Prediction of Remaining Life of Cutting Tool Based on DNN - LIU Sheng-hui; ZHANG Ren-jing; ZHANG Shu-li; MA Chao; ZHANG Hong-guo (Journal of Harbin University of Science and Technology)
In order to better solve the problem that the remaining life of cutting tool is difficult to predict accurately, this paper studies three aspects of the selection of monitoring indexes, the extraction of data features and the establishing of prediction models Firstly, Cutting force and vibration frequency were selected as the indirect monitoring indexes of cutting tool These two indexes can accurately reflect the state of cutting tool, and also can solve the problem that the selecting the direct monitoring indexes causes, the wear analysis results of cutting tool being too subjective in the traditional state monitoring method Secondly, feature extraction is carried out by using wavelet packet analysis, and then the entropy values of the monitoring data are obtained They are taken as the input data Thirdly, the input data are used as the training data and testing data of the prediction model based on Deep Neural Network (DNN) Finally, the simulation experiments of the prediction method are carried out by using the real data of the workshop The results show that the model can effectively predict the useful life