Computers in Industry最新文献

{"title":"State estimation for industrial desiccant air dryers using hybrid mechanistic and machine learning models","authors":"Sida Chai ,&nbsp;Xiangyin Kong ,&nbsp;Mehmet Mercangöz","doi":"10.1016/j.compind.2025.104274","DOIUrl":"10.1016/j.compind.2025.104274","url":null,"abstract":"<div><div>Industrial gas drying systems with twin silica gel packed beds are widely used to remove moisture from gas streams, alternating between drying and regeneration phases to maintain continuous operation. This paper presents an integrated solution for real-time estimation of water content within the packed beds of such a system used for drying process air. First, two mechanistic models were developed and validated using gas exit temperature data, achieving average prediction error rates of 5.6% and 5.2% for the drying and regeneration processes, respectively. These mechanistic model prediction errors were then used to train a machine learning model, reducing previous prediction errors to 1.2% and 3.7%. The models were subsequently combined into a hybrid structure and embedded within a moving horizon estimation framework to monitor the internal states of the mechanistic model in real-time. This approach meets observability requirements and provides a more robust solution than open-loop predictions, as demonstrated in the paper through studies using historical process data for both drying and regeneration operations. Further analysis of the regeneration phase revealed that the quantity of heated air and heating duration exceed requirements for water desorption, indicating potential areas for energy optimization using the proposed state estimation solution.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104274"},"PeriodicalIF":8.2,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592977","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":"Multi-similarity and gradient fusion digital twins for fault detection and diagnosis of rolling bearings","authors":"Xiaotian Zhang,&nbsp;Xue Wang,&nbsp;Haiming Yao,&nbsp;Wei Luo,&nbsp;Zhenfeng Qiang,&nbsp;Donghao Luo","doi":"10.1016/j.compind.2025.104273","DOIUrl":"10.1016/j.compind.2025.104273","url":null,"abstract":"<div><div>In rolling bearing application scenarios, the challenges of acquiring faulty data have led to research focusing on unsupervised fault detection and diagnosis methods trained solely on healthy data. In this study, we built a deep digital twin of a healthy rolling bearing state by combining multi-similarity metrics and a model backpropagation mechanism to fully mine fault information and achieve advancements in both fault detection and diagnosis. Our proposed approach establishes a fault scoring metric set (FSMS) by integrating multi-similarity metrics and model gradient information. Furthermore, a selection and fusion strategy for the FSMS is designed based on the old stage generated validation datasets to obtain a fusion fault scoring metric and realize fault detection. A gradient fusion digital twin is further proposed for fault diagnosis. The method fuses time–frequency and model gradient features to distinguish different fault modes. To verify the effectiveness of the proposed method, experiments were conducted on rolling bearing datasets. The experimental results show that the proposed method has excellent performance, effectively integrating the fault information embedded in multi-similarity metrics and gradient information, while exhibiting strong robustness and generalization to variations in hyperparameters. This study provides a promising new idea for digital twins in fault prediction and health management of rolling bearings.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104273"},"PeriodicalIF":8.2,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143578908","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":"ArchiDiff: Interactive design of 3D architectural forms generated from a single image","authors":"Jun Yin ,&nbsp;Wen Gao ,&nbsp;Jizhizi Li ,&nbsp;Pengjian Xu ,&nbsp;Chenglin Wu ,&nbsp;Borong Lin ,&nbsp;Shuai Lu","doi":"10.1016/j.compind.2025.104275","DOIUrl":"10.1016/j.compind.2025.104275","url":null,"abstract":"<div><div>3D Reconstruction Using Images has made strides in small-scale, uncomplicated scenes but struggles with complex, large-scale architectural forms. Targeting early-stage architectural design, we introduce ArchiDiff, a platform for 3D architectural form generation and editing from images to point clouds. First, we curated a dataset specifically tailored for architectural design, ArchiCloudNet. Second, we proposed a 3D generation method using a conditional denoising diffusion model, with an arbitrary object segmentation model to enhance recognition capabilities in complex input. Finally, we incorporate an interactive feature enabling instantaneous 2D image editing through simple drag-and-drops with simultaneous updates to 3D forms, giving designers improved control. We evaluated ArchiDiff’s generation accuracy against cutting-edge baselines on ArchiCloudNet and two other datasets, RealCity3D and BuildingNet. We also validated it with real sketches from early-stage architectural design. The experiments indicated that our model could generate accurate architectural point clouds, providing rapid-response modification and