Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism-Reference-Cited by-同舟云学术

Deep learning-based tool wear prediction and its application for machining process using multi-scale feature fusion and channel attention mechanism

Published:2021-06 Issue: Volume:177 Page:109254
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Xu Xingwei,Wang Jianwen,Zhong Bingfu,Ming Weiwei,Chen Ming

Funder

Ministry of Industry and Information Technology of the People's Republic of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference47 articles.

1. Development of tool condition monitoring system in end milling process using wavelet features and Hoelder’s exponent with machine learning algorithms;Mohanraj;Measurement,2020

2. A generic tool wear model and its application to force modeling and wear monitoring in high speed milling;Zhu;Mech. Syst. Sig. Process.,2019

3. Z. Li, R. Liu, D.J.J.o.M.P. Wu, Data-driven smart manufacturing: Tool wear monitoring with audio signals and machine learning, 48 (2019) 66-76.

4. Investigation of progressive tool wear for determining of optimized machining parameters in turning;Kuntoğlu;Measurement,2019

5. Tool wear evaluation under minimum quantity lubrication by clustering energy of acoustic emission burst signals;Wang;Measurement,2019

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