Evaluation of Machining Parameters of Hot Turning of Stainless Steel (Type 316) by Applying ANN and RSM

Abstract

Stainless steel (Type 316) workpiece was heated by the mixture of Liquid Petroleum Gas (LPG) and oxygen gas, and it was machined in a lathe under different cutting conditions to study the hot machining characteristics. The orthogonal turning operations were carried out on stainless steel (Type 316) using tungsten carbide (WC) cutting tool insert. During machining the cutting speed (Vc), feed rate (fs), depth of cut (a _p ), and temperature of the workpiece were varied in the range of 200°C, 400°C, and 600°C. Turning experiments were designed based on the statistical three-level full factorial experimental design techniques. An artificial neural network (ANN) and response surface model (RSM) have been developed, which can predict the surface roughness of the machined workpiece. The experimental results concur well with the results obtained from the predictive models.