Xuedong Zhang, Wenlei Sun, Renben Jiang, Dajiang Wang
{"title":"Comprehensive collaborative integration method for high-voltage coil manufacturing workshop based on industrial internet identification and resolution","authors":"Xuedong Zhang, Wenlei Sun, Renben Jiang, Dajiang Wang","doi":"10.1049/cim2.12095","DOIUrl":null,"url":null,"abstract":"<p>The chaotic identification and resolution, inadequate data interoperability, and inefficient management of resources in the high-voltage coil production workshop limited the effectiveness of its management, and posed significant challenges. To address this issue, the authors establish a comprehensive interconnected digital workshop for high-voltage coil manufacturing based on Industrial Internet Identification and Resolution as well as the 5G technology. A comprehensive framework model is developed for the high-voltage coil workshop, along with a formal modelling and tagging approach for objects within the high-voltage coil workshop. In addition, a management shell modelling method for the complete set of resources in the high-voltage coil workshop is explored. An analytical identification and interoperability mechanism for the full resource of the high-voltage coil workshop is introduced. Furthermore, a trusted shared space is developed for the complete resource data of the high-voltage coil workshop. Finally, a field validation is conducted within a specific high-voltage coil production workshop. The obtained results demonstrate that the proposed methods and models facilitate the unified access, mutual integration, and efficient management of the entire resources within the high-voltage coil workshop. These achievements serve as a crucial reference for the implementation and advancement of interconnected manufacturing workshops.</p>","PeriodicalId":33286,"journal":{"name":"IET Collaborative Intelligent Manufacturing","volume":"6 2","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/cim2.12095","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IET Collaborative Intelligent Manufacturing","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/cim2.12095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
The chaotic identification and resolution, inadequate data interoperability, and inefficient management of resources in the high-voltage coil production workshop limited the effectiveness of its management, and posed significant challenges. To address this issue, the authors establish a comprehensive interconnected digital workshop for high-voltage coil manufacturing based on Industrial Internet Identification and Resolution as well as the 5G technology. A comprehensive framework model is developed for the high-voltage coil workshop, along with a formal modelling and tagging approach for objects within the high-voltage coil workshop. In addition, a management shell modelling method for the complete set of resources in the high-voltage coil workshop is explored. An analytical identification and interoperability mechanism for the full resource of the high-voltage coil workshop is introduced. Furthermore, a trusted shared space is developed for the complete resource data of the high-voltage coil workshop. Finally, a field validation is conducted within a specific high-voltage coil production workshop. The obtained results demonstrate that the proposed methods and models facilitate the unified access, mutual integration, and efficient management of the entire resources within the high-voltage coil workshop. These achievements serve as a crucial reference for the implementation and advancement of interconnected manufacturing workshops.
期刊介绍:
IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly.
The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).