effective processing of complex backgrounds. Demostration: <span><span>http://39.101.72.109:3000/archidiff</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104275"},"PeriodicalIF":8.2,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563698","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":"Adaptive human-computer interaction for industry 5.0: A novel concept, with comprehensive review and empirical validation","authors":"Rania Hamdani,&nbsp;Inès Chihi","doi":"10.1016/j.compind.2025.104268","DOIUrl":"10.1016/j.compind.2025.104268","url":null,"abstract":"<div><div>This paper explores the domain of adaptive Human-Computer Interaction (HCI) within the emerging context of Industry 5.0, which marks the transition from Industry 4.0 by emphasizing human-centric approaches and collaboration between humans and intelligent systems. It focuses on enhancing user experience while maintaining high security standards. The complexity of intelligent industrial environments introduces various challenges, including vulnerabilities caused by faults that can propagate across multiple layers and heterogeneous systems. Predictive maintenance becomes more complicated due to the need for advanced monitoring across interconnected systems, and the dynamic nature of these environments demands seamless adaptation. This is essential for fostering harmony between human operators and intelligent systems in real-time. However, the increasing complexity of these environments reveals the limitations of the current HCI models, making adaptability a basic requirement. This paper carries a complete 141-paper review regarding research on HCI along with a detailed exposition of the core architecture, enabling technologies, and real-world application examples. Furthermore, it presents an innovative conceptual model for implementing an HCI system that dynamically adapts to environmental changes, user behavior, diverse user profiles, and varying accessibility needs, specifically within the context of fault detection and diagnosis systems. The proposed approach has been rigorously validated through empirical studies, demonstrating its effectiveness and practical applicability. To improve resiliency and efficiency in such a smart industrial system as is vulnerable, the interface would adapt itself to user behavior and diversity. It addresses the challenges that arise during this dynamic and complex environment that would ensure a secure and seamless interaction.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104268"},"PeriodicalIF":8.2,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multipoint dynamic displacement monitoring of long-span beam bridges and their time-space evolution using a camera-chain system","authors":"Wenjun Chen ,&nbsp;Yihe Yin ,&nbsp;Biao Hu ,&nbsp;Qifeng Yu ,&nbsp;Xiaolin Liu ,&nbsp;Yueqiang Zhang ,&nbsp;Zhendong Ge ,&nbsp;Xiaohua Ding","doi":"10.1016/j.compind.2025.104271","DOIUrl":"10.1016/j.compind.2025.104271","url":null,"abstract":"<div><div>Deflection and lateral displacement are critical factors in bridge structural health monitoring. Vision-based displacement monitoring techniques have advantages, such as full-field coverage, high precision, real-time feedback, and automation. However, existing methods still face two key problems that limit their field application: a) an inherent trade-off between measurement range and accuracy, and b) the effect of environmental disturbance on observation platform stability. This paper proposes a camera-chain-based multipoint displacement measurement system for long-span beam bridges. The system primarily consists of double-head camera stations linked by artificial markers. By connecting the optical paths of the camera stations in the physical setup and compensating for motion-induced errors in the measurement model, the system can be deployed along a deformed beam to enable synchronized dynamic measurements at multiple points. Comparative field tests demonstrate that the displacement measurements obtained from this system are consistent with those from traditional methods, such as manual leveling and automated connecting pipe systems, while providing advantages in terms of dynamic monitoring and the breadth of measurement parameters. Benefiting from real-time multipoint measurement, the system captures detailed spatial deformation curves. In addition, the observed vehicle-induced evolution of bridge deflection aligns well with the recorded vehicle speed and can be used to evaluate the modal parameters of beam bridges. The proposed method and system can provide new options and better data for bridge displacement monitoring to support the accuracy and timeliness of safety warnings.