Computers in Industry最新文献

{"title":"Digitally enhanced development of customised lubricant: Experimental and modelling studies of lubricant performance for hot stamping","authors":"Xiao Yang ,&nbsp;Heli Liu ,&nbsp;Vincent Wu ,&nbsp;Denis J. Politis ,&nbsp;Haochen Yao ,&nbsp;Jie Zhang ,&nbsp;Liliang Wang","doi":"10.1016/j.compind.2024.104152","DOIUrl":"10.1016/j.compind.2024.104152","url":null,"abstract":"<div><p>Digitally enhanced technologies are transforming every aspect of the manufacturing sector towards the era of digital manufacturing. Traditional lubricant development methods involving systematic but time-consuming iterative processes is still extensively used in the metal forming industry. In the present study, a novel digitally enhanced lubricant development scheme was proposed by leveraging a mechanism-based interactive friction modelling framework and quantitative and comprehensive evaluation of lubricant performance via the data-centric lubricant limit diagrams. By predicting transient lubricant behaviour following the complex contact condition evolution experienced in actual forming operations, a close association and quantified relation between the lubricant performance and its properties such as viscosity, evaporation rate and fraction of dry matter was established. This can facilitate the optimisation efficiency of lubricant parameters and minimise the experimental cost for iterative lubricant trials. A case study was conducted in this work to develop a customised lubricant using this digitally enhance scheme for the target hot stamping process based on a benchmark lubricant as a reference. Further industrial forming tests of an automotive component were conducted to validate the ideal performance of the customised lubricant.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104152"},"PeriodicalIF":8.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000800/pdfft?md5=720c572a424101129d0be812521a5372&pid=1-s2.0-S0166361524000800-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117566","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":"A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks","authors":"Zifeng Xu ,&nbsp;Zhe Wang ,&nbsp;Chaojia Gao ,&nbsp;Keqi Zhang ,&nbsp;Jie Lv ,&nbsp;Jie Wang ,&nbsp;Lilan Liu","doi":"10.1016/j.compind.2024.104155","DOIUrl":"10.1016/j.compind.2024.104155","url":null,"abstract":"<div><p>In industrial sectors such as shipping, chemical processing, and energy production, centrifugal pumps often experience failures due to harsh operational environments, making it challenging to accurately identify fault types. Traditional fault diagnosis methods, which heavily rely on existing fault datasets, suffer from limited generalization capabilities, especially when substantial labeled and specific fault sample data are lacking. This paper proposes a novel fault diagnosis approach for centrifugal pumps, utilizing a digital twin (DT) framework powered by a graph transfer learning model to address this issue. Firstly, a high-fidelity DT model is constructed to simulate the flow-induced vibration response of the impeller under different health states to enrich the type and scale of the dataset. Secondly, a graph convolutional neural networks (GCN) model is constructed to learn the knowledge of simulation data, and the Wasserstein distance between simulation data and measured data is optimized for adversarial domain adaptation, thereby achieving efficient cross-domain fault diagnosis. Experimental results demonstrate that the proposed algorithm delivers effective fault diagnosis with minimal prior knowledge and outperforms comparable models. Furthermore, the DT system developed using the proposed model enhances the operational reliability of centrifugal pumps, reduces maintenance costs, and presents an innovative application of DT technology in industrial fault diagnosis.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104155"},"PeriodicalIF":8.2,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094663","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 data-driven framework for enhancing the consistency of deposition contours and mechanical properties in metal additive manufacturing","authors":"Miao Yu,&nbsp;Lida Zhu,&nbsp;Zhichao Yang,&nbsp;Lu Xu,&nbsp;Jinsheng Ning,&nbsp;Baoquan Chang","doi":"10.1016/j.compind.2024.104154","DOIUrl":"10.1016/j.compind.2024.104154","url":null,"abstract":"<div><p>The accuracy and quality of part formation are crucial considerations. However, the laser directed energy deposition (L-DED) process often leads to irregular changes in deposition contours and mechanical properties across parts due to complex flow fields and temperature variations. Hence, to ensure the forming accuracy and quality, it is necessary to achieve precise monitoring and appropriate parameter adjustments during the processing. In this study, a machine vision method for real-time monitoring is proposed, which combines target tracking and image processing techniques to achieve accurate recognition of deposition contours under noisy conditions. Through comparative verification, the measurement accuracy reaches as high as 98.98 %. Leveraging the monitoring information, a bidirectional prediction neural network is proposed to accomplish layer-by-layer forward prediction of layer height. Meanwhile, inverse prediction is employed to determine the processing parameters required for achieving the desired layer height, facilitating the optimization of the deposition contours. It was found that as the processing parameters were adjusted layer-by-layer to achieve consistent deposition contours, there was also a tendency towards consistent changes in microstructure and mechanical properties. The standard deviations of primary dendrite arm spacing (PDAS) and ultimate tensile strength (UTS) at different positions decrease by over 52.2 % and 61.4 %, respectively. This study reveals the consistent patterns of variation in deposition contours and mechanical properties under data-driven variable parameter processing, laying an important foundation for future exploration of the complex process-structure-performance (PSP) relationship in L-DED.