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A technical patent map construction method and system based on multi-dimensional technical feature extraction Comput. Ind. (IF 8.2) Pub Date : 2024-09-12 Chuanxiao Li, Wenqiang Li, Hai Xiang, Yida Hong
A patent map is widely used in the field of technical information mining, which can support tasks such as detecting patent vacuums and predicting technical trends. However, existing patent map construction methods have the limitations of insufficient intelligence and accuracy in mining patent technical features, which leads to failure to effectively complete the above tasks. To address these limitations
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Virtual warehousing through digitalized inventory and on-demand manufacturing: A case study Comput. Ind. (IF 8.2) Pub Date : 2024-09-11 Elham Sharifi, Atanu Chaudhuri, Saeed D. Farahani, Lasse G. Staal, Brian Vejrum Waehrens
Novel digital on-demand manufacturing technologies provide a significant opportunity to support development of virtual warehousing and in turn improve supply chain performance. However, the implementation of virtual warehouse comes with a set of challenges, especially where the objective is to virtually warehouse standard or legacy parts that have been developed and verified initially for conventional
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Remaining useful life prediction model of cross-domain rolling bearing via dynamic hybrid domain adaptation and attention contrastive learning Comput. Ind. (IF 8.2) Pub Date : 2024-09-10 Xingchi Lu, Xuejian Yao, Quansheng Jiang, Yehu Shen, Fengyu Xu, Qixin Zhu
Performance degradation and remaining useful life (RUL) prediction are of great significance in improving the reliability of mechanical equipment. Existing cross-domain RUL prediction methods usually reduce data distribution discrepancy by domain adaptation, to overcome domain shift under cross-domain conditions. However, the fine-grained information between cross-domain degradation features and the
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Learning 3D human–object interaction graphs from transferable context knowledge for construction monitoring Comput. Ind. (IF 8.2) Pub Date : 2024-09-10 Liuyue Xie, Shreyas Misra, Nischal Suresh, Justin Soza-Soto, Tomotake Furuhata, Kenji Shimada
We propose a novel framework for detecting 3D human–object interactions (HOI) in construction sites and a toolkit for generating construction-related human–object interaction graphs. Computer vision methods have been adopted for construction site safety surveillance in recent years. The current computer vision methods rely on videos and images, with which safety verification is performed on common-sense
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Operational process monitoring: An object-centric approach Comput. Ind. (IF 8.2) Pub Date : 2024-09-10 Gyunam Park, Wil M.P. van der Aalst
In business processes, an operational problem refers to a deviation and an inefficiency that prohibits an organization from reaching its goals, e.g., a delay in approving a purchase order in a Procure-To-Pay (P2P) process. Operational process monitoring aims to assess the occurrence of such operational problems by analyzing event data that record the execution of business processes. Once the problems
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Detecting coagulation time in cheese making by means of computer vision and machine learning techniques Comput. Ind. (IF 8.2) Pub Date : 2024-09-09 Andrea Loddo, Cecilia Di Ruberto, Giuliano Armano, Andrea Manconi
Cheese production, a globally cherished culinary tradition, faces challenges in ensuring consistent product quality and production efficiency. The critical phase of determining cutting time during curd formation significantly influences cheese quality and yield. Traditional methods often struggle to address variability in coagulation conditions, particularly in small-scale factories. In this paper
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Prior knowledge embedding convolutional autoencoder: A single-source domain generalized fault diagnosis framework under small samples Comput. Ind. (IF 8.2) Pub Date : 2024-09-07 Feiyu Lu, Qingbin Tong, Xuedong Jiang, Xin Du, Jianjun Xu, Jingyi Huo
The proposed transfer learning-based fault diagnosis models have achieved good results in multi-source domain generalization (MDG) tasks. However, research on single-source domain generalization (SDG) is relatively scarce, and the limited availability of small training samples is seldom considered. Therefore, to address the insufficient feature extraction capability and poor generalization performance
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Computers as co-creative assistants. A comparative study on the use of text-to-image AI models for computer aided conceptual design Comput. Ind. (IF 8.2) Pub Date : 2024-09-07 Jorge Alcaide-Marzal, Jose Antonio Diego-Mas
