Evaluation Method of Tool Wear State of CNC Machine Tool

A tool wear, CNC machine tool technology, applied in the direction of manufacturing tools, metal processing machinery parts, metal processing equipment, etc., can solve the problems of a single decision algorithm, large fluctuations in model prediction performance, poor versatility, etc., to reduce production costs , better prediction effect, better generality and accuracy

Active Publication Date: 2021-10-19
SHANGHAI JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

Since the wear rate of the tool is related to the working conditions during processing, most of the existing method models are trained under a certain working condition, and the algorithm used is generally a single decision algorithm. The prediction performance fluctuates greatly, and the versatility is not strong

Method used

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  • Evaluation Method of Tool Wear State of CNC Machine Tool
  • Evaluation Method of Tool Wear State of CNC Machine Tool
  • Evaluation Method of Tool Wear State of CNC Machine Tool

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Experimental program
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Embodiment Construction

[0019] In this embodiment, the MATSUURA MC-510V machining center was measured, and the milling test was carried out under 8 different working conditions, in which the cutting depth, the feed speed, and the workpiece are variable working parameters, the cutting depth is set to 1.5 MM and 0.75mm, the feed speed is set to 0.5mm / R and 0.25mm / r, and the material material is stainless steel and cast iron, respectively. Under each condition condition, the test uses a new tool for multiple processing. When machining, the spindle current signal and the vibration signal are collected, and the tool Vb value is recorded each time until the tool VB value exceeds the threshold. The group test ends.

[0020] like figure 1 As shown, for the present embodiment, a method of evaluating a CNC machine tool wear condition, and the specific steps include:

[0021] Step 1) Treat analysis of the collected spindle current signal and vibration signal, select its normal cutting phase signal, extract the ...

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Abstract

A method for evaluating the wear state of CNC machine tool tools. By collecting spindle current signals and vibration signals during CNC machine tool processing, time domain analysis, frequency domain analysis and wavelet packet decomposition are used to extract features, and principal component analysis is used to perform feature dimensionality reduction. And use a variety of integrated learning algorithms to train and predict; finally use the compression factor particle swarm optimization algorithm to obtain the optimal weight of each integrated learning algorithm to build a multi-decision tool wear evaluation model, and evaluate the tool wear state; the present invention uses principal component analysis and Based on the integrated learning algorithm, the improved particle swarm optimization algorithm CFPSO is used to construct a multi-decision tool wear evaluation model. This method is less affected by the working conditions parameters during machining, and has good versatility and accuracy.

Description

Technical field [0001] The present invention relates to a technique in a machining field, and is specifically a method for evaluating a digital control machine tool. Background technique [0002] The existing tool milling state monitoring method mainly includes two types of direct methods and indirect methods. The direct monitoring method is to measure the wear status of the tool surface by electrical resistance, radiation, visual image method, etc., to obtain the wear state of the tool surface, and the indirect monitoring method is to measure the signals associated with the tool wear, vibration. , Sound emission, spindle current / power, etc., evaluate the tool wear. [0003] The indirect monitoring method is compared to the direct monitoring method, its accuracy is low, but real-time online measurement, more suitable for actual production applications, so most of the scholars are based on indirect monitoring. Since the wear velocity of the tool is related to the working conditi...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B23Q17/00
CPCB23Q17/00
Inventor 黄亦翔李鹏夏鹏程刘成良
Owner SHANGHAI JIAOTONG UNIV
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