A multi-sensor monitoring methodology for grinding wheel wear evaluation based on INFO-SVM-Reference-Cited by-同舟云学术

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A multi-sensor monitoring methodology for grinding wheel wear evaluation based on INFO-SVM

Published:2024-02 Issue: Volume:208 Page:111003
ISSN:0888-3270
Container-title:Mechanical Systems and Signal Processing
language:en
Short-container-title:Mechanical Systems and Signal Processing

Author:

Wan Linlin,Chen Zejun,Zhang Xianyang,Wen Dongdong,Ran Xiaoru

Publisher

Elsevier BV

Subject

Computer Science Applications,Mechanical Engineering,Aerospace Engineering,Civil and Structural Engineering,Signal Processing,Control and Systems Engineering

Reference38 articles.

1. Tool wear classification based on convolutional neural network and time series images during high precision turning of copper;Zhou;Wear,2023

2. Machining performance and wear behaviour of polycrystalline diamond and coated carbide tools during milling of titanium alloy Ti-54M;Chiderhouse;Wear,2023

3. Machine vision based condition monitoring and fault diagnosis of machine tools using information from machined surface texture: A review;Liu;Mech. Syst. Signal. Pr.,2022

4. Wear assessment of microcrystalline and electrofused aluminum oxide grinding wheels by multi-sensor monitoring technique;Caraguay;J. Manuf. Process.,2022

5. Measurement and prediction of wear volume of the tool in nonlinear degradation process based on multi-sensor information fusion;Gao;Eng. Fail. Anal.,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on wear state identification of ordered grinding wheel for C/SiC composites based on DBO-ELM;Wear;2024-11

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