Computers in Industry最新文献

{"title":"Automated configuration for cost-effective digital solutions","authors":"Zhengyang Ling ,&nbsp;Duncan McFarlane ,&nbsp;Sam Brooks ,&nbsp;Lavindra de Silva ,&nbsp;Gregory Hawkridge ,&nbsp;Alan Thorne","doi":"10.1016/j.compind.2025.104397","DOIUrl":"10.1016/j.compind.2025.104397","url":null,"abstract":"<div><div>Low-cost digital solutions have been proposed as a means of helping Small and Medium-sized Enterprises (SMEs) in manufacturing. To reduce development costs and enable SMEs to create digital solutions for their specific requirements, workers should be able to configure their own solutions. However, such an approach can be problematic – and at times infeasible – as the SME may not have access to staff with the necessary software skills. Hence, this paper proposes an automated configuration approach for the preparation, customisation, and automatic generation of low-cost digital solutions. This approach was implemented in the development of an Automated Solution Configurator (ASC) platform. The ASC specifically makes use of a particular reference architecture (the so-called <em>Shoestring</em> approach) as a foundation for the design of low-cost digital solutions. These solutions are composed of modules of key functions (referred to as a “Service Module”), which themselves integrate “Building Blocks” (BBs) of low cost technology elements. The paper presents an overview of the ASC platform; its usefulness and usability are evaluated via (a) three industrial application studies, (b) a user study with seven participants and (c) a direct comparison between ASC-based and expert-prepared solutions. The evaluations demonstrate that users with a range of expertise can rapidly create low-cost solutions using the ASC platform. Comparing the ASC-generated code side by side with that written by an expert, the ASC code tends to be longer than a solution developed by an expert but still operates effectively. It is also demonstrated that the ASC approach can support simple solution reuse by reconfiguring technology BBs for different digital solutions.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104397"},"PeriodicalIF":9.1,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261909","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":"A cross-domain few-shot remaining useful life estimation framework based on model-agnostic meta-learning with task embeddings","authors":"Xinyu Shang ,&nbsp;Jie Shang ,&nbsp;Mingyu Li ,&nbsp;Haobo Qiu ,&nbsp;Liang Gao ,&nbsp;Danyang Xu","doi":"10.1016/j.compind.2025.104396","DOIUrl":"10.1016/j.compind.2025.104396","url":null,"abstract":"<div><div>Remaining useful life (RUL) estimation aims to predict the time until system failure based on monitoring data, facilitating proactive maintenance actions. Precise RUL estimation can significantly enhance system reliability and safety. However, when new system failures emerge, predictive models trained on historical failures data often encounter difficulties in accurate estimation. The distribution shift between historical and new failures data, coupled with extremely few new failures data, results in cross-domain few-shot prognostic scenarios, posing a significant challenge to many deep-learning-based RUL estimation methods. In response to the challenge, this paper proposes a novel cross-domain few-shot RUL estimation framework based on model-agnostic meta-learning (MAML) with task embeddings. First, a segmentation strategy is adopted to construct more meta-tasks, which can capture more comprehensive degradation information for efficient meta knowledge extraction. Then, task embeddings that are independent of backbone network are designed to encode task-specific degradation knowledge into efficient low-dimensional vectors, which alleviates overfitting caused by limited labeled data, thus improving RUL estimation performance. Moreover, the encoded degradation knowledge is only injected into feature extractor, making representation change dominant for better cross-domain adaptability. Experimental results on turbofan engine and wind turbine gearbox datasets reveal the effectiveness and superiority of the proposed framework. Estimation results evaluated by RMSE and Score improve 9 % and 31 %, respectively.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104396"},"PeriodicalIF":9.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261911","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":"A confident learning-based coordinate attention-guided fusion vision transformer for mixed-type wafer map defect detection","authors":"Xiangyan Zhang ,&nbsp;Xuexiu Liang ,&nbsp;Jian Li ,&nbsp;Shimin Wei","doi":"10.1016/j.compind.2025.104391","DOIUrl":"10.1016/j.compind.2025.104391","url":null,"abstract":"<div><div>Wafer defect detection is crucial for quality assurance in semiconductor manufacturing. Current methods often overlook two key challenges: the adverse effects of mislabeled data on model reliability, and the significant correlation between defects and their spatial locations. To address these issues, we propose a novel confident learning-based coordinate