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Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain

Zhigang Cai¹, Wangyang Li², Jianxin Song¹

¹School of Mechatronic Engineering, Harbin Institute of Technology, Harbin 150001, China.

Sensors (Basel, Switzerland)

|April 28, 2025

Summary

This summary is machine-generated.

This study introduces a new method for recognizing tool wear states under varying cutting conditions using multi-source unsupervised domain adaptation. The approach enhances machining accuracy and efficiency by better utilizing sensor data from multiple parameters.

Keywords:

multi-source unsupervised domain adaption tool wear state identification transfer learning varying cutting parameters

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Surrogate Model Development for Digital Experiments in Welding

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Published on: March 28, 2025

Area of Science:

Manufacturing Engineering
Machine Learning
Signal Processing

Background:

Accurate tool wear state identification is crucial for machining quality and efficiency.
Current unsupervised domain adaptation methods often rely on single-source transfer learning, limiting their ability to leverage diverse cutting parameter data.
This limitation hinders the improvement of wear state recognition performance.

Purpose of the Study:

To propose a novel wear-state recognition method for variable cutting parameters using multi-source unsupervised domain adaptation.
To address the limitations of single-source domain adaptation in utilizing multi-parameter sensor data.
To enhance the accuracy and efficiency of tool wear state identification in machining processes.

Main Methods:

Utilized non-stationary Transformer encoders for extracting non-stationary common features from sensor data.
Employed sliced Wasserstein distance for domain-specific feature distribution alignment.
Implemented classifier output alignment to reduce domain shift and simplify multi-domain synchronous alignment.

Main Results:

The proposed method effectively extracts relevant features from sensor data under variable cutting parameters.
Domain-specific feature and classifier output alignment successfully reduced domain shift.
Milling experiments demonstrated the superior recognition performance of the developed method.

Conclusions:

The multi-source unsupervised domain adaptation approach significantly improves tool wear state recognition accuracy.
The method effectively handles variations in cutting parameters by leveraging information from multiple sources.
This research offers a promising solution for intelligent manufacturing and predictive maintenance.