{"title":"Automated digital twin generation of manufacturing systems with complex material flows: graph model completion","authors":"Giovanni Lugaresi , Andrea Matta","doi":"10.1016/j.compind.2023.103977","DOIUrl":null,"url":null,"abstract":"<div><p><span>Industry 4.0 determined the emergence of technologies that enable data-driven production planning and control approaches. A digital model can be used to make decisions based on the current state of a </span>manufacturing system<span>, and its efficacy strictly depends on the capability to correctly represent the physical counterpart at any time. Automated model generation techniques such as process mining can significantly accelerate the development of digital twins<span> for manufacturing systems. However, complex production environments are characterized by the convergence of different material and information flows. The corresponding data logs present multiple part identifiers, resulting in the wrong finding of the system structure with traditional process mining techniques. This paper describes the problem of discovering manufacturing systems with complex material flows, such as assembly lines. An algorithm is proposed for the proper digital model generation, aided by the new concept of object-centric process mining. The proposed approach has been applied successfully to two test cases and a real manufacturing system. The results show the applicability of the proposed technique to realistic settings.</span></span></p></div>","PeriodicalId":55219,"journal":{"name":"Computers in Industry","volume":null,"pages":null},"PeriodicalIF":8.2000,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in Industry","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0166361523001276","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 1
Abstract
Industry 4.0 determined the emergence of technologies that enable data-driven production planning and control approaches. A digital model can be used to make decisions based on the current state of a manufacturing system, and its efficacy strictly depends on the capability to correctly represent the physical counterpart at any time. Automated model generation techniques such as process mining can significantly accelerate the development of digital twins for manufacturing systems. However, complex production environments are characterized by the convergence of different material and information flows. The corresponding data logs present multiple part identifiers, resulting in the wrong finding of the system structure with traditional process mining techniques. This paper describes the problem of discovering manufacturing systems with complex material flows, such as assembly lines. An algorithm is proposed for the proper digital model generation, aided by the new concept of object-centric process mining. The proposed approach has been applied successfully to two test cases and a real manufacturing system. The results show the applicability of the proposed technique to realistic settings.
期刊介绍:
The objective of Computers in Industry is to present original, high-quality, application-oriented research papers that:
• Illuminate emerging trends and possibilities in the utilization of Information and Communication Technology in industry;
• Establish connections or integrations across various technology domains within the expansive realm of computer applications for industry;
• Foster connections or integrations across diverse application areas of ICT in industry.