attention-guided vision Transformer framework. Our approach includes: (1) automatic mislabel data identification and dataset cleaning using confident learning, and (2) a mixed-type wafer defect detection network that fuses convolutional operations, coordinate attention, and self-attention mechanisms. The architecture enables effective local–global feature extraction with positional awareness, and a decoupled classifier further improves detection performance. Evaluated on the clean MixedWM38 dataset (with 192 mislabeled noisy samples removed via confident learning), our framework achieves 99.60% accuracy while maintaining computational efficiency, outperforming advanced wafer defect detection methods. These results demonstrate its strong potential for industrial applications.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104391"},"PeriodicalIF":9.1,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261915","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":"A hybrid mechanism and data-driven optimization method of process parameters in laser cutting","authors":"Shuai Ma ,&nbsp;Zhuyun Chen ,&nbsp;Zehao Li ,&nbsp;Jiewu Leng ,&nbsp;Huitao Liu ,&nbsp;Yixian Du ,&nbsp;Xiaoji Zhang ,&nbsp;Qiang Liu","doi":"10.1016/j.compind.2025.104394","DOIUrl":"10.1016/j.compind.2025.104394","url":null,"abstract":"<div><div>Laser cutting quality is directly influenced by process parameters, which govern the formation of burrs and the extent of the heat-affected zone. Consequently, selecting and optimizing these parameters is crucial for achieving high-quality laser cutting results. Machine learning techniques have proven effective in process parameter optimization by establishing surrogate models that link process parameters with quality indicators. However, these models often overlook critical temperature field information generated during laser cutting, which provides valuable mechanistic insights. To overcome this limitation, a hybrid mechanism and data-driven optimization method is proposed. First, a laser cutting experimental platform is developed, and the full-factorial design with five factors at three levels is employed for data collection. Detailed laser-cutting physical models are then established to simulate key temperature field information, compensating for the scarcity of such data in real-world scenarios. Subsequently, a novel physics-informed neural network is designed with dual input branches to handle low-dimensional process parameters and high-dimensional temperature field data. Besides, the physics-informed neural network includes a focused fusion layer with an attention mechanism to selectively integrate the most relevant mechanistic features with process parameters. To further optimize the trained physics-informed neural network model, a clustering-assisted multi-objective evolutionary algorithm is developed, which leverages the clustering strategy to select and retrieve historical mechanistic data that best match candidate process parameters, ensuring valid surrogate model inputs and improving optimization efficiency. Experimental validation demonstrates that the proposed hybrid method significantly outperforms conventional machine learning approaches, delivering superior accuracy and reliability in laser cutting process parameter optimization.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104394"},"PeriodicalIF":9.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261933","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":"Integrated label correction for e-commerce data: Boosting accuracy with baseline attention and enhanced Bayesian updating","authors":"Miao Zhang ,&nbsp;Jingyuan Fu ,&nbsp;Yuli Zhang ,&nbsp;Qinghui Ma ,&nbsp;Jinghong Chen","doi":"10.1016/j.compind.2025.104392","DOIUrl":"10.1016/j.compind.2025.104392","url":null,"abstract":"<div><div>The rapid growth of the e-commerce industry has led to an explosion of product data, which e-commerce platforms increasingly leverage to enhance operational decision-making. However, the presence of label noise in product data poses a significant challenge, as mislabeled data can degrade decision-making performance, negatively impacting both platform efficiency and user experience. To tackle this challenge, this paper proposes an integrated label correction (ILC) method, featuring two key innovations: a baseline attention (BA) mechanism for noise detection and an enhanced Bayesian updating (EBU) strategy for label prediction. The proposed BA mechanism utilizes a dynamic attention scaling approach with learnable parameters to measure the similarity between samples and their labeled classes. The EBU strategy integrates sample features and noisy observations into a unified probabilistic framework, jointly optimizing the classifier and the label correction process. A novel adaptive noise transition learning approach is further introduced to refine the noise transition matrix. Extensive experiments on a real-world JD text dataset, a benchmark image dataset, and an automotive user review dataset validate the effectiveness of the proposed ILC method. It consistently outperforms state-of-the-art approaches on average, achieving a 2.78% improvement on the JD dataset, and gains of 0.80% on the image dataset and 1.73% on the review dataset across different noise types. These improvements demonstrate that the ILC method not only improves classification performance but also offers scalable solutions to reduce mislabeling errors in real-world e-commerce platforms, thereby enhancing recommendation systems, inventory management, and user satisfaction.