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A Method of Judgment of Turning Tool Wear Based on Adversarial Neural Network

A neural network and judgment method technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as insufficient number of samples, unsatisfactory diagnosis results, and difficulty in developing deep learning models, so as to avoid rejection rates. The effect of increasing and improving reliability

Active Publication Date: 2021-12-28
JIANGLU MACHINERY & ELECTRONICS GROUP
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  • Application Information

AI Technical Summary

Problems solved by technology

However, the fault diagnosis methods of classical deep learning models usually only consider the case of sufficient number of samples.
In this way, the traditional deep learning model is difficult to develop, and the diagnosis effect is not ideal

Method used

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  • A Method of Judgment of Turning Tool Wear Based on Adversarial Neural Network
  • A Method of Judgment of Turning Tool Wear Based on Adversarial Neural Network
  • A Method of Judgment of Turning Tool Wear Based on Adversarial Neural Network

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Embodiment Construction

[0028] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0029] Such as Figure 1-Figure 3 As shown, a method for judging the wear of a turning tool based on an adversarial neural network includes the following steps:

[0030] S1: Collect the current signal data of the spindle in the CNC machine tool. The collected current signal data of the spindle in the CNC machine tool includes collecting the current signal of the spindle idling and the current signal of the spindle load when the tool is working from the beginning to the wear.

[0031] S2: Perform data preprocessing on the collected spindle current signal data in the CNC machine tool, use wavelet packet transform analysis to obtain a time-frequency diagram, and convert the time-frequency diagram into a 64*64*3 image. The moving average signal is extracted, analyzed using wavelet packet transform, and the obtained image is transformed.

[0032] S3: Usin...

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Abstract

The invention discloses a method for judging the wear of a turning tool based on an adversarial neural network. Sample enhancement; use the current signal data in the enhanced data sample to train the CNN model, adjust the CNN model parameters, and divide the tool wear into primary wear, normal wear, alarm zone, and severe wear; input the current signal data to be measured into the trained CNN model to get classification results. The invention changes the three categories of tools into four categories, and handles the unbalanced problem of data samples by an adversarial neural network, which can effectively detect and predict the state of the tool, and give early warning of tool damage, thereby avoiding the severe wear of the tool during processing Increased scrap rate due to compromised precision improves machine reliability.

Description

technical field [0001] The invention relates to the field of CNC machine tools, in particular to a method for judging the wear of a turning tool based on an adversarial neural network. Background technique [0002] With the introduction of "Industry 4.0", the manufacturing industry is booming in our country, and a large number of mechanical processing and mechanical manufacturing are widely demanded. CNC machine tools are called "industrial mother machines" and play a decisive role in the manufacture of major machine parts. According to the current trend, the machining accuracy requirements of the machine are getting higher and higher, and the quality requirements of the turning tools in the machine tool are also getting higher and higher. [0003] From the perspective of processing technology, on the one hand, the use and demand of turning tools are increasing, on the other hand, the working environment of turning tools is relatively harsh, and the processing intensity is ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B23Q17/09G06N3/04G06N3/08G06K9/00
CPCB23Q17/0957G06N3/08B23Q2717/003G06N3/045G06F2218/08G06F2218/12
Inventor 徐惠余吴继春林雪玉霍览坤秦友
Owner JIANGLU MACHINERY & ELECTRONICS GROUP