Computers in Industry最新文献

{"title":"A novel paradigm for predicting and interpreting uneven roll wear in the hot rolling steel industry","authors":"Wen Peng ,&nbsp;Cheng-yan Ding ,&nbsp;Yu Liu ,&nbsp;Jia-nan Sun ,&nbsp;Zhen Wei ,&nbsp;Wen-bo Wang ,&nbsp;Dian-hua Zhang ,&nbsp;Jie Sun","doi":"10.1016/j.compind.2025.104318","DOIUrl":"10.1016/j.compind.2025.104318","url":null,"abstract":"<div><div>In the hot rolling industry, uneven roll wear significantly influences schedule free rolling and product quality, necessitating more precise wear prediction to improve the capabilities of hot rolling production. However, existing methods, laden with limitations, struggle to predict uneven roll wear precisely and transparently. To address these challenges, we present a novel paradigm that combines a computer simulation technique, classical wear theory and a data-driven approach for predicting uneven work roll wear in the hot rolling industry. Initially, a finite element model is constructed to simulate hot rolling processing. Subsequently, an Archard-theory-based work roll wear model is derived to calculate the theoretical wear loss using the simulation results. Following this, based on the theoretical wear loss, a deep ensemble model containing three base predictors is established. Notably, Shapley additive explanations (SHAP) and ensemble mechanism analysis are implemented to explain the predictive process of the wear loss. The comparative experimental results demonstrate the deep ensemble method achieves a 2 % accuracy improvement over other machine learning models. Additionally, the wear prediction results for a real case of a roll change period prove that, at the peak position of wear profile, the proposed paradigm surpasses the existing model by 7.2 %. Significantly, the feature contributions and process interpretable analysis based on SHAP make the proposed paradigm both transparent and reliable.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"170 ","pages":"Article 104318"},"PeriodicalIF":8.2,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144099280","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 novel and scalable multimodal large language model architecture Tool-MMGPT for future tool wear prediction in titanium alloy high-speed milling processes","authors":"Caihua Hao ,&nbsp;Zhaoyu Wang ,&nbsp;Xinyong Mao ,&nbsp;Songping He ,&nbsp;Bin Li ,&nbsp;Hongqi Liu ,&nbsp;Fangyu Peng ,&nbsp;Weiye Li","doi":"10.1016/j.compind.2025.104302","DOIUrl":"10.1016/j.compind.2025.104302","url":null,"abstract":"<div><div>Accurately predicting the future wear of cutting tools with variable geometric parameters remains a significant challenge. Existing methods lack the capability to model long-term temporal dependencies and predict future wear values—a key characteristic of world models. To address this challenge, we introduce the Tool-Multimodal Generative Pre-trained Transformer (Tool-MMGPT), a novel and scalable multimodal large language model (MLLM) architecture specifically designed for tool wear prediction. Tool-MMGPT pioneers the first tool wear world model by uniquely unifying multimodal data, extending beyond conventional static dimensions to incorporate dynamic temporal dimensions. This approach extracts modality-specific information and achieves shared spatiotemporal feature fusion through a cross-modal Transformer. Subsequently, alignment and joint interpretation occur within a unified representation space via a multimodal-language projector, which effectively accommodates the comprehensive input characteristics required by world models. This article proposes an effective cross-modal fusion module for vibration signals and images, aiming to fully leverage the advantages of multimodal information. Crucially, Tool-MMGPT transcends the limitations of traditional Large Language Models (LLMs) through an innovative yet generalizable method. By fundamentally reconstructing the output layer and redefining training objectives, we repurpose LLMs for numerical regression tasks, thereby establishing a novel bridge that connects textual representations to continuous numerical predictions. This enables the direct and accurate long-term forecasting of future wear time series. Extensive experiments conducted on a newly developed multimodal dataset for variable geometry tools demonstrate that Tool-MMGPT significantly outperforms state-of-the-art (SOTA) baseline methods. These results highlight the model's superior long-context modeling capabilities and illustrate its potential for effective deployment in environments with limited computational resources.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104302"},"PeriodicalIF":8.