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104392"},"PeriodicalIF":9.1,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261916","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":"Real-time fire temperature field reconstruction using multi-source acoustic wave data and a hybrid deep learning approach","authors":"Hengjie Qin ,&nbsp;Pengyu Qu ,&nbsp;Cheng Cheng ,&nbsp;Haowei Yao ,&nbsp;Zhen Lou ,&nbsp;Zihe Gao ,&nbsp;Donghao Li ,&nbsp;Xiaoge Wei","doi":"10.1016/j.compind.2025.104389","DOIUrl":"10.1016/j.compind.2025.104389","url":null,"abstract":"<div><div>Accurate reconstruction of temperature distributions in complex fire scenarios is essential for effective fire monitoring, early warning, and firefighting decision-making. Traditional methods often face challenges in both accuracy and computational efficiency due to the highly nonlinear and dynamic nature of fire environments. To address this issue, a novel reconstruction framework driven by multi-source acoustic wave data is presented. This approach integrates an Adaptive Weighted Hybrid Convolution–Dynamic Residual Attention-Aware Fusion Network (AWHC-DRAAFN) with an Integrated K-Nearest Neighbor (IKNN) model. The AWHC-DRAAFN facilitates efficient extraction and fusion of multi-scale features by combining various convolution operations with adaptive weighting mechanisms, thereby enhancing the network’s capacity to capture complex nonlinear relationships between acoustic wave propagation and temperature distribution. Meanwhile, the IKNN model transforms discrete temperature data into a continuous field through a locally weighted K-nearest neighbor interpolation strategy. Experimental results demonstrate that the proposed method achieves high prediction accuracy (MAE <span><math><mo>&lt;</mo></math></span> 5.3%, MSE <span><math><mo>&lt;</mo></math></span> 0.7%, RMSE <span><math><mo>&lt;</mo></math></span> 8.3%) and high computational efficiency (reconstruction time <span><math><mo>&lt;</mo></math></span> 0.54s), highlighting its potential as a reliable solution for real-time reconstruction of fire temperature fields.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104389"},"PeriodicalIF":9.1,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261917","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":"A fuzzy feature integration-enhanced network for surface defect detection of no-service rails","authors":"Liming Huang ,&nbsp;Aojun Gong","doi":"10.1016/j.compind.2025.104382","DOIUrl":"10.1016/j.compind.2025.104382","url":null,"abstract":"<div><div>Surface defect detection on no-service rails is crucial for ensuring the safety and reliability of industrial manufacturing processes. Vision-based detection methods have seen significant progress in this domain. However, this task still faces significant challenges from the following aspects: (1) The great amounts of noise in rail surface defect images; (2) Great similarity between the foreground and background of defect images. Fuzzy logic, a significant technique in the automatic control, is effective for handling uncertain and imprecise features to improve the stability of detection systems. In this paper, we propose a novel approach by integrating fuzzy logic into deep neural networks for rail surface defect detection. First, we introduce a fuzzy logic-based feature enhancement module, where a Gaussian-based fuzzy strategy is utilized to improve feature representation. Next, we devise a fuzzy logic-based loss function tailored for fuzzy features which ensures that the fuzzy representation of features is beneficial for defect segmentation. Experimental validation using both RGB and RGB-depth images demonstrates the competitive and promising performance of our proposed approach compared to state-of-the-art models. Extensive validation on strip steel surface defect detection and salient object detection in natural images further confirms the effectiveness of our model and the application of fuzzy logic. Furthermore, this paper discusses the research significance and potential applications of the proposed methodology across various domains.