Abstract
Surface roughness is one of the most important product quality characteristics. In this paper, experimental investigation of surface roughness was performed in high speed turning of hardened AISI P20 steel with CBN tool based on design of experiment. The influence of cutting speed, feed rate, depth of cut and nose radius on surface roughness were assessed using analysis of variance (ANOVA). Optimal cutting parameters were found to improve the machining performance. Due to the complexity of machining process, artificial neural network (ANN) was employed to develop the predictive model of surface roughness. Simulations were done to study the relationship between surface roughness and cutting parameters based on the proposed model.
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