A Novel Method for Updating Time-Varying Information of Milling Thin-Walled Components Based on Digital Twin Model

• Published in: IEEE sensors journal Vol. 24; no. 3; p. 1
• Main Authors: Wang, Zhaodong, Liu, Shujie, Li, Hongkun, Ou, Jiayu, Peng, Defeng, Li, Zhi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerodynamics; Computational modeling; Data models; Digital twin; Digital twins; Finite element method; Machine tools; Mathematical models; Milling; Milling (machining); Predictive models; Reduced order models; Signal analysis; Thin-walled components; Time-varying information; Workpieces
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3342025

Within this research, a novel method for updating time-varying information of milling thin-walled components based on digital twin model is proposed, which is used to solve the problem of chatter prediction and feature mapping for thin-walled components during milling. According to the characteristics of the workpiece to be machined, the overall workpiece is divided into two substructure parts. Among them, the substructure of constant workpiece is analysed using the double coordinated free interface method, and the substructure of removed material workpiece is analyzed using the finite element method. Based on interface coordination conditions, the two substructure models are coupled in hybrid coordinate space, which is to obtain the reduced-order model and modal characteristics about overall thin-walled components. Meanwhile, a digital twin model is constructed to characterise the time-varying information of milling thin-walled components, which combines structural dynamics modification method with data model. This proposed method has better generalization ability and efficiency, which is known by comparing with other existing methods. And its normalized relative frequency difference(NRFD) values are all less than 5% and consistently close to 0; its computational efficiency is also improved by 97.06%. A series of milling experiments are carried out to verify the effectiveness of the novel strategy. The results show that the predicted and experimental results are in good agreement with each other, indicating that the currently constructed digital twin model is effective.