Tool Wear Identification in Turning Titanium Alloy Based on SVM

Liao, Zhi Rong; Li, Sheng Ming; Lu, Yong; Gao, Dong

Abstract

Titanium alloy is difficult cutting materials, the samples of toolwear features are hard to acquire because of short tool life. In terms of the characteristic, Support Vector Machine (SVM) is proposed in this paper to monitor tool condition, the energy ratio of six different frequency bands of acoustic emission (AE) signal are extracted as cutting tool state features, SVM is trained and tested using these features, Good classification results were achieved by using test set. © (2014) Trans Tech Publications, Switzerland.

Citation

Liao, Z. R., Li, S. M., Lu, Y., & Gao, D. (2014). Tool Wear Identification in Turning Titanium Alloy Based on SVM. Materials Science Forum, 800-801, 446-450. https://doi.org/10.4028/www.scientific.net/MSF.800-801.446

• Journal Article Type: Article
• Publication Date: 2014
• Deposit Date: May 11, 2023
• Journal: Materials Science Forum
• Print ISSN: 0255-5476
• Electronic ISSN: 1662-9752
• Publisher: Trans Tech Publications
• Peer Reviewed: Peer Reviewed
• Volume: 800-801
• Pages: 446-450
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.800-801.446
• Keywords: Mechanical Engineering; Mechanics of Materials; Condensed Matter Physics; General Materials Science
• Public URL: https://nottingham-repository.worktribe.com/output/3128875
• Publisher URL: https://www.scientific.net/MSF.800-801.446