Multiple regression model for surface roughness using full factorial design

This paper describes the modeling of surface roughness in turning of AISI 1042 Steel at four cutting parameters: cutting speed, feed rate, depth of cut and tool nose radius. Full factorial design is implemented to investigate the effects of interactions of these parameters on surface roughness. By using the multiple regression method, we developed a model with high correlation coefficient of 99.55 % and error of 0.07. Moreover, a good agreement was observed between estimated and experimental surface roughness in this model. The effects of cutting parameters and their interactions on surface roughness were investigated by using the analysis of variance.