Feature extraction and classification of machined component texture images using wavelet and artificial intelligence techniques

• Published in: 2017 8th International Conference on Mechanical and Aerospace Engineering (ICMAE) pp. 140 - 144
• Main Authors: Vakharia, Vinay, Kiran, M. B., Dave, Neil Jayeshbhai, Kagathara, Uday
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2017
• Subjects: artificial neural network; Artificial neural networks; Entropy; Milling; support vector machine; Support vector machines; Surface texture; Surface treatment; Testing; texture characterization; wavelet
• ISBN: 1538633051; 9781538633052
• DOI: 10.1109/ICMAE.2017.8038631

In recent years use of image processing techniques for texture analysis of machined surface is gaining importance in the field of manufacturing. This manuscript addresses texture identification methodology using Wavelet transform and artificial intelligence techniques. Captured images of machined surface using Electric discharge machining, milling, sand blasting and shaping is decomposed in to sub images and then discrete wavelet transform is applied on the sub images. To select the base wavelet minimum permutation entropy criterion is applied and statistical features were calculated from the base wavelet. Training and testing of feature vector is performed using two artificial intelligence techniques support vector machine and artificial neural network for identifying textured surface images.100 % training identification of textured images is obtained using support vector machine and artificial neural network and 87.5 % and 100 % testing identification of textured images is obtained using support vector machine and artificial neural network respectively. Results revealed that the present methodology identifies machined surface images with high accuracy.