Method for predicting cutter remaining life based on multi-scale cyclic convolutional network

Abstract

In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life prediction. To extract the features with time-series information to predict the remaining cutter life more effectively, this article proposes a new deep neural network, which is named the multi-scale cyclic convolutional neural network. In the multi-scale cyclic convolutional neural network, a multi-scale cyclic convolutional layer is constructed to memorize the degradation state at different moments and to mine the timing characteristics of multiple sensor data. Multi-scale features are extracted through multi-scale convolution, and the convergence of parameters is improved by layer-by-layer training and fine-tuning. Finally, the remaining cutter life is predicted based on the features. The comparison with the published prediction methods of convolutional neural network and recurrent neural network models proves that our method (multi-scale cyclic convolutional neural network) is superior in improving the precision and accuracy of remaining cutter life prediction. This method breaks through the limitations of the convolutional neural network prediction model in this field and provides a theoretical basis for evaluating the remaining service life of the cutter.