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143886351","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 simple and reliable semi-supervised anomaly detection network for detecting crack in stamped parts","authors":"Xingjun Dong ,&nbsp;Changsheng Zhang ,&nbsp;Shuaitong Liu ,&nbsp;Dawei Wang","doi":"10.1016/j.compind.2025.104301","DOIUrl":"10.1016/j.compind.2025.104301","url":null,"abstract":"<div><div>Stamped parts play a crucial role in industrial manufacturing, and it is particularly important to automatically inspect their surface cracks. Since crack is rare and diverse, supervised defect detection methods lack sufficient data and cannot achieve ideal results. Unsupervised anomaly detection algorithms, which do not require crack data, can identify unknown cracks. However, they tend to have high rates of missed detections and false positives when dealing with complex backgrounds in stamped parts. To address these problems, this paper proposes a network called simple and reliable semi-supervised anomaly detection, considering the presence of a small number of anomalous data in actual production. This network uses a large number of normal samples and a small number of anomalous samples to detect surface cracks in stamped parts. Firstly, a pre-trained feature extractor is used for feature extraction, coupled with a designed feature adaptation network to reduce domain bias. Secondly, by extracting normal features from normal images, adding noise to these normal features to generate abnormal features, and extracting abnormal features from abnormal images at multiple scales, a feature space is constructed. Finally, by training a simplified discriminator based on the constructed feature space, computational efficiency is enhanced, and the deployment process is simplified. In the experiments, we collaborated with a multinational company, using an actual production dataset for verification. The proposed algorithm can achieve the score of area under the receiver operating characteristic curve of 98.2% for detection and 97.9% for localization at a processing speed of 19 frames per second.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104301"},"PeriodicalIF":8.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143874165","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":"Toward laser-assisted cutting: A real-time segmentation method for reinforcing particles in particle-reinforced metal matrix composites","authors":"Jixiang Ding ,&nbsp;Zhengding Zheng ,&nbsp;Shayu Song ,&nbsp;Long Bai ,&nbsp;Jianfeng Xu ,&nbsp;Jianguo Zhang ,&nbsp;Wenjie Chen","doi":"10.1016/j.compind.2025.104305","DOIUrl":"10.1016/j.compind.2025.104305","url":null,"abstract":"<div><div>Particle-reinforced metal matrix composites (PRMMCs) are widely used because of their exceptional material properties. Online control of the laser field to soften and modify the reinforcing particles on the machined surface of the composites is an effective way to improve the machinability and machining quality of PRMMCs. A real-time segmentation method for reinforcing particles in PRMMCs is proposed. First, real-time acquisition of reinforcing particle images along the processing path is achieved using machine vision, and cutting region images are determined. Next, to improve the model’s ability to effectively segment the reinforcing particles in low-resolution images of the machining region, a reinforcing particle segmentation network (RPSNet) is proposed, incorporating a multimodal fusion and space-to-depth convolution module. Subsequently, position signals along the cutting direction are obtained by using a sliding window method. The effectiveness of each module and the performance of the model are analyzed and verified through comparative and ablation experiments. The results demonstrated that the proposed RPSNet achieved a mean average precision (mAP) of 95.4 % in segmenting reinforcing particles, with an inference time of 5.8 ms. In comparison to other methods, it demonstrated better real-time performance and accuracy. Additionally, the proposed method can convert image information into position signals, thus enabling real-time control of the laser for softening and modifying the reinforcing particles.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104305"},"PeriodicalIF":8.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869739","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 task-oriented physical collaborative network for pipeline defect diagnosis in a magnetic flux leakage detection system","authors":"Xiangkai Shen ,&nbsp;Jinhai Liu ,&nbsp;Yifu Ren ,&nbsp;Lin Jiang ,&nbsp;Lei Wang ,&nbsp;He Zhao ,&nbsp;Rui Li","doi":"10.1016/j.compind.2025.104290","DOIUrl":"10.1016/j.compind.2025.104290","url":null,"abstract":"<div><div>Defect diagnosis based on magnetic flux leakage (MFL) signals is an important process for assessing pipeline health, including defect detection and size quantification. However, existing studies suffer from poor consistency of results, because they regard defect detection and size quantification as separate tasks, lacking paradigm harmonization and interaction. In addition, the calibration of experts is required to achieve harmonization between the two, which increases the time cost of data analysis. To address the above challenges, our motivation is to synergistically learn two tasks within a unified framework and utilize their task properties for mutual benefit. Therefore, a novel defect diagnosis method based on a task-oriented physical collaborative network (TOPC-Net) is proposed, which is the first attempt at joint defect detection and size quantification in MFL inspection. First, a feature extraction subnetwork with a heterogeneous focus module is proposed to decompose initial task-specific features from shared spaces. Second, considering the strong correlation between the two tasks, a cross-task information awareness method is proposed to realize the information interaction between the two tasks, so that the task-specific features can be enhanced. Finally, a physical information-guided collaborative decision subnetwork is proposed to jointly optimize two tasks, where MFL domain knowledge is embedded into the subnetwork to provide expert guidance, ensuring the accuracy and stability of predictions. Experimental results show that the proposed method outperforms existing methods, with a detection accuracy of 96.0% and an average improvement of 7.5% in quantification accuracy, which makes it promising for industrial applications.