Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : Elsevier
Source : Expert Systems with Applications, Elsevier, Volume 38, Issue 12, Number 12, p.15202–15207 (2011)
Url : http://www.sciencedirect.com/science/article/pii/S0957417411008657
Keywords : Feature extraction; Decision Tree; Statistical features; Support Vector Machine; Tool condition monitoring
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2011
Abstract : The studies on tool condition monitoring along with digital signal processing can be used to prevent damages on cutting tools and workpieces when the tool conditions become faulty. These studies have become more relevant in today’s context where the order realization dates are crunched and deadlines are to be met in order to catch up with the competition. Based on a continuous acquisition of signals with sensor systems it is possible to classify certain wear parameters by the extraction of features. Data mining approach is extensively used to probe into structural health of the tool and the process. This paper discusses condition monitoring of carbide tipped tool using Support Vector Machine and compares the classification efficiency between C-SVC and ν-SVC. It further analyses the results with other classifiers like Decision Tree and Naïve Bayes and Bayes Net. The vibration signals are acquired for various tool conditions like tool-good condition, tip-breakage, etc. The effort is to bring out the better features-classifier combination.
Cite this Research Publication : Dr. Elangovan M., Sugumaran, V., Dr. K. I. Ramachandran, and Ravikumar, S., “Effect of SVM kernel functions on classification of vibration signals of a single point cutting tool”, Expert Systems with Applications, vol. 38, no. 12, pp. 15202–15207, 2011.