This preliminary research presents a comparative study between Text-to-Image AI models and Shape Grammars, one of the main generative approaches to Computer Aided Conceptual Design. The goal is to determine to which extent AI models can reproduce or complement the performance of grammar algorithms as creative support tools for shape exploration in conceptual product design. Workflows, advantages and
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Adaptive early initial degradation point detection and outlier correction for bearings Comput. Ind. (IF 8.2) Pub Date : 2024-09-07 Qichao Yang, Baoping Tang, Lei Deng, Zihao Li
This paper delves into the accurate detection of the early initial degradation point (IDP) in bearings, and proposes, for the first time, a comprehensive adaptive IDP detection framework for bearings under variable operating conditions, along with an outlier data repair strategy. First, this study introduces the adaptive early initial degradation point detection (AEIDPD) method, which incorporates
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Fusing multichannel autoencoders with dynamic global loss for self-supervised fault diagnosis Comput. Ind. (IF 8.2) Pub Date : 2024-09-07 Chuan Li, Manjun Xiong, Hongmeng Shen, Yun Bai, Shuai Yang, Zhiqiang Pu
Engineering fault diagnosis often needs to be implemented without prior knowledge of labels. Considering the randomness and drift of fault features, this paper proposes fusing multichannel autoencoders with dynamic global loss (FMA-DGL) to enhanc self-supervised fault diagnosis. Multiple autoencoders are employed to represent the fault features of multichannel vibration signals. A dynamic global loss
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Intelligent crude oil price probability forecasting: Deep learning models and industry applications Comput. Ind. (IF 8.2) Pub Date : 2024-09-04 Liang Shen, Yukun Bao, Najmul Hasan, Yanmei Huang, Xiaohong Zhou, Changrui Deng
The crude oil price has been subject to periodic fluctuations because of seasonal changes in industrial demand and supply, weather, natural disasters and global political unrest. An accurate forecast of crude oil prices is of utmost importance for decision makers and industry players in the energy sector. Despite this, the volatility of crude oil prices contributes to the uncertainty of the energy
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Detecting visual anomalies in an industrial environment: Unsupervised methods put to the test on the AutoVI dataset Comput. Ind. (IF 8.2) Pub Date : 2024-09-02 Philippe Carvalho, Meriem Lafou, Alexandre Durupt, Antoine Leblanc, Yves Grandvalet
The methods for unsupervised visual inspection use algorithms that are developed, trained and evaluated on publicly available datasets. However, these datasets do not reflect genuine industrial conditions, and thus current methods are not evaluated in real-world industrial production contexts. To answer this shortcoming, we introduce AutoVI, an industrial dataset of visual defects that can be encountered
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Digitally enhanced development of customised lubricant: Experimental and modelling studies of lubricant performance for hot stamping Comput. Ind. (IF 8.2) Pub Date : 2024-09-01 Xiao Yang, Heli Liu, Vincent Wu, Denis J. Politis, Haochen Yao, Jie Zhang, Liliang Wang
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
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A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks Comput. Ind. (IF 8.2) Pub Date : 2024-08-30 Zifeng Xu, Zhe Wang, Chaojia Gao, Keqi Zhang, Jie Lv, Jie Wang, Lilan Liu
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
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A novel data-driven framework for enhancing the consistency of deposition contours and mechanical properties in metal additive manufacturing Comput. Ind. (IF 8.2) Pub Date : 2024-08-29 Miao Yu, Lida Zhu, Zhichao Yang, Lu Xu, Jinsheng Ning, Baoquan Chang
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
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A three-directional stress-strain model-based physics-embedded prediction framework for metal tube full-bent cross-sectional characteristics Comput. Ind. (IF 8.2) Pub Date : 2024-08-28 Yongzhe Xiang, Zili Wang, Shuyou Zhang, Yaochen Lin, Jie Li, Jianrong Tan
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
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Examining the effect of locomotion techniques on virtual prototype assessment: Gaze analysis using a Head-Mounted Display Comput. Ind. (IF 8.2) Pub Date : 2024-08-28 Julia Galán Serrano, Francisco Felip-Miralles, Almudena Palacios-Ibáñez
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
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Unlocking inherent values of manufacturing metadata through digital characteristics (DC) alignment Comput. Ind. (IF 8.2) Pub Date : 2024-08-28 Heli Liu, Xiao Yang, Maxim Weill, Shengzhe Li, Vincent Wu, Denis J. Politis, Huifeng Shi, Zhichao Zhang, Liliang Wang
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
