Cutting tool wear monitoring method based on multiscale deep convolutional recurrent neural network

Abstract

The invention discloses a cutting tool wear monitoring method based on a multiscale deep convolutional recurrent neural network. The method comprises the following steps of building an input matrix based on cutting tool data preprocessing data measured by a multipath sensor; building a multiscale convolutional neural network, obtaining multiscale characteristics, carrying out characteristic fusionon each branch of the multiscale convolutional neural network based on the output after pooling a maximum value, and finally obtaining the multiscale characteristics; building a deep cycle GRU network for extracting characteristics and expressions of different time scales, wherein the deep cycle GRU network comprises a first layer of GRU network and a second layer of GRU network, the unit numberof the second layer of GRU network is more than the unit number of the first layer of GRU network, and after the multiscale characteristics are processed through the deep cycle GRU network, the characteristics and the expressions of the different time scales are obtained; building a full connecting layer based on the characteristics, and mapping the characteristics to a sample marking space; and building a linear regression layer based on an output result of the full connecting layer, and obtaining a cutting tool wear capacity.

Description

technical field [0001] The invention belongs to the field of cutting tools, in particular to a method for monitoring tool wear based on a multi-scale deep convolutional cyclic neural network. Background technique [0002] During machining, tool wear will reduce the dimensional accuracy and surface integrity of the part. When the tool wear is severe, it may even cause tool damage, resulting in the scrapping of the workpiece and damage to the machine tool. Specifically, the state of the tool has an important influence on the cutting deformation process, and tool wear will not only cause a decrease in the dimensional accuracy of the part, but also deteriorate the surface quality of the part. When the tool enters the severe wear stage, the cutting force increases sharply, the friction between the tool flank and the processed surface of the workpiece intensifies, and the surface temperature of the workpiece rises significantly, causing thermoplastic deformation on the surface of ...

AI Technical Summary

Problems solved by technology

[0004] However, indirect methods also have corresponding disadvantages: most indirect methods for tool wear monitoring are feature-driven

Second, the extraction of these features depends on experience and professional knowledge, which is difficult for non-professionals to complete, making the model not universally applicable; third, feature extraction is difficult to achieve under complex working conditions and strong noise environments; fourth, feature extraction is a Time-consuming and labor-intensive process, making it difficult to improve the efficiency of tool life monitoring

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