Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization

Abstract

Abstract The surface roughness is a very significant indicator of surface quality. It represents an essential exploitation requirement and influences technological time and costs, i.e. productivity. For that reason, the main objective of this paper is to analyse the influence of face milling cutting parameters (number of revolution, feed rate and depth of cut) on the surface roughness of aluminium alloy. Hence, a statistical (regression) model has been developed to predict the surface roughness by using the methodology of experimental design. Central composite design is chosen for fitting response surface. Also, numerical optimization considering two goals simultaneously (minimum propagation of error and minimum roughness) was performed throughout the experimental region. In this way, the settings of cutting parameters causing the minimum variability in response were determined for the estimated variations of the significant regression factors.