Texture classification of machined surfaces using image processing and machine learning techniques

Abstract

The identification of surface texture images from machining surfaces using image processing techniques has been a prominent research area in the recent decades. The aim of this paper is to identify various machined surface texture images using machine learning techniques. Charge coupled device is used to capture images of machined components. Based on captured images, twelve statistical features are extracted and feature vector is formed. Grey Level Co-occurrence Matrix is used to extract statistical features from the machined surface images. Four Machine learning algorithms such as Random Forest, Support Vector Machine, Artificial Neural Network and J48 were utilized to characterize machined surfaces. Training and Ten-fold cross validation process is utilized for identification of machined component images. It is found that Artificial Neural Network and Random forest give 100 % training accuracy and 99% cross validation accuracy. Results obtained demonstrate the efficiency of proposed methodology, which is useful for identifying texture images.

Keywords

• surface texture
• grey level co-occurrence matrix
• feature extraction
• classification
• ten-fold cross validation