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104382"},"PeriodicalIF":9.1,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261941","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":"SymmFlow: Unsupervised anomaly detection via symmetric normalizing flow","authors":"Zeyu Zhang ,&nbsp;Danqing Kang ,&nbsp;Biaohua Ye ,&nbsp;Jianhuang Lai","doi":"10.1016/j.compind.2025.104393","DOIUrl":"10.1016/j.compind.2025.104393","url":null,"abstract":"<div><div>Anomaly detection in industrial imaging has attracted significant research interest due to its critical applications. Recent advancements have demonstrated the potential of normalizing flows for unsupervised anomaly detection. However, conventional approaches often face challenges with the degeneracy of transformed distributions, especially in scenarios where anomaly samples are both scarce and subtle. To overcome these challenges, we propose a symmetrically structured normalizing flow model called SymmFlow. SymmFlow addresses the degeneracy of transformed distributions by maintaining the positive definiteness of the covariance matrix within multivariate Gaussian distributions. A novel two-stage training strategy is also proposed to stabilize training initially with regularization and subsequently reinforce the model’s robustness through symmetrical design. Extensive experiments on MVTec, VisA, and BTAD datasets demonstrate that the proposed SymmFlow outperforms existing methods, delivering superior detection accuracy both at the image and pixel levels. The source code is available at: <span><span>https://github.com/Ace-blue/SymmFlow</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104393"},"PeriodicalIF":9.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221261","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":"CASIA-Net: An indoor work site smoking detection framework","authors":"Meng Wang ,&nbsp;Mei Li","doi":"10.1016/j.compind.2025.104383","DOIUrl":"10.1016/j.compind.2025.104383","url":null,"abstract":"<div><div>Detecting smoking behaviors in indoor work sites poses significant challenges due to the small scale of targets, poor visibility, and cluttered environments. These factors significantly heighten the risk of fire hazards. We propose the Context-Aware Small-Item Attention Net (CASIA-Net), a novel detection framework to address these issues. CASIA-Net incorporates a Deformable Feature Extraction (DFE) module to tackle the non-salient characteristics of smoking targets. It adaptively adjusts the convolution kernel size according to the target scale. An Adaptive Feature Attention (AFA) module is proposed to extract small objects. It enhances the attention to critical features of smoking from complex indoor work site backgrounds. To address the issue of attention drift in complex environments, a Smoker Feature Integration module is proposed to integrate the features extracted by DFE and AFA. Additionally, a dedicated dataset for indoor work site smoking detection is constructed. Experimental results demonstrate that the proposed model achieves an mAP50 of 0.917 on the dataset with a compact weight of 6MB. The proposed model demonstrates outstanding accuracy, robustness, and lightweight design. It is highly suitable for deployment in complex indoor work sites and industrial applications.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104383"},"PeriodicalIF":9.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221260","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":"Securing additive manufacturing with blockchain-based cryptographic anchoring and dual-lock integrity auditing","authors":"Mahender Kumar,&nbsp;Gregory Epiphaniou,&nbsp;Carsten Maple","doi":"10.1016/j.compind.2025.104395","DOIUrl":"10.1016/j.compind.2025.104395","url":null,"abstract":"<div><div>The Additive Manufacturing (AM) industry is a multibillion-dollar sector offering numerous benefits, such as customisation and efficiency. However, the rise in cyber–physical attacks significantly threatens its growth and security. These attacks target the integrity of digital design files, intellectual property (IP), and interconnected AM systems’ security. To address these security challenges, we propose Securing AM with Blockchain-based Cryptographic Anchoring and Dual-lock Integrity Auditing (SAM-BCADA), a system that ensures the security, integrity, and authenticity of 3D objects within the AM supply chain. SAM-BCADA utilises a dual-lock approach to connect the physical attributes of a product with its digital counterpart, improving traceability and security. By leveraging private permissioned blockchain technology, the system enables robust tracking, authentication, and verification of a product within the AM supply chain. Additionally, SAM-BCADA has developed a distributed off-chain storage for G-code data files that includes an advanced integrity auditing system using homomorphic verifiable tags. This allows for secure and efficient verification of data integrity without compromising confidentiality. This integrated approach addresses critical vulnerabilities in the AM process, providing a reliable framework for safeguarding digital and physical assets. The security analysis shows that SAM-BCADA is secure against product counterfeiting and IP theft and outperforms state-of-the-art schemes. Performance analyses have revealed that SAM-BCADA is computationally and communication-efficient and scalable in a distributed AM environment.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"173 ","pages":"Article 104395"},"PeriodicalIF":9.1,"publicationDate":"2025-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145228758","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}