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Research on Tool Wear State Recognition Method Based on Multi-Scale Feature Extraction and Deep Residual Network Fusion Cover

Research on Tool Wear State Recognition Method Based on Multi-Scale Feature Extraction and Deep Residual Network Fusion

Measurement Science Review
Volume 26 (2026): Issue 1 (February 2026)
By: Erliang Liu,  Cong Liu,  Yuhang Du,  Baiwei Zhu and  Limin Shi  
Open Access
|Jan 2026

Authors

Erliang Liu

School of Mechanical Engineering and Railway Transit, Changzhou University, Changzhou, China

Cong Liu

School of Mechanical Engineering and Railway Transit, Changzhou University, Changzhou, China

Yuhang Du

School of Mechanical Engineering and Railway Transit, Changzhou University, Changzhou, China

Baiwei Zhu

baiweizhu@cczu.edu.cn

School of Mechanical Engineering and Railway Transit, Changzhou University, Changzhou, China

Limin Shi

Mechano-Electronic and Automotive Engineering College, Tianshui Normal University, Tianshui, China
DOI: https://doi.org/10.2478/msr-2026-0003 | Journal eISSN: 1335-8871
Journal RSS Feed
Language: English
Page range: 14 - 23
Submitted on: May 19, 2025
|
Accepted on: Nov 12, 2025
|
Published on: Jan 5, 2026
Published by: Slovak Academy of Sciences, Institute of Measurement Science
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open
Keywords:
tool wear,
multi-scale features,
deep residual network,
cutting vibration,
continuous wavelet transform
Related subjects:
Engineering,
Electrical engineering,
Control engineering, metrology and testing

© 2026 Erliang Liu, Cong Liu, Yuhang Du, Baiwei Zhu, Limin Shi, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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