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104154"},"PeriodicalIF":8.2,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142094796","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":"Examining the effect of locomotion techniques on virtual prototype assessment: Gaze analysis using a Head-Mounted Display","authors":"Julia Galán Serrano ,&nbsp;Francisco Felip-Miralles ,&nbsp;Almudena Palacios-Ibáñez","doi":"10.1016/j.compind.2024.104149","DOIUrl":"10.1016/j.compind.2024.104149","url":null,"abstract":"<div><p>Improvements in the performance and graphical quality of Head-Mounted Displays (HMDs) have led to their increasing use in Virtual Reality (VR) for product presentation and virtual prototype (VP) evaluations. Various locomotion techniques in VR make it possible to move through a virtual scenario and approach the VP for evaluation purposes. The integration of eye-tracking devices into recent HMDs allows the trajectory and gaze behavior of observers to be reported during the evaluation, often more objectively than self-report questionnaires. However, very few studies have used physiological measures for the evaluation of products embedded in VR environments. Therefore, this paper offers a study in which 95 people evaluated three VPs of street furniture presented in their environment of use using Meta Quest Pro headset and explored through teleport and natural walking. The influence of the locomotion techniques on the ratings recorded using a semantic differential, sense of presence, cybersickness, and the role of eye-tracking in understanding gaze behavior while evaluating products' Areas of Interest (AOIs), are investigated. This study found no evidence that the way of approaching the product influences the evaluation of some of its features, overall product evaluation, confidence in responses, sense of presence, or cybersickness differently. On the other hand, this work evidences that the locomotion technique had an impact on how the user approached the products, which could significantly influence the viewing time of some AOIs. The study revealed that the most observed AOIs coincided with those parts closely related to important features, generally located at the top of the products, so paying special attention to these parts when designing and evaluating similar VPs is recommended.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104149"},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000770/pdfft?md5=d398aafb367f389fc5407079f358764e&pid=1-s2.0-S0166361524000770-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087577","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":"A three-directional stress-strain model-based physics-embedded prediction framework for metal tube full-bent cross-sectional characteristics","authors":"Yongzhe Xiang ,&nbsp;Zili Wang ,&nbsp;Shuyou Zhang ,&nbsp;Yaochen Lin ,&nbsp;Jie Li ,&nbsp;Jianrong Tan","doi":"10.1016/j.compind.2024.104153","DOIUrl":"10.1016/j.compind.2024.104153","url":null,"abstract":"<div><p>A metal tube system is known as the industrial blood vessel, among which the bent section is the most vulnerable part. The cross-sectional defects (CSDs) of the bent tube cause the flow fluctuation of the fluid inside the tube. The existing defect characterization methods are roughly presented by describing CSDs in some specific cross-sections, which results in the lack of the tube full-bent section (FBS) characteristic information. To comprehensively describe and predict the tube FBS characteristics, an advanced physics-embedded CSDs prediction framework is proposed. This framework includes an FBS-neutral layer displacement angle (NLDA) prediction module and an FBS-CSDs prediction module, which uses the method that integrates the analytical model and BiLSTM-based deep learning (DL) models to predict the CSDs in the FBS of the tube. A novel analytical model of CSDs that considers both three-directional stresses and strains during tube bending is embedded in the FBS-CSDs prediction module. The analytical model provides the initial predicted values of CSDs through the NLDA sequence obtained from the FBS-NLDA module. The inaccurate CSDs are then treated as physical information to be fed into DL models for further correction and prediction. The prediction performance of this framework is validated through numerical simulations and experiments. The results prove that the framework can accurately predict the CSDs in the tube FBS. The integration of DL models with the analytical model not only overcomes the limitations of the analytical model, but also improves the prediction accuracy and convergence speed of DL models.