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ProIDS: A Segmentation and Segregation-based Process-level Intrusion Detection System for Securing Critical Infrastructures Comput. Ind. (IF 8.2) Pub Date : 2024-08-21 Vikas Maurya, Sandeep Kumar Shukla
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
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Intelligent cotter pins defect detection for electrified railway based on improved faster R-CNN and dilated convolution Comput. Ind. (IF 8.2) Pub Date : 2024-08-15 Xin Wu, Jiaxu Duan, Lingyun Yang, Shuhua Duan
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
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Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions Comput. Ind. (IF 8.2) Pub Date : 2024-08-12 Giovanna Culot, Matteo Podrecca, Guido Nassimbeni
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
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A novel framework for low-contrast and random multi-scale blade casting defect detection by an adaptive global dynamic detection transformer Comput. Ind. (IF 8.2) Pub Date : 2024-08-06 De-Jun Cheng, Shun Wang, Han-Bing Zhang, Zhi-Ying Sun
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
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A method for the automated digitalization of fluid circuit diagrams Comput. Ind. (IF 8.2) Pub Date : 2024-08-05 Valentin Stegmaier, Nasser Jazdi, Michael Weyrich
The benefits of Digital Twins are widely recognized across various use cases. However, to ensure efficient utilization of Digital Twins, it is crucial to minimize the effort required for their creation. This is particularly relevant for behavior models, which play a significant role in many Digital Twin use cases. While there are existing approaches for creating these models efficiently, they rely
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Quality prediction for magnetic pulse crimping cable joints based on 3D vision and ensemble learning Comput. Ind. (IF 8.2) Pub Date : 2024-08-02 Ming Lai, Shaoluo Wang, Hao Jiang, Junjia Cui, Guangyao Li
Magnetic pulse crimping (MPC) addresses the limitations of conventional hydraulic crimping in cable joint applications. However, the lack of dependable detection methods presents a significant challenge in MPC manufacturing. This study proposed a novel approach integrating 3D vision and ensemble learning to achieve a non-destructive quality assessment of MPC joints. By analyzing the geometric characteristics
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Assessment of a large language model based digital intelligent assistant in assembly manufacturing Comput. Ind. (IF 8.2) Pub Date : 2024-07-31 Silvia Colabianchi, Francesco Costantino, Nicolò Sabetta
The use of Digital Intelligent Assistants (DIAs) in manufacturing aims to enhance performance and reduce cognitive workload. By leveraging the advanced capabilities of Large Language Models (LLMs), the research aims to understand the impact of DIAs on assembly processes, emphasizing human-centric design and operational efficiency. The study is novel in considering the three primary objectives: evaluating
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A novel dimensional variational prototypical network for industrial few-shot fault diagnosis with unseen faults Comput. Ind. (IF 8.2) Pub Date : 2024-07-30 Chuang Peng, Lei Chen, Kuangrong Hao, Shuaijie Chen, Xin Cai, Bing Wei
A Dimensional Variational Prototypical Network (DVPN) is proposed to learn transferable knowledge from a largescale dataset containing sufficient samples of diverse faults, enabling few-shot diagnosis on new faults that are unseen in the dataset. The network includes a multiscale feature fusion module with shared weights to extract fault features, followed by a dimensional variational prototypical
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Performance-driven closed-loop optimization and control for smart manufacturing processes in the cloud-edge-device collaborative architecture: A review and new perspectives Comput. Ind. (IF 8.2) Pub Date : 2024-07-29 Chi Zhang, Yilin Wang, Ziyan Zhao, Xiaolu Chen, Hao Ye, Shixin Liu, Ying Yang, Kaixiang Peng
With the transformation and upgrading of the manufacturing industry, manufacturing systems have become increasingly complex in terms of the structural functionality, process flows, control systems, and performance assessment criteria. Digital representation, performance-related process monitoring, process regulation and control, and comprehensive performance optimization have been viewed as the core
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Digital Twin Stakeholder Communication: Characteristics, Challenges, and Best Practices Comput. Ind. (IF 8.2) Pub Date : 2024-07-29 Christian Kober, Francisco Gomez Medina, Martin Benfer, Jens Peter Wulfsberg, Veronica Martinez, Gisela Lanza
Digital Twins (DT) encompass virtual models interconnected with a physical system through data links. Although DTs hold significant potential for positive organisational impact, their successful adoption in industrial practice remains limited. Whereas existing research predominantly focuses on technical challenges, more recent studies underscore the importance of addressing organisational and human
