Milling vibration state identification based on improved MobileNetV2
iming at the problem that the existing milling vibration state identification model has low accuracy and long training time,a milling vibration state identification method based on improved MobileNetV2 was proposed.The MobileNetV2 backbone structure was used as the backbone feature extraction network,and the Multiscale Attention Fusion Layer(MAFL)and Layered Classifier(LC)were combined to reconstruct the top-level structure of Mobile-NetV2,so as to achieve the purpose of model improvement.Based on variational mode decomposition and Hilbert transform,the data preprocessing of milling vibration state was carried out,and the improved model was trained by combining Transfer Learning(TL)with Fine-tune.Furthermore,the improved MobileNetV2 model and a variety of classical classification models were used to identify and compare the milling vibration state with the variable cutting depth side milling process at different speeds.The results showed that the improved MobileNetV2 had advantages in accuracy and time consumption.The proposed identification method was more suitable for the application require-ments of real-time cognition and chatter warning of cutting state in the field of manufacturing engineering,which had a broad engineering application prospect.
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