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

A technology of wavelet packet analysis and neural network, applied in the field of online monitoring of tool wear based on wavelet packet analysis and RBF neural network, can solve the problem of low resolution

Active Publication Date: 2018-08-03
NORTHWESTERN POLYTECHNICAL UNIV
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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

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

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specific Embodiment approach

[0032] Refer to attached Figure 1-6 , taking the milling process of titanium alloy, a general aviation material, as an example, the specific implementation of the present invention will be described.

[0033] The present invention proposes an online tool wear monitoring method based on wavelet packet analysis and RBF neural network, with Figure 1-2 Indicates the process flow and feature extraction process of the tool wear on-line monitoring method, which mainly includes 6 steps:

[0034] Step 1: Under a certain working condition, use constant cutting parameters to process the part. The tool mills the side edge with a radial distance of 1mm and an axial depth of 2mm on the titanium alloy workpiece 58 times. The tool changes from initial wear to Blunt wear, measuring the flank wear of the tool after each machining, and extracting 50 sets of tool wear measurement values ​​as the output value of the neural network for training. At the same time, a Kistler 9123C rotary dynamome...

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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...

Claims

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Application Information

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IPC IPC(8): B23Q17/09
CPCB23Q17/0957
Inventor 张定华李涛罗明张仲玺陈曦罗欢
Owner NORTHWESTERN POLYTECHNICAL UNIV
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