Multi-sensor numerical control machine tool cutter wear monitoring method based on deep learning

Abstract

The invention discloses a multi-sensor numerical control machine tool cutter wear monitoring method based on deep learning. The method comprises the following steps that cutting force and vibration signals in a machining process of a numerical control machine tool are collected, and a plurality of sections of signals of the same time intervals are intercepted; the intercepted signals of all sections are normalized and subjected to reduction using a principal component analysis method; a numerical control machine tool cutter wear identification model is built based on a convolutional autocoder,and is trained, and the numerical control machine tool cutter wear identification model comprises a convolution layer, a linear rectification layer, a pooling layer and an auxiliary layer; and the cutting force and vibration signals collected in real time are subjected to normalization treatment and are subjected to reduction by using the principal component analysis method, and then are input into the trained numerical control machine tool cutter wear identification model to obtain a tool wear state identification result. According to the method, wear states of various cutters under different working conditions can be accurately identified in real time.

Description

technical field [0001] The invention relates to the technical field of tool wear monitoring of CNC machine tools, in particular to a method for monitoring tool wear of multi-sensor CNC machine tools based on deep learning. Background technique [0002] Tool wear status monitoring of CNC machine tools refers to the real-time monitoring of tool wear status through various sensor signals during the machining process. Tool wear monitoring is a state identification process, and a complete tool wear monitoring system consists of tools, processing environment, sensors, signal processing, feature extraction, and state identification. [0003] The tool will wear during the machining process, which will affect the machining efficiency and the surface integrity of the workpiece, and even damage the workpiece and destroy the machining accuracy of the machine tool. Therefore, it is necessary to monitor the tool wear status in real time during machining. [0004] After a lot of research...

AI Technical Summary

Problems solved by technology

[0004] After a lot of research and practice, tool wear status monitoring technology has formed a relatively complete system. Most of the existing methods rely on a lot of signal processing technology and diagnosis experience, which makes the extracted wear characteristics unable to accurately and comprehensively reflect the tool The state of wear and tear leads to low accuracy of subsequent state recognition; with the advent of intelligent manufacturing and the era of big data, the types of sensors are very rich, and the amount of monitoring data is extremely

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