Publication Type : Journal Article
Thematic Areas : Center for Computational Engineering and Networking (CEN)
Publisher : Expert Systems with Applications
Source : Expert Systems with Applications, Elsevier, Volume 38, Issue 4, Number 4, p.4450–4459 (2011)
Url : http://www.sciencedirect.com/science/article/pii/S0957417410010730
Keywords : PCA; Decision tree; Statistical features; Principle component analysis; Tool condition monitoring
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN), Mechanical Engineering
Year : 2011
Abstract : Tool wear and tool life are the principle areas are focus in any machining activity. The production rate, surface finish of machined component and the machine condition are directly related to the tool condition. This work on tool condition monitoring delves into data mining approach to discover the hidden information available in the tool vibration signals. The use of statistical features derived from the vibration data is used as the primary feature and Principle Component Analysis (PCA) transformed statistical features are evaluated as an alternative. In order to increase the robustness of the classifier and to reduce the data processing load, feature reduction is necessary. The feature reduction using (a) decision tree and (b) feature transformation and reduction using PCA are evaluated independently and the results are compared. The effective combination of feature reducer and classifier for designing the expert system is studied and reported.
Cite this Research Publication : Dr. Elangovan M., S Devasenapati, B., Dr. Sakthivel N.R., and Dr. K. I. Ramachandran, “Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm”, Expert Systems with Applications, vol. 38, no. 4, pp. 4450–4459, 2011.