Digital Twin-Driven Tool Wear Monitoring and Predicting Method for the Turning Process

Autor: Lei Wang, Zhongmei Gao, Zhenchuan Shi, Kejia Zhuang, Yaobing Sun
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Symmetry; Volume 13; Issue 8; Pages: 1438
Symmetry, Vol 13, Iss 1438, p 1438 (2021)
ISSN: 2073-8994
DOI: 10.3390/sym13081438
Popis: Accurate monitoring and prediction of tool wear conditions have an important influence on the cutting performance, thereby improving the machining precision of the workpiece and reducing the production cost. However, traditional methods cannot easily achieve exact supervision in real time because of the complexity and time-varying nature of the cutting process. A method based on Digital Twin (DT), which establish a symmetrical virtual tool system matching exactly the actual tool system, is presented herein to realize high precision in monitoring and predicting tool wear. Firstly, the framework of the cutting tool system DT is designed, and the components and operations rationale of the framework are detailed. Secondly, the key enabling technologies of the framework are elaborated. In terms of the cutting mechanism, a virtual cutting tool model is built to simulate the cutting process. The modifications and data fusion of the model are carried out to keep the symmetry between physical and virtual systems. Tool wear classification and prediction are presented based on the hybrid-driven method. With the technologies, the physical–virtual symmetry of the DT model is achieved to mapping the real-time status of tool wear accurately. Finally, a case study of the turning process is presented to verify the feasibility of the framework.
Databáze: OpenAIRE
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