Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization
Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization
About this item
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Author / Creator
Wang, Jiaqi , Xiang, Zhong , Cheng, Xiao , Zhou, Ji and Li, Wenqi
Publisher
Basel: MDPI AG
Journal title
Language
English
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Publication information
Publisher
Basel: MDPI AG
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Scope and Contents
Contents
Tool wear condition significantly influences equipment downtime and machining precision, necessitating the exploration of a more accurate tool wear state identification technique. In this paper, the wavelet packet thresholding denoising method is used to process the acquired multi-source signals and extract several signal features. The set of featu...
Alternative Titles
Full title
Tool Wear State Identification Based on SVM Optimized by the Improved Northern Goshawk Optimization
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Author / Creator
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_038b4e8c163e43e1a12a824a49281c29
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_038b4e8c163e43e1a12a824a49281c29
Other Identifiers
ISSN
1424-8220
E-ISSN
1424-8220
DOI
10.3390/s23208591
