extract balanced training samples from source domains, mitigating class imbalances and driving unbiased model learning. Furthermore, AIRNet integrates fault classification, metric learning, and domain adversarial training modules with a tailored data augmentation strategy, jointly enhancing its robustness and generalizability across unseen domains. These components collaborate to establish fault-discriminative and domain-invariant diagnostic boundaries while improving model resistance against unseen data distribution discrepancies. Extensive computational experiments on HVAC compressors demonstrate the superiority of AIRNet over state-of-the-art methods in addressing real-world industrial fault diagnosis challenges. Compared to the best-performing benchmark, AIRNet achieves an average performance gain of 1.11 % in total accuracy and 2.76 % in macro F1 score across all studied tasks. The code is available at <span><span>https://github.com/ifuturekk/AIRNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104386"},"PeriodicalIF":9.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From user-generated content to quality improvement: A multi-granularity analysis of customer satisfaction and attention in new energy vehicles using deep learning","authors":"Zhaoguang Xu ,&nbsp;Yifan Wu ,&nbsp;Lin Tang ,&nbsp;Shumeng Gui","doi":"10.1016/j.compind.2025.104380","DOIUrl":"10.1016/j.compind.2025.104380","url":null,"abstract":"<div><div>Understanding customer satisfaction is crucial to improving product quality and ensuring the market competitiveness of new energy vehicles (NEVs). Although user-generated content (UGC)-based analysis offers a cost-effective alternative to traditional customer satisfaction surveys, existing studies have largely overlooked users’ fine-grained needs and rarely translated sentiment insights into actionable guidance for product improvement. To address this, we propose a novel Multi-Aspect Dynamic Knowledge Graph Convolutional Network to extract aspect-level customer perceptions from UGC. The model utilizes a scaled dependency matrix to filter redundant syntactic relations and captures semantic interactions across various aspects. It integrates a sentiment knowledge base with a cross-attention mechanism to enhance sentiment feature extraction. Leveraging the extracted sentiment, we develop a quantitative method to evaluate customer attention and satisfaction across multi-granularity indicators. Experiments on benchmark datasets show that our model outperforms most state-of-the-art methods. A case study of BYD NEVs based on 55,511 sentences from Autohome further validates its superiority, achieving 91.46% Macro-F1 and 91.41% accuracy. Furthermore, by incorporating a customized importance–performance analysis, we pinpoint high-attention aspects with low satisfaction, such as <em>air conditioner</em> and <em>trunk size</em>, which are subsequently integrated into a house of quality measure to support quality improvement. Our analysis further reveals a steady improvement in customer satisfaction across major aspects, despite temporary declines in certain years. We also observe a 14% decline in attention to battery range, alongside a 3.7% increase in vehicle space. These insights can help NEV manufacturers align their product quality improvement efforts with evolving customer expectations.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104380"},"PeriodicalIF":9.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving interoperability in robot digital twinning for facility management: An industry foundation class-represented RoboAvatar approach","authors":"Junjie Chen ,&nbsp;Weisheng Lu ,&nbsp;Xiang Ji ,&nbsp;Yonglin Fu","doi":"10.1016/j.compind.2025.104384","DOIUrl":"10.1016/j.compind.2025.104384","url":null,"abstract":"<div><div>With its bi-directional information flow, a digital twin offers the potential to enhance predictability and controllability of robots for facility management (FM). The implementation of FM involves frequent robot-building interactions, necessitating information exchanges between a robot digital twin (RDT) and a building information model (BIM). However, such information exchanges are prohibited by the different data formats used by the RDT and BIM. Our recent study has proven the viability of industry foundation class (IFC) in digitally representing robots as Avatars, and seamlessly integrating the resulting RoboAvatars into BIM-based software. Building upon that, this paper explores how the IFC-represented RoboAvatars can be used to improve interoperability of RDTs for FM. A lab experiment was conducted with an indoor trash picking robot. It demonstrates effectiveness of IFC-based RDTs in FM via the freely exchangeable robot-building information. The robot movements can be mirrored with high granularity within a BIM context. Information from BIM can be directly retrieved to trigger robot movements remotely. The research contributes to the field of FM robotics by providing the world’s first methodology to directly develop and deploy RDTs in a mainstream BIM-based environment.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104384"},"PeriodicalIF":9.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Segmentation, correction, and classification of abnormal sensor data in mechanical engineering based on multi-task learning","authors":"Xirui Chen,&nbsp;Hui Liu","doi":"10.1016/j.compind.2025.104387","DOIUrl":"10.1016/j.compind.2025.104387","url":null,"abstract":"<div><div>Rolling bearings and hydraulic internal pumps are the two most commonly used fault diagnosis devices in mechanical engineering. However, harsh industrial environments not only harm their health but also the sensors used for monitoring. Abnormal sensor data problems are common in practice and significantly affect data-based fault detection methods. Therefore, this study jointly investigates the anomaly detection of sensor data and the fault detection of engineering components. The related issues are divided into three tasks: classification, correction, and segmentation of abnormal sensor data. A multi-task learning framework based on the teacher-student structure is then proposed to fulfill these tasks in one shot. The designed feature corrector corrects abnormal representations, while the correction attention guides the classifier to focus on the normal parts. A semantic segmentation model is integrated to achieve novel and comprehensive anomaly detection. The proposed multi-task framework is validated using rolling bearing and hydraulic pump datasets. The experimental results show that the jointly trained models outperform those that are trained independently.