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104153"},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087578","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":"Unlocking inherent values of manufacturing metadata through digital characteristics (DC) alignment","authors":"Heli Liu ,&nbsp;Xiao Yang ,&nbsp;Maxim Weill ,&nbsp;Shengzhe Li ,&nbsp;Vincent Wu ,&nbsp;Denis J. Politis ,&nbsp;Huifeng Shi ,&nbsp;Zhichao Zhang ,&nbsp;Liliang Wang","doi":"10.1016/j.compind.2024.104148","DOIUrl":"10.1016/j.compind.2024.104148","url":null,"abstract":"<div><p>Data form the backbone of manufacturing sciences, initiating a revolutionary transformation in our understanding of manufacturing processes by unravelling complex scientific patterns embedded within them. Digital characteristics (DC) is defined as a strategic framework mapping the manufacturing metadata and integrates essential information across the entire spectrum spanning from the design, manufacturing, and application phases of manufactured products. By carrying these inherent distinctive features, DC serves as the ‘DNA’ for every manufacturing process. Through enormous experimental and simulation efforts, a digital characteristics space (DCS) was established to provide access to the up-to-date and information-rich DC repository containing over 140 manufacturing processes. In digital manufacturing, sensing networks play a pivotal role in metadata acquisition, contributing nearly 2000 petabytes of metadata annually. However, an overwhelming majority-nearly 100 %-of the data collected through sensing networks can be categorised as ‘fragmental data’, encompassing only a few (e.g., 1–2) essential pieces of information. Moreover, the current absence of efficient metadata identification methods presents an emerging and critical need to enable industry to unlock the full potential of manufacturing metadata. To this end, the authors of the present paper developed a physics-based alignment filter, considering DCS as an alignment reference similar to the ‘GenBank’. Specifically, the origins of naturally unattributed fragmental data were identified with an overall probability exceeding 82 % with a minimum length of 10 data points. The probability increased to 99 % when aligning the fragmental data with length of 100 data points. This was realised by comparing the thermo-mechanical DC of fragmental data with their counterparts stored in the DCS. Subsequently, we analysed the distinct DC of this identified manufacturing process to facilitate digitally-enhanced research. This study introduces a pioneering methodology developed to extract latent values embedded in manufacturing metadata derived from unattributed fragmental data. By revolutionising insights into advanced manufacturing sciences, our work provides an enabling approach for identifying and leveraging fragmental data sourced from sensing networks. This empowers the exploration of manufacturing metadata, promising transformative implications for the field.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104148"},"PeriodicalIF":8.2,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000769/pdfft?md5=2092be22979e23e80e58e1b153817dbf&pid=1-s2.0-S0166361524000769-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142087580","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":"ProIDS: A Segmentation and Segregation-based Process-level Intrusion Detection System for Securing Critical Infrastructures","authors":"Vikas Maurya ,&nbsp;Sandeep Kumar Shukla","doi":"10.1016/j.compind.2024.104147","DOIUrl":"10.1016/j.compind.2024.104147","url":null,"abstract":"<div><p>Critical infrastructures (CIs) are highly susceptible to cyber threats due to their crucial role in the nation and society. Intrusion Detection Systems (IDS) are deployed at the process level to enhance CI security. These process-level IDSs are broadly categorized into univariate and multivariate systems. Our research underscores that both types of systems encounter limitations, especially in handling correlations among process variables (PVs). Univariate IDSs neglect correlations by assessing PVs in isolation, while multivariate IDSs capture these but are vulnerable to evasion attacks. In response, we introduce ProIDS- a novel segmentation and segregation-based process-level IDS. ProIDS leverages the inherent correlations among PVs while segregating them into distinct units to enhance security against evolving threats. This strategic approach ensures the capture of correlations and mitigates the risk of evasion attacks, enhancing the system’s ability to detect abnormal activities. Additionally, ProIDS offers non-parametric modeling for heightened performance, minimal computational overhead, and noise reduction properties. Our comprehensive experiments demonstrate ProIDS’s superiority over baseline methods, delivering precise detection of various attacks while maintaining operational efficiency.