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104290"},"PeriodicalIF":8.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869732","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":"Gradient-free physics-informed neural networks (GF-PINNs) for vortex shedding prediction in flow past square cylinders","authors":"Chunhao Jiang ,&nbsp;Nian-Zhong Chen","doi":"10.1016/j.compind.2025.104304","DOIUrl":"10.1016/j.compind.2025.104304","url":null,"abstract":"<div><div>Physics-informed neural networks (PINNs) face significant challenges to predict the vortex shedding in the flow past a two-dimensional cylinder, mainly due to complex loss landscapes, spectral bias, and a lack of inductive bias towards periodic functions. To overcome these challenges, a novel gradient-free PINN (GF-PINN) coupled with a U-Net+ + architecture is proposed. For optimizing the complex loss landscape, the skip pathways in U-Net+ + are redesigned to reduce the semantic gap between encoder and decoder feature maps. Then, the stream function instead of velocity, is used as the input and output for the neural network, ensuring flow incompressibility and reducing output dimensionality. This approach aims to overcome the inherent problems of spectral bias and the lack of inductive bias towards periodic functions in PINNs. Furthermore, gradient-free convolutional filters are employed to approximate the derivative terms in the loss function to further optimize the complex loss landscape. A series of numerical experiments and dynamic mode analyses are conducted and the results show that the vortex shedding in the wake of a square cylinder is successfully captured by the proposed model and the estimated drag coefficients and Strouhal numbers are in a good agreement with those predicted by traditional methods. In addition, numerical experiments also show that the model exhibits great capabilities of generalization and extrapolation. This work demonstrates the potential of PINN-based models to effectively solve complex fluid dynamics problems.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104304"},"PeriodicalIF":8.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869738","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":"3D modeling from a single image via a novel dual-decoder framework for Agile design","authors":"Jieyang Peng ,&nbsp;Andreas Kimmig ,&nbsp;Simon Kreuzwieser ,&nbsp;Zhibin Niu ,&nbsp;Xiaoming Tao ,&nbsp;Jivka Ovtcharova","doi":"10.1016/j.compind.2025.104303","DOIUrl":"10.1016/j.compind.2025.104303","url":null,"abstract":"<div><div>In the fast-paced manufacturing industry, rapid and efficient product design is essential for meeting customer demands and maintaining a competitive edge. Despite advancements, transforming 2D design concepts into accurate 3D models remains a complex challenge, primarily due to the non-differentiability of traditional rendering processes that hinder gradient-based optimizations. To address this limitation, this paper introduces an innovative dual-decoder architecture that effectively separates the shape and color components of 3D models. By assigning separate decoders for vertex positions and color assignment, our proposed model enables targeted optimization of each, leading to more refined and authentic 3D reconstructions. Moreover, we have overcome the non-differentiability issue, enabling gradient-based learning through the incorporation of differentiable rendering techniques. These techniques facilitate gradient-based optimization, paving the way for data-driven enhancements in the design process. Our empirical research has demonstrated the effectiveness of our approach in generating high-fidelity 3D models from 2D inputs. Additionally, we have shed light on the sensitivity of hyperparameters within our framework, offering valuable insights for future model refinement and optimization. In summary, our research provides valuable insights into enhancing 3D modeling frameworks, thereby contributing to incremental progress in the field of computer-aided design and manufacturing.