Cutter wear prediction method and device

By extracting and fusing the multi-domain features of CNC machine tools, and using multi-scale one-dimensional convolutional neural network and denoising automatic encoder model to integrate global and local features, the problem of low tool wear prediction accuracy in the existing technology is solved, and more efficient prediction and more scientific tool change decisions are achieved.

CN119646441APending Publication Date: 2025-03-18QILU INST OF TECH
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Patent Information

Application Number
CN202411727357.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-28
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

The prior art is difficult to effectively predict the wear of CNC machine tools, resulting in reduced workpiece quality and sudden machine shutdown. The feature engineering based on traditional machine learning is complex, and the noise information cannot be processed, and the prediction performance is low.

Method used

A tool wear prediction method is adopted to extract multi-domain features and fusion by obtaining data such as vibration signals, cutting force signals and sound emission signals. A multi-domain feature is extracted and fused. A multi-scale one-dimensional convolutional neural network and a denoising automatic encoder model is used to integrate global and local features to improve prediction accuracy.

Benefits of technology

It achieves higher tool wear prediction accuracy, reduces the risk of overfitting, can capture feature information in the data more comprehensively, provide scientific tool change decision support, improves productivity and reduces losses.

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Abstract

The invention provides a tool wear prediction method and device, and the method comprises the steps: obtaining vibration signal data, cutting force signal data, sound emission signal data and tool wear value label data of a tool, constructing a data set, and dividing the data set into a training set, a verification set and a test set; respectively extracting multi-domain features of the training set, the verification set and the test set to obtain a first feature, a second feature and a third feature; inputting the first feature, the second feature and the third feature into a first training model for training, and outputting an intermediate feature; inputting the training set, the verification set and the test set into a second training model for training, and outputting multi-scale features; fusing the intermediate features with the multi-scale features; and inputting the training set of the fused features into a third training model for training, inputting the test set of the fused features into the trained third training model, and outputting a target predicted wear value. According to the method, the prediction precision of tool wear is improved.
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