{"title":"数字孪生制造过程的信息建模和分析方法","authors":"Shuo Su, Aydin Nassehi, Qunfen Qi, Ben Hicks","doi":"10.1016/j.rcim.2024.102813","DOIUrl":null,"url":null,"abstract":"<div><p>This paper introduces a methodology for information modelling and analysis of physical manufacturing processes for digital twins (DTs). It aims to establish a comprehensive and fundamental understanding of manufacturing processes regarding the specific purpose of the DT. Through this methodology, information entities within the manufacturing process that can be represented in DTs, along with their essential attributes, are systematically identified. To achieve this, an information model is firstly proposed to define such entities, termed as representative information. The attributes and hierarchy of entities are formulated based on a requirements analysis of the DT lifecycle. An Integration Definition for Process Modelling 0 (IDEF0) model, Petri nets, and a literature-based identification process are applied to represent the manufacturing process’s workflow and identify information entities. Moreover, the relative importance of representing each information entity in a DT is evaluated by integrating domain-specific knowledge with the specific purpose of the DT. Three types of information analysis are suggested, each with its corresponding methods: empirical analysis, theoretical analysis, and experimental analysis. Specifically, this study explores the material extrusion (MEX) process of the Prusa i3 MK3 printer, resulting in an information model consisting of 128 entities including 21 components, 25 activities and 82 properties. These information entities and associated attributes provide a reference for selecting and synchronizing specific physical information in a DT for estimating dimensional accuracy during the MEX process.</p></div>","PeriodicalId":21452,"journal":{"name":"Robotics and Computer-integrated Manufacturing","volume":"90 ","pages":"Article 102813"},"PeriodicalIF":9.1000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0736584524001005/pdfft?md5=61c0aeecf4f7efa0ebdabbc063706c3a&pid=1-s2.0-S0736584524001005-main.pdf","citationCount":"0","resultStr":"{\"title\":\"A methodology for information modelling and analysis of manufacturing processes for digital twins\",\"authors\":\"Shuo Su, Aydin Nassehi, Qunfen Qi, Ben Hicks\",\"doi\":\"10.1016/j.rcim.2024.102813\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper introduces a methodology for information modelling and analysis of physical manufacturing processes for digital twins (DTs). It aims to establish a comprehensive and fundamental understanding of manufacturing processes regarding the specific purpose of the DT. Through this methodology, information entities within the manufacturing process that can be represented in DTs, along with their essential attributes, are systematically identified. To achieve this, an information model is firstly proposed to define such entities, termed as representative information. The attributes and hierarchy of entities are formulated based on a requirements analysis of the DT lifecycle. An Integration Definition for Process Modelling 0 (IDEF0) model, Petri nets, and a literature-based identification process are applied to represent the manufacturing process’s workflow and identify information entities. Moreover, the relative importance of representing each information entity in a DT is evaluated by integrating domain-specific knowledge with the specific purpose of the DT. Three types of information analysis are suggested, each with its corresponding methods: empirical analysis, theoretical analysis, and experimental analysis. Specifically, this study explores the material extrusion (MEX) process of the Prusa i3 MK3 printer, resulting in an information model consisting of 128 entities including 21 components, 25 activities and 82 properties. These information entities and associated attributes provide a reference for selecting and synchronizing specific physical information in a DT for estimating dimensional accuracy during the MEX process.</p></div>\",\"PeriodicalId\":21452,\"journal\":{\"name\":\"Robotics and Computer-integrated Manufacturing\",\"volume\":\"90 \",\"pages\":\"Article 102813\"},\"PeriodicalIF\":9.1000,\"publicationDate\":\"2024-07-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0736584524001005/pdfft?md5=61c0aeecf4f7efa0ebdabbc063706c3a&pid=1-s2.0-S0736584524001005-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Robotics and Computer-integrated Manufacturing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0736584524001005\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Robotics and Computer-integrated Manufacturing","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0736584524001005","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}
A methodology for information modelling and analysis of manufacturing processes for digital twins
This paper introduces a methodology for information modelling and analysis of physical manufacturing processes for digital twins (DTs). It aims to establish a comprehensive and fundamental understanding of manufacturing processes regarding the specific purpose of the DT. Through this methodology, information entities within the manufacturing process that can be represented in DTs, along with their essential attributes, are systematically identified. To achieve this, an information model is firstly proposed to define such entities, termed as representative information. The attributes and hierarchy of entities are formulated based on a requirements analysis of the DT lifecycle. An Integration Definition for Process Modelling 0 (IDEF0) model, Petri nets, and a literature-based identification process are applied to represent the manufacturing process’s workflow and identify information entities. Moreover, the relative importance of representing each information entity in a DT is evaluated by integrating domain-specific knowledge with the specific purpose of the DT. Three types of information analysis are suggested, each with its corresponding methods: empirical analysis, theoretical analysis, and experimental analysis. Specifically, this study explores the material extrusion (MEX) process of the Prusa i3 MK3 printer, resulting in an information model consisting of 128 entities including 21 components, 25 activities and 82 properties. These information entities and associated attributes provide a reference for selecting and synchronizing specific physical information in a DT for estimating dimensional accuracy during the MEX process.
期刊介绍:
The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.