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104303"},"PeriodicalIF":8.2,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869740","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":"Interactions between BIM and robotics: Towards intelligent construction engineering and management","authors":"Hongzhe Yue ,&nbsp;Qian Wang ,&nbsp;Zixuan Zhao ,&nbsp;Sha Lai ,&nbsp;Guanying Huang","doi":"10.1016/j.compind.2025.104299","DOIUrl":"10.1016/j.compind.2025.104299","url":null,"abstract":"<div><div>The interactions between robotics and Building Information Modeling (BIM) are revolutionizing the construction industry by fostering smarter, more adaptive, and efficient workflows. BIM provides robots with geometric and semantic information for precise task execution, while robots contribute real-time as-built data to update and refine BIM models. Despite its significant potential, research on BIM-robotics interactions is still in the early stages and lacks comprehensive reviews. This paper presents a detailed review of the BIM-robotics interactions in the construction industry. A two-fold literature search was conducted, resulting in the collection of 92 research papers published since 2015. Four key applications are identified: task planning, intelligent assembly, 3D printing, and inspection. Additionally, the role of robotics in facilitating BIM model generation is discussed. To address challenges such as data interoperability and the absence of standardized frameworks, this study proposes a four-layer interaction framework: Foundation Layer, Application Layer, Communication Layer, and Intelligence Layer. This framework aims to enhance BIM-robotics synergy, enabling seamless data exchange and advancing intelligent construction and management.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104299"},"PeriodicalIF":8.2,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855382","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":"Deformation-aware positioning optimization in aircraft assembly using surrogate model-assisted deep reinforcement learning","authors":"Yifan Zhang ,&nbsp;Ye hu ,&nbsp;Wenxu Luo ,&nbsp;Qing Wang ,&nbsp;Liang Cheng ,&nbsp;Yinglin Ke","doi":"10.1016/j.compind.2025.104300","DOIUrl":"10.1016/j.compind.2025.104300","url":null,"abstract":"<div><div>Assembly positioning processes play a crucial role in determining the final manufacturing precision of aircraft components. Traditional methods typically treat components as rigid bodies, focusing on adjusting their position and orientation while overlooking the complexities associated with deformable structures. This paper proposes an innovative methodology to optimize the positioning process of aircraft components by incorporating deformation considerations. A two-stage surrogate model, enhanced by machine learning techniques, is introduced to approximate the deformation of structures under various locator configurations. Deep Reinforcement Learning (DRL) is subsequently applied to leverage the surrogate model-based simulation environment. The high-dimensional stress field, compressed by the surrogate model, is used as the state input for the DRL agent, significantly reducing training complexity and enhancing stability. The agent's action corresponds to adjusting the locator's end effector position, while the reward function is designed to minimize the deformation indicator. Upon training, the resulting policy demonstrates strong generalization on the test dataset, achieving a median structural deformation reduction of 99.3 %, with 95 % of the test samples showing a reduction of over 92 %. This approach not only improves the precision but also increases the productivity of aircraft assembly, establishing a new benchmark for intelligent assembly systems that involve deformable components.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104300"},"PeriodicalIF":8.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855381","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":"Synchronized identification and localization of defect on the bottom of steel box girders based on a dynamic visual perception system","authors":"Wang Chen ,&nbsp;Binhong Yuan ,&nbsp;Dongliang Chen ,&nbsp;Yong Hu ,&nbsp;Feiyu Wang ,&nbsp;Jian Zhang","doi":"10.1016/j.compind.2025.104291","DOIUrl":"10.1016/j.compind.2025.104291","url":null,"abstract":"<div><div>Inspecting the underside of large-span bridges is a major challenge due to the extensive area and inaccessibility. This study developed a system that integrates advanced equipment with intelligent algorithms, designed to achieve precise identification and rapid localization of defects on the underside of bridges. The key components of the system are summarized as follows: (1) The dynamic visual perception system is composed of a perception module, a control and transmission module, and a motion module. It enables automated data collection at any position beneath the bridge structure. (2) A block-based panoramic generation strategy is employed, which uses a spatially ordered block concept to simplify the panorama stitching process and enhance accuracy. (3) Deep learning-driven two-phase synchronous identification and localization method. In the first phase, MobileNetV4 serves as the primary feature representation tool, facilitating the lightweight reconstruction of panoramic images. In the second phase, the YOLOv9 detection framework is employed to perform a precise analysis of the identified defect regions, providing detailed defect information on a localized level. The design of this system significantly enhances the efficiency and accuracy of inspections of large-span bridge undersides, offering robust technical support for bridge health maintenance. Experimental results indicate that the proposed method achieves over 90 % accuracy in defect recognition tasks, alongside millimeter-level precision in localization.</div></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"169 ","pages":"Article 104291"},"PeriodicalIF":8.2,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143830140","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}