Manufacturers focus on developing manufacturing systems that produce superior quality products with acceptable features of safety, quality and with on time delivery at minimum cost. Turning is one of the common machining processes and is widely used in variety of manufacturing industries. And the performance is indicated by surface quality. The determination of optimum cutting parameters achieving better surface roughness is a matter of research for the past few decades.Lot of studies are going on in this field and several models were developed to predict surface roughness of different materials used in turning process but only few studies have been carried out for the prediction of surface roughness in turning of AISI 4140 STEEL.

In this study we developed an artificial neural network (ANN) model for prediction of surface roughness with independent variables feed rate, cutting speed and depth of cut. Turning was conducted on AISI 4140 STEEL work pieces using CVD coated carbide cutting tool. Surface roughness was measured with different cutting speed, feed rate and depth. Also the effect of parameters of surface roughness was studied by keeping two variables constant and other one varying.