Tool Wear Identification in Turning Titanium Alloy Based on SVM

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.

• Periodical: Materials Science Forum (Volumes 800-801)
• Pages: 446-450
• DOI: https://doi.org/10.4028/www.scientific.net/MSF.800-801.446
• Online since: July 2014
• Authors: Zhi Rong Liao, Sheng Ming Li*, Yong Lu, Dong Gao
• Keywords: SVM, Titanium Alloy, Tool State Features, Tool Wear

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