Computers in Industry最新文献

{"title":"Process mining beyond workflows","authors":"Wil M.P. van der Aalst ,&nbsp;Hajo A. Reijers ,&nbsp;Laura Maruster","doi":"10.1016/j.compind.2024.104126","DOIUrl":"10.1016/j.compind.2024.104126","url":null,"abstract":"<div><p>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 process mining techniques needs to go beyond workflow-like processes, that represent the life-cycle of a single case and enable multiple object types and events. This can only be accomplished by capitalizing on essential concepts from production and logistics domains, such as Bills-of-Materials (BOMs), and Customer Order Decoupling Points (CODPs). Pioneer researchers, e.g. Hans Wortmann contributed to the development of Enterprise Resource Planning, enterprise modeling, product models, and lean manufacturing. Experiences from these fields help to lift the process mining domain from case-based (i.e. workflow mining) to object-centered process mining. These contributions could be realized by conducting insightful case studies at company sites, one of them being discussed in this paper. The evaluation of process mining techniques is elaborated by proposing an “evaluation ladder”, and its application is shown in the case study under consideration.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104126"},"PeriodicalIF":8.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S016636152400054X/pdfft?md5=1a7c8c680d22d908a890dbdb32198922&pid=1-s2.0-S016636152400054X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An ontology-based method for knowledge reuse in the design for maintenance of complex products","authors":"Ziyue Guo ,&nbsp;Dong Zhou ,&nbsp;Dequan Yu ,&nbsp;Qidi Zhou ,&nbsp;Hongduo Wu ,&nbsp;Aimin Hao","doi":"10.1016/j.compind.2024.104124","DOIUrl":"10.1016/j.compind.2024.104124","url":null,"abstract":"<div><p>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 global markets. Capturing, formalizing and reusing multidisciplinary knowledge that is scattered among different departments and stages to help make effective decisions has been a crucial way for digital enterprises to improve manufacturing efficiency. Design for maintenance is typical work requiring cross-domain knowledge and involving stakeholder collaboration. This paper presents a structured domain-specific ontology and its development method, namely, the Maintainability Design Ontology for Complex prOducts (MDOCO), to formalize heterogeneous knowledge and improve semantic interoperability in the maintainability design area. The MDOCO has a rigorous semantic structure and complies with well-designed top-level and middle ontologies such as the Basic Formal Ontology and the Industrial Ontology Foundry (IOF) Core Ontology to ensure semantic interoperability. A set of reasoning rules is carefully designed to enable the MDOCO to perform knowledge reasoning. In a practical case, the effectiveness of the MDOCO is validated at both the semantic and application levels. The MDOCO combines recent methodology and best practices, enabling the well-structured modeling of heterogeneous knowledge and good semantic interoperability.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104124"},"PeriodicalIF":8.2,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141729127","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":"Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network","authors":"Xiaoyuan Liu ,&nbsp;Jinhai Liu ,&nbsp;Huanqun Zhang ,&nbsp;Huaguang Zhang","doi":"10.1016/j.compind.2024.104123","DOIUrl":"10.1016/j.compind.2024.104123","url":null,"abstract":"<div><p>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 when facing low-contrast X-ray images, as the core information of defects is often obscured and the profile details are ambiguous. To address this issue, this paper explicitly decomposes the X-ray defect segmentation task into two subtasks: core feature learning and elasticity profile refinement, allowing task “serial” decomposition and performance “parallel” improvement. On this basis, a novel core-profile decomposition network (CPDNet) is developed to achieve accurate defect segmentation of X-ray images. Specifically, the core feature learning module is designed to construct the effective feature space from two views, discriminative and structural, to extract defect-related core features from X-ray images. Subsequently, the elasticity profile refinement module is developed to further improve the defect segmentation performance, which makes the first attempt to define the profile refinement as an out-of-distribution detection and leverage the elasticity score to refine the profile details at the pixel level. To fully evaluate the presented method, we conduct a series of experiments using two real-world X-ray defect datasets, and the results demonstrate that the CPDNet outperforms state-of-the-art methods.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104123"},"PeriodicalIF":8.2,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638934","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":"An efficient firefighting method for robotics: A novel convolution-based lightweight network model guided by contextual features with dual attention","authors":"Juxian Zhao ,&nbsp;Wei Li ,&nbsp;Jinsong Zhu ,&nbsp;Zhigang Gao ,&nbsp;Lu Pan ,&nbsp;Zhongguan Liu","doi":"10.1016/j.compind.2024.104127","DOIUrl":"10.1016/j.compind.2024.104127","url":null,"abstract":"<div><p>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. Therefore, this article proposes a novel convolution-based context-guided dual attention lightweight network (CG-DALNet) model to develop efficient firefighting methods for firefighting robots. To expand the field of fire perception, this study employs monocular vision from drones to assist ground firefighting robots in autonomous firefighting decision-making in an end-to-end manner. By introducing depthwise separable convolutions to construct the feature backbone layer, the number of the parameters in the model is reduced. To better understand target position information in fire scenes, we propose a position attention module guided by contextual features to enhance the model's positional awareness. Additionally, to efficiently integrate feature information at different scales in the fire scene, we adopt a residual-connected convolutional kernel attention module to enhance the model's ability to express complex fire scene features. Numerical experiments show that the proposed CG-DALNet lightweight network model achieves significant performance improvement in autonomous firefighting tasks for robots. This research provides an innovative solution for autonomous firefighting methods for firefighting robots and demonstrates its effectiveness and potential.