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An offset-transformer hierarchical model for point cloud-based resistance spot welding quality classification Comput. Ind. (IF 8.2) Pub Date : 2024-07-26 Bo Yang, Qing Peng, Zhengping Zhang, Yucheng Zhang, Yufeng Li, Zerui Xi
Resistance spot welding (RSW) is a widely used welding technology in automotive manufacturing, and weld nugget quality is closely related to the quality of the vehicle body. Offline random checks are largely relied on the quality inspection of weld nuggets, but they have low efficiency and high cost. To address this issue, this paper proposes a deep learning model for RSW weld nugget classification
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DMWMNet: A novel dual-branch multi-level convolutional network for high-performance mixed-type wafer map defect detection in semiconductor manufacturing Comput. Ind. (IF 8.2) Pub Date : 2024-07-25 Xiangyan Zhang, Zhong Jiang, Hong Yang, Yadong Mo, Linkun Zhou, Ying Zhang, Jian Li, Shimin Wei
Wafer map defect detection plays an important role in semiconductor manufacturing by identifying root causes and accelerating process adjustments to ensure product quality and reduce unnecessary expenditures. However, existing methods have some limitations, such as low accuracy in mixed-type defect detection and poor recognition of similar defects and weak features. In this article, a novel dual-branch
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Addressing challenges in industrial pick and place: A deep learning-based 6 Degrees-of-Freedom pose estimation solution Comput. Ind. (IF 8.2) Pub Date : 2024-07-25 Elena Govi, Davide Sapienza, Samuele Toscani, Ivan Cotti, Giorgia Franchini, Marko Bertogna
Object picking is a fundamental, long-lasting, and yet unsolved problem in industrial applications. To complete it, 6 Degrees-of-Freedom pose estimation can be crucial. This task, easy for humans, is a challenge for machines as it involves multiple intelligent processes (for example object detection, recognition, pose prediction). Pose estimation has recently made huge steps forward, due to the advent
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Impact of generative artificial intelligence models on the performance of citizen data scientists in retail firms Comput. Ind. (IF 8.2) Pub Date : 2024-07-21 Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Keng Boon Ooi, Garry Wei Han Tan, Hing Kai Chan
Generative Artificial Intelligence (AI) models serve as powerful tools for organizations aiming to integrate advanced data analysis and automation into their applications and services. Citizen data scientists—individuals without formal training but skilled in data analysis—combine domain expertise with analytical skills, making them invaluable assets in the retail sector. Generative AI models can further
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Process mining beyond workflows Comput. Ind. (IF 8.2) Pub Date : 2024-07-18 Wil M.P. van der Aalst, Hajo A. Reijers, Laura Maruster
After two decades of research and development, process mining techniques are now recognized as essential analysis tools, as they have their own Gartner Magic Quadrant. The development of process mining techniques is rooted in process-related research fields such as Business Process Management and fueled by increasing data availability. To cope with the complexity of business processes, the focus of
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An ontology-based method for knowledge reuse in the design for maintenance of complex products Comput. Ind. (IF 8.2) Pub Date : 2024-07-18 Ziyue Guo, Dong Zhou, Dequan Yu, Qidi Zhou, Hongduo Wu, Aimin Hao
In the context of the Fourth Industrial Revolution, a large amount of heterogeneous data and information is generated during the lifecycle of complex products, which poses a considerable challenge for manufacturers and effective knowledge integration. It has been challenging for traditional experience-based design methods to meet the diverse needs of customers and maintain competitiveness in fierce
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Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network Comput. Ind. (IF 8.2) Pub Date : 2024-07-17 Xiaoyuan Liu, Jinhai Liu, Huanqun Zhang, Huaguang Zhang
Accurate X-ray image defect segmentation is of paramount importance in industrial contexts, as it is the foundation for product quality control and production safety. Deep learning (DL) has demonstrated powerful image scene understanding capabilities and has achieved unprecedented performance in defect segmentation tasks. However, existing DL methods suffer from significant performance degradation
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An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention Comput. Ind. (IF 8.2) Pub Date : 2024-07-16 Juxian Zhao, Wei Li, Jinsong Zhu, Zhigang Gao, Lu Pan, Zhongguan Liu