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104271"},"PeriodicalIF":8.2,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An evaluation scheme incorporating digital characteristics for transient tribological behaviours under complex loading conditions for the hot stamping process","authors":"Heli Liu ,&nbsp;Xiao Yang ,&nbsp;Denis Politis ,&nbsp;Huifeng Shi ,&nbsp;Liliang Wang","doi":"10.1016/j.compind.2025.104270","DOIUrl":"10.1016/j.compind.2025.104270","url":null,"abstract":"<div><div>The growing availability of metal forming data has driven a new era of data-centric approaches in digital manufacturing. This wealth of data enables the development of digitally enhanced metal forming processes and associated technologies. In this work, using the hot stamping data obtained from a cloud-based manufacturing database, the digital characteristics (DC), defined as the visualisation of a specific manufacturing process containing essential information spanning over the design, manufacturing, and application phases of the products, were unlocked for the hot stamping process. The complex contact conditions were successfully visualised by the hot stamping DC. Following this discovery, the performance of transient lubricant behaviours was evaluated under complex loading and constant loading conditions regarding coefficient of friction evolution and lubricant limit diagram (LLD), which is a digitally-enhanced approach to enable the quantitative evaluation of different lubricants. Results demonstrate that the efficacy of DC-enhanced methodology facilitates the insightful comprehension of transient tribological behaviours and offers great potential on customised lubricant development towards optimisation of hot stamping and metal forming processes.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104270"},"PeriodicalIF":8.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic obstacle avoidance control based on a novel dynamic window approach for agricultural robots","authors":"Jichun Wang ,&nbsp;Liangliang Yang ,&nbsp;Haiyan Cen ,&nbsp;Yong He ,&nbsp;Yufei Liu","doi":"10.1016/j.compind.2025.104272","DOIUrl":"10.1016/j.compind.2025.104272","url":null,"abstract":"<div><div>With the ongoing advancements in autonomous navigation technology, agricultural robots are increasingly being deployed across various sectors of agriculture. Among the critical components of this technology, dynamic obstacle avoidance in complex agricultural environments serves as the foundation for enhancing the autonomy and safety of these robots. The Dynamic Window Approach (DWA) is a widely recognized method for achieving local obstacle avoidance. It operates by sampling the robot's velocity space and then evaluating the sampled trajectories using a value function to determine the optimal velocity pair. However, a significant limitation of the traditional DWA method lies in its fixed weights for the value function, which restricts its performance to manual tuning and renders it less adaptable to intricate and dynamic obstacle environments. To address this limitation, we introduced an innovative approach by integrating the Twin Delayed Deep Deterministic Policy Gradient (TD3) method into the weight determination process of the DWA algorithm's value function. This integration enabled the weight coefficients to adapt dynamically in response to environmental variations, thereby enhancing the algorithm's flexibility and effectiveness. Our extensive simulation and field testing revealed that while the traditional DWA algorithm struggled to navigate complex dynamic obstacle environments, the proposed TD3-DWA algorithm achieved a success rate of over 90 % in obstacle avoidance. This outcome underscored the algorithm's adaptability and robustness, positioning it as a reliable solution for ensuring safe and efficient navigation in agricultural robotics.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104272"},"PeriodicalIF":8.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143508518","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":"Physics-informed digital twin design for supporting the selection of process settings in continuous manufacturing, with a focus in fiberboard production","authors":"Francisco Ambrosio Garcia ,&nbsp;Hendrik Devriendt ,&nbsp;Hüseyin Metin ,&nbsp;Merih Özer ,&nbsp;Frank Naets","doi":"10.1016/j.compind.2025.104267","DOIUrl":"10.1016/j.compind.2025.104267","url":null,"abstract":"<div><div>In process industry, plant operators often rely on their experience to choose suitable process settings that meet the productivity and quality goals. When these goals are not met, multiple changes to the settings might be necessary, which is time-consuming because each adjustment requires waiting for the new steady-state condition. A digital twin that quickly provides key performance indicators in steady-state as a function of these settings can speed up this task. The settings can be manually simulated before being adopted, or the digital twin can be integrated into an optimizer to automatically suggest optimal values to the operator, who ultimately makes the final decision. Despite advances in approaches to design such digital twins, most studies lack strategies to update the models when the plant behavior changes, and often overlook constraints and human-centric aspects of the plant operation. To address these gaps, we present a framework for training, tuning, and updating models for supporting the selection of process settings in continuous manufacturing. By directly mapping the steady-state conditions as a function of process settings, our approach enables informed decision-making and paves the way towards process optimization without requiring modifications to the plant control software, a crucial factor in established plants to ensure safety. We propose an interpretable model architecture, and a training process that incorporates both data and prior physical knowledge. Triggers