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104127"},"PeriodicalIF":8.2,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629941","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 fair and scalable watermarking scheme for the digital content trading industry","authors":"Xiangli Xiao ,&nbsp;Moting Su ,&nbsp;Jiajia Jiang ,&nbsp;Yushu Zhang ,&nbsp;Zhongyun Hua ,&nbsp;Zhihua Xia","doi":"10.1016/j.compind.2024.104125","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104125","url":null,"abstract":"<div><p>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 embedding mode, which results in poor scalability. While a handful of schemes adopt the client-side embedding mode to enhance scalability, they either require the deep involvement of a trusted third party or fall short of ensuring complete fairness due to the unresolved unbinding problem. To address these challenges, this paper proposes a fair and scalable watermarking scheme for digital content transactions based on proxy re-encryption and digital signatures. For one thing, this scheme solves the unbinding problem and ensures complete fair protection of the rights of both buyers and sellers. For another, it adopts the client-side embedding mode and has good scalability. Additionally, it eliminates the need for a trusted third party. Finally, theoretical analysis and experiments demonstrate that the proposed scheme achieves the intended design goals and possesses superior efficiency advantages.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104125"},"PeriodicalIF":8.2,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141605679","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":"CFD-ML: Stream-based active learning of computational fluid dynamics simulations for efficient product design","authors":"Youngjae Bae ,&nbsp;Kyunghye Nam ,&nbsp;Seokho Kang","doi":"10.1016/j.compind.2024.104122","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104122","url":null,"abstract":"<div><p>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 this cost, machine learning (ML) approaches have been employed to approximate CFD simulations. Although ML enables the fast approximation of CFD, it can suffer from low accuracy when making predictions for design instances that significantly deviate from the training dataset. In this study, we propose a CFD-ML combined system based on stream-based active learning to utilize the CFD simulator cost-efficiently. The proposed method has two main objectives: reducing the number of CFD simulations and ensuring high accuracy of the ML approximations. When a design instance is queried, the CFD-ML system interchangeably uses the CFD simulator and the ML model depending on the predictive uncertainty of the ML model. If the uncertainty of the ML model is high, the CFD simulator is used to obtain an evaluation result, which is subsequently used to enhance the ML model. Conversely, if the uncertainty is low, the ML model is used to obtain an approximated evaluation result. The CFD-ML system reduces computational costs compared to exclusive reliance on the CFD simulator and yields more accurate evaluations compared to exclusive reliance on the ML model. We demonstrated the effectiveness of the proposed method through a case study on a centrifugal fan development task.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104122"},"PeriodicalIF":8.2,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141543600","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":"On implementing autonomous supply chains: A multi-agent system approach","authors":"Liming Xu ,&nbsp;Stephen Mak ,&nbsp;Maria Minaricova ,&nbsp;Alexandra Brintrup","doi":"10.1016/j.compind.2024.104120","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104120","url":null,"abstract":"<div><p>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, has recently emerged as a promising solution. However, research on ASCs is relatively limited, with no existing studies specifically focusing on their implementations. This paper aims to address this gap by presenting an implementation of ASC using a multi-agent approach. It presents a methodology for the analysis and design of such an agent-based ASC system (A2SC). This paper provides a concrete case study, the autonomous meat supply chain, which showcases the practical implementation of the A2SC system using the proposed methodology. Additionally, a system architecture and a toolkit for developing such A2SC systems are presented. Despite limitations, this work demonstrates a promising approach for implementing an effective ASC system.