Cutter wear state monitoring method based on deep gated cycle unit neural network

Abstract

The invention discloses a cutter wear state monitoring method based on a deep gated cycle unit neural network. The method comprises the steps that vibration signals generated in the tool machining process are collected in real time through a sensor, after wavelet threshold denoising, the signals are input into a one-dimensional convolutional neural network for single time step time sequence signallocal feature extraction; then, inputting the time series signal into an improved deep gated recurrent unit neural network CABGRUs to carry out time series signal time series feature extraction; an Attention mechanism is introduced to calculate network weights and reasonably distribute the network weights, and finally, signal feature information with different weights is put into a Softmax classifier to classify tool wear states, so that complexity and limitation caused by manual feature extraction are avoided; meanwhile, the problem that a single convolutional neural network ignores correlation before and after a time sequence signal is effectively solved, and the accuracy of the model is improved by introducing an Attention mechanism. Therefore, the method has the characteristic of improving the real-time performance and accuracy of cutter wear state monitoring.

Description

technical field [0001] The invention belongs to the field of manufacturing process monitoring, and in particular relates to a tool wear state monitoring method based on deep gating cycle unit neural network. Background technique [0002] In the machining process, cutting is the most important processing method for part forming. The wear state of the tool will directly affect the machining accuracy, surface quality and production efficiency of the part. Therefore, tool condition monitoring (Tool Condition Monitoring, TCM) technology is very important for ensuring It is of great significance to improve the quality and realize continuous automatic processing. At present, the tool condition monitoring method mainly adopts the indirect measurement method. This method can collect signals in real time through sensors during the tool cutting process. After data processing and feature extraction, the machine learning (Machine Learning, ML) model is used to monitor the tool wear. [...

AI Technical Summary

Problems solved by technology

The above methods all use deep learning to extract features adaptively, but the convolutional neural network used relies too much on high-dimensional feature extraction, too many convolutional layers are prone to gradient dispersion, and too few convolutional layers are difficult to grasp. Global, and does not take into account the important feature of the correlation between the timing signal samples generated during tool machining

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