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104387"},"PeriodicalIF":9.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zero-shot printed circuit board defect detection via optical flow and reconstruction guidance","authors":"Xinghang Yin ,&nbsp;Shuxia Wang ,&nbsp;Yue Wang ,&nbsp;Peng Wang ,&nbsp;Yongxu Liu ,&nbsp;Tianle Shen ,&nbsp;Hengjie Qiao","doi":"10.1016/j.compind.2025.104355","DOIUrl":"10.1016/j.compind.2025.104355","url":null,"abstract":"<div><div>Deep learning is widely used in printed circuit board (PCB) defect detection, owing to its excellent performance. Different types and styles of PCBs exist, for application in different fields and scenarios, making it necessary to fine-tune model on unseen PCB types to maintain detection performance. Few-shot learning methods reduce the cost of data collection and annotation, as they require fewer samples. Under ideal circumstances and with standardized electronic components, image differencing techniques can highlight defects by comparing test images with defect-free reference images, making them category-agnostic, generalizable, and highly interpretable. However, they require careful image preprocessing and parameter selection, and fail if the images are misaligned. To address this issue, while preserving the generalizability of image differencing, we propose a method for PCB defect detection by simulating image differencing using a neural network comprising a shared encoder and three decoders for different tasks: (1) The flow decoder outputs an optical flow displacement field to align image pairs and guides the encoder to learn pixel correspondence relationships, (2) The reconstruction decoder guides the encoder to focus on perceiving the discrepancies between images. (3) The mask decoder locates defective areas with significant visual discrepancies between images. We train the network exclusively on synthetic data and then test it on the publicly available datasets, DeepPCB, PCBS, and MVTec AD, achieving results comparable to that of supervised learning with numerous real samples. Ablation experiments demonstrate that optical flow and reconstruction guidance can effectively enhance the robustness of the network.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104355"},"PeriodicalIF":9.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault diagnosis technology of aero-engine rotors based on meta-action theory driven by machine learning for reliability improvement","authors":"Yulong Li ,&nbsp;Junfa Li ,&nbsp;Hui Long ,&nbsp;Shutao Wen ,&nbsp;Minghui Gu ,&nbsp;Hongwei Wang","doi":"10.1016/j.compind.2025.104381","DOIUrl":"10.1016/j.compind.2025.104381","url":null,"abstract":"<div><div>The intricate structure of electromechanical products presents significant challenges in fault diagnosis, and conventional methods frequently fail to capture the correlation between time-domain and frequency-domain features of fault vibration signals. Moreover, these methods typically rely on extensive training datasets and demonstrate limited generalization capabilities. To overcome these limitations, this paper introduces a fault analysis framework based on the meta-action unit (MAU) to streamline fault diagnosis processes in electromechanical products. An integrated model comprising Fast Fourier Transform (FFT), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (Bi-GRU), Transformer and Attention mechanisms, which designated as the FFT-CNN-Bi-GRU-Transformer-Attention model, was developed to enhance the extraction and representation of vibration signal features, thereby improving model robustness and accuracy. The methodology involves several sequential processes. Initially, fault signals were collected using the MAU and transformed from the time-domain to frequency-domain via FFT. Subsequently, a CNN was employed to automatically extract salient features from the frequency-domain signals. Bi-GRU was then applied to process these features in both forward and backward directions, thus enriching the expressiveness of the data representation. To facilitate efficient parallel computation, the Transformer mechanism was incorporated to refine the output from the Bi-GRU, while the Attention mechanism was used to capture intricate fault features and patterns, significantly enhancing the model’s diagnostic performance. The proposed method was validated using an aero-engine rotor unit as a test case, achieving an accuracy of 98.16 %. Comparative analyses with conventional fault diagnosis techniques underscore the clear advantages of the proposed method. This method provides a foundation for accurate fault identification and timely maintenance of aero-engine rotors, as well as other electromechanical products with analogous structural characteristics.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104381"},"PeriodicalIF":9.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems","authors":"Steve Yuwono ,&nbsp;Dorothea Schwung ,&nbsp;Andreas Schwung","doi":"10.1016/j.compind.2025.104376","DOIUrl":"10.1016/j.compind.2025.104376","url":null,"abstract":"<div><div>This paper presents a novel online transfer learning approach in state-based potential games (TL-SbPGs) for distributed self-optimization in manufacturing systems. The approach targets practical industrial scenarios where knowledge sharing among similar players enhances learning in large-scale and decentralized environments. TL-SbPGs enable players to reuse learned policies from others, which improves learning outcomes and accelerates convergence. To accomplish this goal, we develop transfer learning concepts and similarity criteria for players, which offer two distinct settings: (a) predefined similarities between players and (b) dynamically inferred similarities between players during training. The applicability of the SbPG framework to transfer learning is formally established. Furthermore, we present a method to optimize the timing and weighting of knowledge transfer. Experimental results from a laboratory-scale testbed show that TL-SbPGs improve production efficiency and reduce power consumption compared to vanilla SbPGs.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104376"},"PeriodicalIF":9.1,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}