Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills-Reference-Cited by-同舟云学术

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Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills

Published:2023-10 Issue: Volume:70 Page:69-98
ISSN:0278-6125
Container-title:Journal of Manufacturing Systems
language:en
Short-container-title:Journal of Manufacturing Systems

Author:

Ma Wei,Liu Xianli,Yue Caixu,Wang Lihui,Liang Steven Y.

Funder

National Natural Science Foundation of China

International Cooperation and Exchange Programme

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference66 articles.

1. State-of-the-art methods and results in tool condition monitoring: a review;Rehorn;Int J Adv Manuf Technol,2005

2. Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model;Javed;J Intell Manuf,2018

3. Tool wear monitoring using acoustic emissions by dominant-feature identification;Zhou;IEEE Trans Instrum Meas,2011

4. Prognosis of the probability of failure in tool condition monitoring application-a time series based approach;Wu;Int J Adv Manuf Technol,2015

5. Gaussian process regression for tool wear prediction;Kong;Mech Syst Signal Process,2018

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