Efficient firefighting operations are crucial for ensuring the safety of firefighters and preventing direct exposure to high-temperature and high-radiation environments. However, traditional firefighting robots face the challenges of low efficiency, high misjudgment rates, and difficulty in control during firefighting processes, particularly in extremely complex and dynamically changing fire scenes
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A fair and scalable watermarking scheme for the digital content trading industry Comput. Ind. (IF 8.2) Pub Date : 2024-07-12 Xiangli Xiao, Moting Su, Jiajia Jiang, Yushu Zhang, Zhongyun Hua, Zhihua Xia
The booming Internet economy and generative artificial intelligence have driven the rapid growth of the digital content trading industry, creating an urgent need for the fair protection of the rights of both buyers and sellers. To meet this need, a technique known as buyer–seller watermarking has emerged. Despite its existence, the majority of existing buyer–seller watermarking schemes adopt the owner-side
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CFD-ML: Stream-based active learning of computational fluid dynamics simulations for efficient product design Comput. Ind. (IF 8.2) Pub Date : 2024-07-05 Youngjae Bae, Kyunghye Nam, Seokho Kang
Computational fluid dynamics (CFD) has been extensively used as a simulation tool for product development in various industrial fields. Engineers sequentially query the CFD simulator to evaluate their design instances, during which they improve the new designs based on previous evaluations. The high cost of performing CFD simulations for numerous design instances is a practical challenge. To reduce
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On implementing autonomous supply chains: A multi-agent system approach Comput. Ind. (IF 8.2) Pub Date : 2024-06-20 Liming Xu, Stephen Mak, Maria Minaricova, Alexandra Brintrup
Trade restrictions, the COVID-19 pandemic, and geopolitical conflicts have significantly exposed vulnerabilities within traditional global supply chains. These events underscore the need for organisations to establish more resilient and flexible supply chains. To address these challenges, the concept of the autonomous supply chain (ASC), characterised by predictive and self-decision-making capabilities
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Mapping the hot stamping process through developing distinctive digital characteristics Comput. Ind. (IF 8.2) Pub Date : 2024-06-18 Heli Liu, Xiaochuan Liu, Xiao Yang, Denis J. Politis, Yang Zheng, Saksham Dhawan, Huifeng Shi, Liliang Wang
Structural components produced through hot stamping of lightweight materials, such as aluminium alloys, play a pivotal role in mass reduction, leading to decreased CO emissions and enhanced fuel efficiency, especially in applications such as electric vehicles, high-speed trains, and aircraft. Concurrently, the hot stamping process is experiencing an exponential increase in data generation, stemming
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A Digital Twin use cases classification and definition framework based on Industrial feedback Comput. Ind. (IF 8.2) Pub Date : 2024-06-10 Emmanuelle Abisset-Chavanne, Thierry Coupaye, Fahad R. Golra, Damien Lamy, Ariane Piel, Olivier Scart, Pascale Vicat-Blanc
The Digital Twin paradigm is a very promising technology that can be applied to various fields and applications. However, it lacks a unifying framework for classifying and defining use cases. The goal of this paper is to address the identified gap. Using a field study and a bottom-up approach, it aims to categorize the various uses of the industrial Digital Twin to help formalize the concept and rationalize
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Enabling Building Information Model-driven human-robot collaborative construction workflows with closed-loop digital twins Comput. Ind. (IF 8.2) Pub Date : 2024-06-07 Xi Wang, Hongrui Yu, Wes McGee, Carol C. Menassa, Vineet R. Kamat
The introduction of assistive construction robots can significantly alleviate physical demands on construction workers while enhancing both the productivity and safety of construction projects. Leveraging a Building Information Model (BIM) offers a natural and promising approach to However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built
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Knowledge-Enhanced Spatiotemporal Analysis for Anomaly Detection in Process Manufacturing Comput. Ind. (IF 8.2) Pub Date : 2024-05-31 Louis Allen, Haiping Lu, Joan Cordiner
Effective fault detection and diagnosis (FDD) is crucial for proactively identifying irregular states that could jeopardize operator well-being and process integrity. In the era of Industry 4.0, data-driven FDD techniques have received particular attention, driven by the proliferation of stored manufacturing sensor data. While these methods have proven adept at categorizing established process fault
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Causal knowledge extraction from long text maintenance documents Comput. Ind. (IF 8.2) Pub Date : 2024-05-31 Brad Hershowitz, Melinda Hodkiewicz, Tyler Bikaun, Michael Stewart, Wei Liu
