Wear Status Monitoring Method of Milling Cutter Under Variable Working Conditions Based on Transfer Learning and Lightweight SqueezeNet Model

In the existing tool wear status monitoring process, the difference in the distribution of tool wear signal characteristics under different processing conditions leads to insufficient generalization of the model and poor accuracy of wear status recognition. Aiming at the problem, a method for monitoring the wear status of milling cutters under variable working conditions based on transfer learning and a lightweight SqueezeNet model is proposed. Firstly, the continuous wavelet transform (CWT) is employed to realize the conversion of the raw vibration signal to a time–frequency energy diagram to completely preserve the joint feature distribution of the vibration signal in the time and frequency dimensions. Secondly, based on the phased transfer learning strategy and the lightweight SqueezeNet, a monitoring model of the wear status of the milling cutter under variable working conditions is established, which realizes the adaptive and accurate identification of the wear status of the milling cutter under different milling conditions. Finally, comparative experiments were performed using three groups of vibration signals under different milling condition as the model inputs. As demonstrated by the experimental results, the recognition accuracy of the test set of the proposed monitoring model under variable working conditions can reach 94.583%, which is higher than the 91.133% of the LSTM-DBO-SVM model, which proves the accuracy and feasibility of the presented approach under variable working conditions.