A real-time prediction method of tool wear based on wavelet packet decomposition and deep learning

Abstract

The invention belongs to the related technical field of tool state monitoring, and discloses a real-time prediction method of tool wear based on wavelet packet decomposition and deep learning, including the following steps: (1) Synchronously collect relevant sensor signals in the process of workpiece processing, and select stable ones among them The signal segment is used as the signal segment to be analyzed, and the signal sample to be analyzed is expanded to increase the sample size; the signal to be analyzed is decomposed and transformed by wavelet packet to obtain multiple two-dimensional matrix of wavelet packet coefficients; (2) the two-dimensional wavelet packet coefficient The matrix correspondence is used as the input of a feature extraction CNN model block, and the one-dimensional feature matrix output by each feature extraction CNN model block is spliced ​​into a longer one-dimensional matrix, and then feature fusion is performed and a two-layer fully connected network is established. This results in a convolutional neural network model; (3) inputting the signal data to be analyzed into the convolutional neural network model to predict the wear amount of the tool in real time. The invention can reduce the cost and has strong applicability.

Description

technical field [0001] The invention belongs to the technical field related to tool state monitoring, and more specifically relates to a real-time tool wear prediction method based on wavelet packet decomposition and deep learning. Background technique [0002] The state of tool wear will directly affect the surface quality of the machined workpiece, thereby affecting the yield rate of the workpiece. Long-term use of the tool in an excessively worn state will greatly affect the spindle accuracy of the machine tool, resulting in long-term shutdown of the machine tool for maintenance. According to relevant research, by accurately monitoring the state of the machine tool tool, the machine tool spindle speed can be increased by 10% to 50% during processing, the downtime of the machine tool can be reduced by 20%, and the factory can save 10% to 40% of the total cost. Therefore, the tool state The detection system has a good market prospect. [0003] At present, the mainstream me...

AI Technical Summary

Problems solved by technology

At the same time, the prediction method uses PReLU as the activation function of the mathematical model, uses the Adam algorithm as the model optimization algorithm, and uses a supervised learning method to establish the relationship between the target signal and the amount of tool wear by analyzing the relevant signals generated by the machine tool during the machining process. relationship, so as to solve the problem of difficult real-time prediction of tool wear

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