Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm-Reference-Cited by-同舟云学术

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Tool wear state recognition based on GWO–SVM with feature selection of genetic algorithm

Published:2019-06-14 Issue:1-4 Volume:104 Page:1051-1063
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Liao Xiaoping,Zhou Gang,Zhang Zhenkun,Lu Juan,Ma Junyan

Funder

This research was supported by the National Natural Science Foundation of China

Innovation Project of Guangxi Graduate Education

Project of Guangxi Colleges and Universities Key Laboratory Breeding Base for Coastal Mechanical Equipment Design, Manufacturing and Control

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

http://link.springer.com/content/pdf/10.1007/s00170-019-03906-9.pdf

Reference35 articles.

1. Ye Y, Wu M, Ren X, Zhou J, Li L (2018) Hole-like surface morphologies on the stainless steel surface through laser surface texturing underwater. Appl Surf Sci 462:847–855

2. Lauro CH, Brandão LC, Baldo D, Reis RA, Davim JP (2014) Monitoring and processing signal applied in machining processes–a review. Measurement 58:73–86

3. Wang G, Yang Y, Xie Q, Zhang Y (2014) Force based tool wear monitoring system for milling process based on relevance vector machine. Adv Eng Softw 71:46–51

4. Zhu K, Liu T (2018) Online tool wear monitoring via hidden semi-Markov model with dependent durations. IEEE Trans Ind Inf 14(1):69–78

5. Pai PS, Mello GD (2015) Vibration signal analysis for monitoring tool wear in high speed turning of Ti-6Al-4V. JEMS 22(6):652–660

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