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