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"163 ","pages":"Article 104147"},"PeriodicalIF":8.2,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142044635","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":"Intelligent cotter pins defect detection for electrified railway based on improved faster R-CNN and dilated convolution","authors":"Xin Wu ,&nbsp;Jiaxu Duan ,&nbsp;Lingyun Yang ,&nbsp;Shuhua Duan","doi":"10.1016/j.compind.2024.104146","DOIUrl":"10.1016/j.compind.2024.104146","url":null,"abstract":"<div><p>The cotter pin (CP) is a vital fastener for the catenary support components (CSCs) of high-speed electrified railways. Due to the vibration and excitation caused by the passing of railway vehicles, some CPs may be broken or fallen off over time, which poses a significant safety hazard to the railway systems. Currently, the CP defect detection is primarily conducted by humans, which is inefficient and inconsistent. Therefore, there is an urgent need for automatic CP defect detection to ensure railway safety. However, this task is very challenging as it requires covering hundreds or thousands of miles in limited times when the railway stops running. To this end, we first design a traffic track intelligent imaging device to capture catenary images at various angles at high speed. Then, inspired by the success of deep learning-based object detection, we develop a CP detection model based on an improved Faster R-CNN with a multi-scale region proposal network (MS-RPN) and propose the positive sample adaptive loss function (PSALF) to enhance detection accuracy. Finally, we propose a module to recognize the CP defect based on dilated convolution. The experimental results show that our method can effectively detect the CP defect in the catenary image, achieving 99.05 % precision and 98.40 % recall rate on CP defect detection. Furthermore, CP detection method and CP defect detection are significantly faster than baseline method, with FPS improvements of 2.76 and 24.67, respectively, thus making it more suitable for real-time applications in railway systems.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104146"},"PeriodicalIF":8.2,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141991273","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":"Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions","authors":"Giovanna Culot ,&nbsp;Matteo Podrecca ,&nbsp;Guido Nassimbeni","doi":"10.1016/j.compind.2024.104132","DOIUrl":"10.1016/j.compind.2024.104132","url":null,"abstract":"<div><p>This article presents a systematic literature review (SLR) of empirical studies concerning Artificial Intelligence (AI) in the field of Supply Chain Management (SCM). Over the past decade, technologies belonging to AI have developed rapidly, reaching a sufficient level of maturity to catalyze transformative changes in business and society. Within the SCM community, there are high expectations about disruptive impacts on current practices. However, this is not the first instance where AI has sparked business excitement, often falling short of the hype. It is thus important to examine both opportunities and challenges emerging from its actual implementation. Our analysis clarifies the current technological approaches and application areas, while expounding research themes around four key categories: data and system requirements, technology deployment processes, (inter)organizational integration, and performance implications. We also present the contextual factors identified in the literature. This review lays a solid foundation for future research on AI in SCM. By exclusively considering empirical contributions, our analysis minimizes the current buzz and underscores relevant opportunities for future studies intersecting AI, organizations, and supply chains (SCs). Our effort is also meant to consolidate existing research insights for a managerial audience.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104132"},"PeriodicalIF":8.2,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000605/pdfft?md5=e4096e708d00b43ee184323d386559a6&pid=1-s2.0-S0166361524000605-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141954113","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":"A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer","authors":"De-Jun Cheng ,&nbsp;Shun Wang ,&nbsp;Han-Bing Zhang,&nbsp;Zhi-Ying Sun","doi":"10.1016/j.compind.2024.104138","DOIUrl":"10.1016/j.compind.2024.104138","url":null,"abstract":"<div><p>The radiographic inspection plays a crucial role in ensuring the casting quality for improving the service life under harsh environments. However, due to the low-contrast between the defects and the image background, the random spatial position distribution, random shapes and aspect ratios of the defects, the development of an accurate defect automatic detection system is still challenging. To address these issues, this paper proposes a novel framework for low-contrast and random multi-scale casting defect detection, which is referred to as adaptive global dynamic detection transformer (AGD-DETR). A novel defect-aware data augmentation method is first proposed to adaptively highlight the feature of the low-contrast defect boundary. A multi-attentional pyramid feature refinement (MPFR) module is then established to refine and fuse the multi-scale defect features of random sizes. Afterwards, a novel global dynamic receptive fusion-transformer (GDRF-Transformer) detection scheme is designed to perform the global perception and feature dynamic extraction of complex internal casting defects. It includes 4D-anchor query and cross-layer box update strategy, query rectification by prior information of defect aspect ratio, and global adaptive-feed forward network (GA-FFN). A dataset comprising turbine blade casting defect radiographic (TBCDR) images, is used to demonstrate the high efficiency of the proposed AGD-DETR. The obtained results show that the proposed method can accurately capture the spatial position distributions and complex defect shapes. Furthermore, it outperforms existing state-of-the-art defect detection methods.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"162 ","pages":"Article 104138"},"PeriodicalIF":8.2,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910595","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}