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104120"},"PeriodicalIF":8.2,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000484/pdfft?md5=9ca61e741ba16e7c32cd07e405dbefad&pid=1-s2.0-S0166361524000484-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141434786","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":"Mapping the hot stamping process through developing distinctive digital characteristics","authors":"Heli Liu ,&nbsp;Xiaochuan Liu ,&nbsp;Xiao Yang ,&nbsp;Denis J. Politis ,&nbsp;Yang Zheng ,&nbsp;Saksham Dhawan ,&nbsp;Huifeng Shi ,&nbsp;Liliang Wang","doi":"10.1016/j.compind.2024.104121","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104121","url":null,"abstract":"<div><p>Structural components produced through hot stamping of lightweight materials, such as aluminium alloys, play a pivotal role in mass reduction, leading to decreased CO₂ 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 from ongoing production, research, and development activities. Yet, translating the inherent values of these voluminous metadata into scientific innovations and industrial breakthroughs requires the emerging expertise by consolidating the knowledge of hot stamping and data science. Here, the authors have conceptualised and developed the digital characteristics (DC) for manufacturing processes. The DC serves as the ‘DNA’ of every manufacturing process by encompassing its inherent and distinctive natures spanning over the design, manufacturing and application phases of the manufactured products. Focusing on the hot stamping process, the authors have developed the unique DC from voluminous hot stamping data derived from experimentally validated simulations and sensing networks. Results demonstrate that the DC revealed the distinct evolutionary thermo-mechanical characteristics of the hot stamping process in terms of representative geometric features, which facilitates the fundamental scientific understanding and unlocks the potential on implementing data-centric scientific innovations in advanced manufacturing paradigms.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104121"},"PeriodicalIF":10.0,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000496/pdfft?md5=3d8a199a38915a8fe5e2bd8da79ab656&pid=1-s2.0-S0166361524000496-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141423836","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 use cases classification and definition framework based on Industrial feedback","authors":"Emmanuelle Abisset-Chavanne ,&nbsp;Thierry Coupaye ,&nbsp;Fahad R. Golra ,&nbsp;Damien Lamy ,&nbsp;Ariane Piel ,&nbsp;Olivier Scart ,&nbsp;Pascale Vicat-Blanc","doi":"10.1016/j.compind.2024.104113","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104113","url":null,"abstract":"<div><p>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 its adoption by a range of industrial sectors. The study is based on an iterative process of collecting use cases from a wide variety of verticals, applying grounded theory principles. The usage scenarios were extracted, synthesized, grouped and abstracted to develop an actionable use cases classification framework. This article presents the resulting taxonomy and illustrates it by detailing real industrial use cases, including their value proposition and application areas. This collection, classification and analysis of use cases led to a study of the common aspects proposed in academic and industrial definitions of the Digital Twin. The goal was to combine and generalize these aspects into a pragmatic and unifying definition, on which the Alliance for Industry of the Future (AIF) committee has converged. The main contributions of this work include proposing, from a joint industrial and academic perspective, (i) the first domain-independent and industry-focused systematic collection of Digital Twin use cases, (ii) a comprehensive framework for analyzing and classifying Digital Twin use cases and their requirements, and (iii) a consensual general definition of the industrial Digital Twin to contribute to the structuring and standardization of this very active ecosystem.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104113"},"PeriodicalIF":10.0,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0166361524000411/pdfft?md5=bee35046743252dba563bca2280bdcfc&pid=1-s2.0-S0166361524000411-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141303064","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":"Enabling Building Information Model-driven human-robot collaborative construction workflows with closed-loop digital twins","authors":"Xi Wang ,&nbsp;Hongrui Yu ,&nbsp;Wes McGee ,&nbsp;Carol C. Menassa ,&nbsp;Vineet R. Kamat","doi":"10.1016/j.compind.2024.104112","DOIUrl":"https://doi.org/10.1016/j.compind.2024.104112","url":null,"abstract":"<div><p>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 <strong>driving robotic construction workflows.</strong> However, because of uncertainties inherent in construction sites, such as discrepancies between the as-designed and as-built components, robots cannot solely rely on a BIM to plan and perform field construction work. Human workers are adept at improvising alternative plans with their creativity and experience and thus can assist robots in overcoming uncertainties and performing construction work successfully. In such scenarios, it is critical to continuously update the BIM as work processes unfold so that it includes as-built information for the ensuing construction and maintenance tasks. This research introduces an interactive closed-loop digital twin framework that integrates a BIM into human-robot collaborative construction workflows. The robot’s functions are primarily driven by the BIM, but it adaptively adjusts its plans based on actual site conditions, while the human co-worker oversees and supervises the process. When necessary, the human co-worker intervenes in the robot’s plan by changing the task sequence or workspace geometry or requesting a new motion plan to help the robot overcome the encountered uncertainties. A drywall installation case study is conducted to verify the proposed workflow. In addition, experiments are carried out to evaluate the system performance using an industrial robotic arm in a research laboratory setting that mimics a construction site and in the Gazebo simulation. Integrating the flexibility of human workers and the autonomy and accuracy afforded by the BIM, the proposed framework offers significant promise of increasing the robustness of construction robots in the performance of field construction work.</p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":"161 ","pages":"Article 104112"},"PeriodicalIF":10.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141290985","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}