Cutter abrasion online monitoring method based on wavelet packet analysis and radial basis function (RBF) neural network

Abstract

The invention relates to a cutter abrasion online monitoring method based on wavelet packet analysis and a radial basis function (RBF) neural network. The method comprises the steps that shear force coefficients and cutting edge force coefficients of tangential force and radial force in different cutter abrasion states are calibrated by means of an instantaneous cutting force coefficient recognition method; and by analyzing the correlation between cutting force coefficients and cutter abrasion, the coefficients are taken as cutter abrasion characteristic parameters and input into a RBF neutralnetwork model after being subjected to normalization processing. An input layer of a RBF neutral network monitoring model training process comprises cutting force characteristics, cutting vibration characteristics, the shear force coefficients and the cutting edge force coefficients after being subjected to normalization processing; and an output layer comprises the cutter rear cutter surface abrasion capacity after being subjected to normalization processing; a hidden layer comprises neurons obtained through radial basis function iterative optimization; and it is verified that the RBF neuralnetwork monitoring model has the advantages of high response speed and high recognition precision through cutter abrasion monitoring experiments.

Description

technical field [0001] The invention belongs to the on-line monitoring method of tool wear based on wavelet packet analysis and radial basis function neural network (radial basis function neural network), and more specifically relates to a method for extracting tool wear during processing by using wavelet packet analysis and instantaneous cutting force coefficient identification method. Wear eigenvalues, using the RBF neural network to train the eigenvalues, so as to accurately monitor tool wear through the trained RBF neural network monitoring model. Background technique [0002] As an important part of advanced manufacturing technology, the intelligent online monitoring technology of tool status has become the subject of this research field in recent years. As the direct executor of the cutting process, the tool inevitably suffers from wear and damage during the cutting process of the workpiece. The change of the tool state directly leads to an increase in cutting force, a...

AI Technical Summary

Problems solved by technology

[0006] In order to avoid the deficiencies of the prior art, the present invention proposes an online tool wear monitoring method based on wavelet packet analysis and RBF neural network, which solves the problem of distinguishing between the time domain method and the frequency domain method in the frequency domain and time domain when processing signals. low rate problem

Images