detect deviations between the models’ predictions and the plant condition, in order to start model updates. The procedure for updating the models is tuned to perform consistently well in a variety of conditions, based on substantial simulations in historical data. To select the triggers, we balance technical and human aspects, by considering the trade-off between frequent model updates, increasing operator workload with frequent settings changes, versus how closely the models track the plant conditions. The framework is applied to five different stages of the fiberboard production process in a 1.4-year dataset, to predict key energy and quality-related variables as a function of process settings. The results show that the models, when connected to the data stream, are effectively updated when needed, show high sensitivity to the process settings and consistency with the available physical knowledge, making them well-suited to support the selection of process settings.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104267"},"PeriodicalIF":8.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488703","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":"UGP-KD: An unsupervised generalized prediction framework for robot machining quality under historical task knowledge distillation for new tasks","authors":"Teng Zhang ,&nbsp;Fangyu Peng ,&nbsp;Zhao Yang ,&nbsp;Xiaowei Tang ,&nbsp;Rong Yan","doi":"10.1016/j.compind.2025.104269","DOIUrl":"10.1016/j.compind.2025.104269","url":null,"abstract":"<div><div>Despite the extensive use of robots in numerous fields, condition-sensitive robotic machining errors represent a significant obstacle to their high-precision implementation. Prediction-based compensatory control represents a crucial approach to enhancing robot accuracy. The extant machining error prediction methods are beset with shortcomings, including inadequate feature extraction, limited generalizability with respect to working conditions, and the squandering of knowledge. Therefore, the influence mechanisms of robot errors by different working conditions and spatial ontology properties are explored in this paper. A spatial-temporal dual-view error prediction model is constructed for a single condition. Moreover, an innovative unsupervised generalized prediction strategy of machining error for new conditions under the historical task knowledge distillation of Multi-Teacher-Single-Student (MTSS) is proposed. This strategy enables the extraction and reuse of knowledge at three levels: teacher-teaching, student-learning, and generalized expansion. It also ensures the high-precision, lightweight, and high-efficiency prediction of machining error for unseen conditions. The proposed method was validated on constructed complex part inner wall features. The minimum mean absolute error (MAE) indicator for single condition prediction is 0.005 mm, which is a significantly more accurate result than other methods under comparison. Furthermore, the average MAE of unsupervised generalization for new conditions is 0.019 mm, which meets the practical application requirements. Furthermore, the distilled model complexity is reduced by 75 %, and the average inference efficiency is enhanced by over 95 %. This provides the potential for lightweight online deployment. The proposed method offers a robust foundation for prediction-based error online compensation, which is anticipated to facilitate the expansion of robots in high-precision scenarios.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"168 ","pages":"Article 104269"},"PeriodicalIF":8.2,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488702","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":"DFSDNet: A dual-branch multi-scale feature fusion network for surface defect detection of copper strips and plates","authors":"Fajia Wan,&nbsp;Guo Zhang,&nbsp;Zeteng Li","doi":"10.1016/j.compind.2025.104265","DOIUrl":"10.1016/j.compind.2025.104265","url":null,"abstract":"<div><div>Surface defect detection is a research hotspot in the field of computer vision. Due to the complex characteristics of metal surfaces and the multitude of industrial defects, it remains a challenging task. In order to meet the requirement of accurate identification of surface defects on copper strips and plates in industrial quality control, we propose a computer vision-based dual-branch features fusion neural network named as DFSDNet. We gather defect samples to construct the KUST-DET dataset of surface defects on copper strips and plates to support the training and evaluation of detection models. Experiments on the KUST-DET dataset demonstrate that DFSDNet-s achieved a mean Average Precision (mAP) of 88.53%, while maintaining low computational complexity and low parameters, and the model achieves a good balance between detection precision and computational efficiency. In addition, the mAP on the NEU-DET dataset is 75.67%, showcasing its good defect detection performance. Experiments have shown that DFSDNet is an effective surface defect detection model with great potential in other metal industry applications.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"167 ","pages":"Article 104265"},"PeriodicalIF":8.2,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479623","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}