Modeling of machined surface roughness and optimization of cutting parameters in face milling

The surface quality is one of the most specified customer requirements and the major indicator of surface quality on machined parts is surface roughness. The surface roughness is mainly a result of various controllable or uncontrollable process parameters and it is harder to attain and track than physical dimensions are. A considerable number of studies have researched the effects of the cutting speed, feed, depth of cut, nose radius and other factors on the surface roughness. In recent studies 1, 2, 3 the effects of some factors on surface roughness has been evaluated and models has been developed. A central task in science and engineering practice is to develop models that give a satisfactory description of physical systems being observed. The goal of this study is to obtain a mathematical model that relates the surface roughness to three cutting parameters in face milling, precisely to the cutting speed, feed rate and depth of cut. In this work two different approaches have been used in order to get the mathematical models. The first approach is a design of experiment (DOE) together with an analysis of variance (ANOVA) and regression analysis (RA). The second approach is modeling by means of artificial neural networks (ANNs).