A method for monitoring tool wear in varying operating conditions

By combining local feature extraction and feature transformation methods with one-dimensional convolutional neural networks and bidirectional long short-term memory neural networks, and utilizing an unsupervised domain adaptation strategy to align feature distributions, the accuracy problem of tool wear monitoring under varying working conditions is solved, thereby improving monitoring effectiveness and autonomous decision-making capabilities.

CN118848666BActive Publication Date: 2026-07-24TAIYUAN UNIVERSITY OF TECHNOLOGY
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Patent Information

Application Number
CN202410775033.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-06-17
Publication Date
2026-07-24
Estimated Expiration
2044-06-17

AI Technical Summary

Technical Problem

Existing technologies for tool wear monitoring under varying operating conditions have failed to effectively construct feature quantities that are insensitive to changes in process parameters, and rely on target domain sample labels, which limits the application of monitoring methods.

Method used

Local feature extraction and feature transformation methods are used to select insensitive features. A parallel hybrid neural network is constructed by fusing a one-dimensional convolutional neural network and a bidirectional long short-term memory neural network. An unsupervised domain adaptation strategy that represents subspace distance and orthogonal basis mismatch penalty is used to align feature distribution. A contrastive learning module is combined to retain cutting signal information under new working conditions.

Benefits of technology

It improves the accuracy of tool wear monitoring under varying working conditions and enhances the dynamic perception and autonomous decision-making capabilities of the CNC machining process.

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Abstract

The present application relates to the field of mechanical processing, in particular to a variable working condition tool wear monitoring method fusing a feature conversion method and a contrastive unsupervised domain adaptation regression strategy. The present application uses the feature conversion method to select features that are not sensitive to process parameter changes, while converting the feature amplitude; a one-dimensional convolutional neural network and a bidirectional long short-term memory neural network are fused to construct a parallel hybrid neural network as a feature extractor; and an unsupervised domain adaptation strategy representing subspace distance and orthogonal basis mismatch penalty is used to align the feature distribution under variable working conditions. The present application uses the unsupervised domain adaptation strategy based on the representation subspace distance and the orthogonal basis mismatch penalty to reduce the distribution difference of the mixed features learned by the feature extractor under different working conditions, and integrates the contrastive learning module to retain the inherent structural information in the data under the new working condition, thereby improving the generalization ability of the model and helping to improve the dynamic perception and autonomous decision-making ability level of the numerical control processing process.
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