Data Extraction and Classification of Machined Module Surface Structure Images Using Artificial Intelligence Techniques

Mechanical Engineering

In recent era use of artificial intelligence techniques for texture analysis of machined surface is gaining importance in the field of manufacturing. The developed vision system uses a CCD camera for scanning gray-scale images from an area of the machined workpiece 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. training identification of textured images is obtained using support vector machine and artificial neural network 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.

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