In order to solve the problem of monitoring the tool wear state under multiple operating conditions,a tool wear state prediction method based on deep learning was proposed, and the function for extracting sensitive features was constructed. Based on the tool wear dataset, the tool wear state monitoring model under multiple working conditions was established, and the tool wear state monitoring method under multiple conditions was studied. The results show that when the sensitivity limit is set to 0.3, 56 sensitive features are extracted from the features of the acoustic emission, vibration and current signals; using root mean square error as the evaluation function, the average value of the test sample is 0.123; the prediction result under severe wear state of the tool is better than that of the normal wear state of the tool; multiple sets of repetitive verification experiments prove that the proposed method is stable and effective.
YANG Hanb, ZHAO Fei, ZHU Nili, GAO Zhicong, FENG Chuanfeng. Application of Deep Convolutional Neural Network in Tool Wear Monitoring under Multiple Working Conditions[J]. Machine Tool & Hydraulics,2021,49(3):69-74