Classification Of Surface Roughness Of End Milled 6061 Aluminum Alloy Components: Data Mining Approach

Nathan D¹, Thanigaiyarasu G², Vani K³

¹St.Joseph’s Institute of Technology
²Rajalakshmi Engineering College
³Anna University Chennai

Abstract

Automatic classification of surface roughness of machined surface finds its application in product quality. Machine vision is used in the automatic classification of surface roughness of the end milled components. Machine learning approach to machine vision system helps in classifying image features of machined surface. The steps involved in this approach are component machining, surface roughness measurement, Image acquisition, Image preprocessing, feature extraction and classification. There are various data classifier are available in the literature, however the selection of best classifier yield higher classification accuracy. In this article, images of various cutting conditions such as speed, feed and depth of cut were acquired, preprocessed and features are extracted. The features were classified using C4.5 algorithm and Naïve Bayes algorithm and compared. The study result shows that C4.5 algorithm performs better.

Keywords

• Machine Vision, Data Mining, Feature Extraction, Image processing, Classifier, Surface roughness

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Cite this Article as Follows

Nathan D, Thanigaiyarasu G, Vani K. Classification Of Surface Roughness Of End Milled 6061 Aluminum Alloy Components: Data Mining Approach. International Conference on Inter Disciplinary Research in Engineering and Technology 2015. Vol. 1. Chennai: Association of Scientists, Developers and Faculties, 2015. 171-186. Print.