Prediction of surface roughness of 6061 aluminium alloy end milling: a machine vision approach
by Nathan Dhanapalan; G. Thanigaiyarasu; K. Vani
International Journal of Machining and Machinability of Materials (IJMMM), Vol. 16, No. 3/4, 2014

Abstract: Surface roughness plays an important role to improve the productivity of the component. Prediction of surface roughness becomes the challenging task in the manufacturing environment. In this work, surface roughness of machined surface of aluminium alloy 6061 is predicted from the image features extracted from images of machined surfaces using machine vision. Machine vision paves a platform to predict the surface roughness of machined surface in non-contact method using CCD camera. Aluminium alloy 6061 is machined using conventional vertical milling machine for various cutting speed, feed and depth of cut combinations. The surface roughness values are determined experimentally using stylus instrument. The artificial neural network (ANN) controller is designed to predict the surface roughness of machined surface from the image features. Image features such as skewness, kurtosis, entropy, mean and standard deviation are given as input parameters for training the neural network and surface roughness value measured experimentally have been given as target values. A regression between input and target parameters has been achieved using neural network to predict the surface roughness of the machined surface.

Online publication date: Sat, 14-Feb-2015

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