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LAD-Net: A lightweight welding defect surface non-destructive detection algorithm based on the attention mechanism Comput. Ind. (IF 8.2) Pub Date : 2024-05-31 Feng Liang, Lun Zhao, Yu Ren, Sen Wang, Sandy To, Zeshan Abbas, Md Shafiqul Islam
Ultrasound welding technology is widely applied in the field of industrial manufacturing. In complex working conditions, various factors such as welding parameters, equipment conditions and operational techniques contribute to the formation of diverse and unpredictable line defects during the welding process. These defects exhibit characteristics such as varied shapes, random positions, and diverse
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A complex network-based approach for resilient and flexible design resource allocation in industry 5.0 Comput. Ind. (IF 8.2) Pub Date : 2024-05-31 Nanfeng Ma, Xifan Yao, Kesai Wang
The development of Industry 5.0 focuses on customization, personalization in production, and the innovative thinking of employees, elevating the value of human contribution. Design, being an innovation-driven domain, demands greater flexibility in resource allocation. Consequently, rapidly and effectively allocating cloud service resources for personalized design tasks becomes crucial. With the emergence
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Supporting business process variability through declarative process families Comput. Ind. (IF 8.2) Pub Date : 2024-05-28 H. Groefsema, N.R.T.P. van Beest
Organizations use business process management systems to automate processes that they use to perform tasks or interact with customers. However, several variants of the same business process may exist due to, e.g., mergers, customer-tailored services, diverse market segments, or distinct legislation across borders. As a result, reliable support for process variability has been identified as a necessity
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Analysis and classification of employee attrition and absenteeism in industry: A sequential pattern mining-based methodology Comput. Ind. (IF 8.2) Pub Date : 2024-05-27 M. Saqib Nawaz, M. Zohaib Nawaz, Philippe Fournier-Viger, José María Luna
Employee attrition and absenteeism are major problems that affect many industries and organizations, resulting in diminished productivity, elevated costs, and losses. These phenomena can be attributed to multiple factors that are difficult to anticipate for human resources or management. Therefore, this paper proposes a content-based methodology for the analysis and classification of employee attrition
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Real-time detection of surface cracking defects for large-sized stamped parts Comput. Ind. (IF 8.2) Pub Date : 2024-05-10 Xingjun Dong, Changsheng Zhang, Junhao Wang, Yao Chen, Dawei Wang
This study presents a framework for the real-time detection of surface cracking in large-sized stamped metal parts. The framework aims to address the challenges of low detection efficiency and high error rates associated with manual cracking detection. Within this framework, a novel network, SNF-YOLOv8, is proposed to efficiently detect cracking while ensuring that the detection speed matches the production
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Designing production planning and control in smart manufacturing Comput. Ind. (IF 8.2) Pub Date : 2024-05-08 Arno Kasper, Martin Land, Will Bertrand, Jacob Wijngaard
To make manufacturing technology productive, manufacturers rely on a production planning and control (PPC) framework that plans ahead and monitors ongoing transformation processes. The design of an appropriate framework has far-reaching implications for the manufacturing organization as a whole. Yet, to date, there has been no unified guidance on key PPC design issues. This is strongly needed, as it
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Construction of design requirements knowledgebase from unstructured design guidelines using natural language processing Comput. Ind. (IF 8.2) Pub Date : 2024-05-07 Baekgyu Kwon, Junho Kim, Hyunoh Lee, Hyo-Won Suh, Duhwan Mun
In the manufacturing industry, unstructured documents such as design guidelines, regulatory documents, and failure cases are essential for product development. However, due to the large volume and frequent revisions of these documents, designers often find it difficult to keep up to date with the latest content. This study presents a method for analyzing the characteristics of unstructured design guidelines
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A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples Comput. Ind. (IF 8.2) Pub Date : 2024-05-01 Zhenya Wang, Qiusheng Luo, Hui Chen, Jingshan Zhao, Ligang Yao, Jun Zhang, Fulei Chu
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A self-supervised leak detection method for natural gas gathering pipelines considering unlabeled multi-class non-leak data Comput. Ind. (IF 8.2) Pub Date : 2024-04-30 Zhonglin Zuo, Hao Zhang, Zheng Li, Li Ma, Shan Liang, Tong Liu, Mehmet Mercangöz
