{"title":"从 MBD 模型中提取加工特征,定义特征级数字孪生工艺模型,用于智能工艺规划","authors":"Jingjing Li, Guanghui Zhou, Chao Zhang, Junsheng Hu, Fengtian Chang, Andrea Matta","doi":"10.1007/s10845-024-02406-2","DOIUrl":null,"url":null,"abstract":"<p>The booming development of emerging technologies and their integration in process planning provide new opportunities for solving the problems in traditional trial-and-error process planning. Combining digital twin with 3D computer vision, this paper defines a novel feature-level digital twin process model (FL-DTPM) by extracting machining features from model-based definition models. Firstly, a multi-dimensional FL-DTPM framework is defined by fusing on-site data, quality information, and process knowledge, where the synergistic mechanism of its virtual and physical processes is revealed. Then, 3D computer vision-enabled machining features extraction method is embedded into the FL-DTPM framework to support the reuse of process knowledge, which involves the procedures of data pre-processing, semantic segmentation, and instance segmentation. Finally, the effectiveness of the proposed features extraction method is verified and the application of FL-DTPM in machining process is presented. Oriented to the impeller process planning, a prototype of FL-DTPM is constructed to explore the potential application scenarios of the proposed method in intelligent process planning, which could provide insights into the industrial implementation of FL-DTPM for aerospace manufacturing enterprises.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"94 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning\",\"authors\":\"Jingjing Li, Guanghui Zhou, Chao Zhang, Junsheng Hu, Fengtian Chang, Andrea Matta\",\"doi\":\"10.1007/s10845-024-02406-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The booming development of emerging technologies and their integration in process planning provide new opportunities for solving the problems in traditional trial-and-error process planning. Combining digital twin with 3D computer vision, this paper defines a novel feature-level digital twin process model (FL-DTPM) by extracting machining features from model-based definition models. Firstly, a multi-dimensional FL-DTPM framework is defined by fusing on-site data, quality information, and process knowledge, where the synergistic mechanism of its virtual and physical processes is revealed. Then, 3D computer vision-enabled machining features extraction method is embedded into the FL-DTPM framework to support the reuse of process knowledge, which involves the procedures of data pre-processing, semantic segmentation, and instance segmentation. Finally, the effectiveness of the proposed features extraction method is verified and the application of FL-DTPM in machining process is presented. Oriented to the impeller process planning, a prototype of FL-DTPM is constructed to explore the potential application scenarios of the proposed method in intelligent process planning, which could provide insights into the industrial implementation of FL-DTPM for aerospace manufacturing enterprises.</p>\",\"PeriodicalId\":16193,\"journal\":{\"name\":\"Journal of Intelligent Manufacturing\",\"volume\":\"94 1\",\"pages\":\"\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2024-05-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Intelligent Manufacturing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s10845-024-02406-2\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02406-2","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Defining a feature-level digital twin process model by extracting machining features from MBD models for intelligent process planning
The booming development of emerging technologies and their integration in process planning provide new opportunities for solving the problems in traditional trial-and-error process planning. Combining digital twin with 3D computer vision, this paper defines a novel feature-level digital twin process model (FL-DTPM) by extracting machining features from model-based definition models. Firstly, a multi-dimensional FL-DTPM framework is defined by fusing on-site data, quality information, and process knowledge, where the synergistic mechanism of its virtual and physical processes is revealed. Then, 3D computer vision-enabled machining features extraction method is embedded into the FL-DTPM framework to support the reuse of process knowledge, which involves the procedures of data pre-processing, semantic segmentation, and instance segmentation. Finally, the effectiveness of the proposed features extraction method is verified and the application of FL-DTPM in machining process is presented. Oriented to the impeller process planning, a prototype of FL-DTPM is constructed to explore the potential application scenarios of the proposed method in intelligent process planning, which could provide insights into the industrial implementation of FL-DTPM for aerospace manufacturing enterprises.
期刊介绍:
The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.