Tool Wear State Identification Method of Thin-Walled Parts Milling Process Driven by Digital Twin
SONG Qinghua1, 2, PENG Yezhen1, 2, WANG Runqiong1, 2, LIU Zhanqiang1, 2
1. Shandong University, Jinan 250061, China;
2. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Shandong University, Ministry of Education, Jinan 250061, China
Due to its weak rigidity, thin-walled parts are prone to chatter and deformation in the milling process, which aggravates tool wear. In order to improve the milling efficiency and surface quality of thin-walled parts, a tool wear state recognition method driven by the fusion of digital twin and support vector machine (SVM) is proposed. The feature vectors are extracted by time-frequency domain analysis and wavelet packet transform. The super parameters are optimized by grid search and cross validation (GSCV). Combined with SVM algorithm, the wear state recognition model of milling tool for thin-walled parts is constructed. The experimental results show that SVM algorithm has obvious advantages in the classification and recognition of high-dimensional and small sample data. The recognition accuracy of different milling cutter wear states reaches 96% and 90.16% respectively, and has good generalization ability. Combined with machine learning algorithm, a high fidelity and lightweight digital twin is constructed and embedded into the milling process monitoring platform of thin-walled parts, so as to solve the problems of real-time signal monitoring and online recognition of tool wear state in the machining process.
宋清华,彭业振,王润琼,刘战强. 数字孪生驱动的薄壁件铣削刀具磨损状态识别方法[J]. 航空制造技术, 2023, 66(3): 46-52,60.
SONG Qinghua, PENG Yezhen, WANG Runqiong, LIU Zhanqiang. Tool Wear State Identification Method of Thin-Walled Parts Milling Process Driven by Digital Twin[J]. Aeronautical Manufacturing Technology, 2023, 66(3): 46-52,60.