Detecting leaks in natural gas gathering pipelines is paramount for the safe and reliable operation of the gas and oil industry. Due to the lack of leak data and the changes in leak features, semi-supervised leak detection methods that use normal data for health model learning have attracted much attention. However, these approaches usually consider one-class normal samples as health data, which may
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Knowledge-based digital twin system: Using a knowlege-driven approach for manufacturing process modeling Comput. Ind. (IF 8.2) Pub Date : 2024-04-30 Chang Su, Yong Han, Xin Tang, Qi Jiang, Tao Wang, Qingchen He
The Knowledge-Based Digital Twin System is a digital twin system developed on the foundation of a knowledge graph, aimed at serving the complex manufacturing process. This system embraces a knowledge-driven modeling approach, aspiring to construct a digital twin model for the manufacturing process, thereby enabling precise description, management, prediction, and optimization of the process. The core
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Estimating and explaining regional land value distribution using attention-enhanced deep generative models Comput. Ind. (IF 8.2) Pub Date : 2024-04-27 Feifeng Jiang, Jun Ma, Christopher John Webster, Weiwei Chen, Wei Wang
Accurate land valuation is crucial in sustainable urban development, influencing pivotal decisions on resource allocation and land-use strategies. Most existing studies, primarily using point-based modeling approaches, face challenges on granularity, generalizability, and spatial effect capturing, limiting their effectiveness in regional land valuation with high granularity. This study therefore proposes
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EA-GAT: Event aware graph attention network on cyber-physical systems Comput. Ind. (IF 8.2) Pub Date : 2024-04-26 Mehmet Yavuz Yağci, Muhammed Ali Aydin
Anomaly detection with high accuracy, recall, and low error rate is critical for the safe and uninterrupted operation of cyber-physical systems. However, detecting anomalies in multimodal time series with different modalities obtained from cyber-physical systems is challenging. Although deep learning methods show very good results in anomaly detection, they fail to detect anomalies according to the
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A blockchain-based deployment framework for protecting building design intellectual property rights in collaborative digital environments Comput. Ind. (IF 8.2) Pub Date : 2024-04-20 Weisheng Lu, Liupengfei Wu
Protecting intellectual property rights (IPR) in the architecture, engineering, and construction (AEC) industry is a long-standing challenge. In the collaborative digital environments, where multiple professionals use digital platforms such as building information modelling to collaborate on a building design, this challenge has intensified. This research harnesses the functions of blockchain technology
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E-fulfillment cost management in omnichannel retailing: An exploratory study Comput. Ind. (IF 8.2) Pub Date : 2024-04-05 Miguel Rodríguez-García, Iria González-Romero, Ángel Ortiz-Bas, José Carlos Prado-Prado
The purpose of this study is twofold: investigating how omnichannel (OC) retailers manage e-fulfillment costs and establishing how these costs relate to the evolution of OC retailers' e-fulfillment strategies. Experts in e-fulfillment from 34 European OC retailers across various sectors participated in an exploratory survey. The study's results reveal that although e-fulfillment costs significantly
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Assessing user performance in augmented reality assembly guidance for industry 4.0 operators Comput. Ind. (IF 8.2) Pub Date : 2024-03-28 Emanuele Marino, Loris Barbieri, Fabio Bruno, Maurizio Muzzupappa
In the realm of smart manufacturing, Augmented Reality (AR) technology has gained increasing attention among researchers and manufacturers due to its practicality and adaptability. For this reason, it has been widely embraced in various industrial fields, especially for helping operators assemble products. Despite its widespread adoption, there is a debate in the research community about how effective
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A data-driven approach toward a machine- and system-level performance monitoring digital twin for production lines Comput. Ind. (IF 8.2) Pub Date : 2024-03-26 Yaqing Xu, Yassine Qamsane, Saumuy Puchala, Annette Januszczak, Dawn M. Tilbury, Kira Barton
Efficient performance monitoring in production systems holds paramount importance as it enables organizations to optimize their manufacturing processes, enhance productivity, and maintain a competitive edge in the market. Typically, machine and system level performance monitoring systems are investigated independently, whereas an integrated approach that considers both levels can offer valuable insights