Mathematical Model to Predict the Surface Roughness on the Machining of Glass Fiber Reinforced Polymer Composites

Abstract

Glass fiber reinforced polymer composite materials are finding increased applications in many different engineering fields. Accordingly, the need for accurate machining of composites has increased enormously. In the present work, a mathematical model has been developed to predict the surface roughness of machined glass fiber reinforced plymer (GFRP) work piece using regression analysis and analysis of variance (ANOVA) in order to study the main and interaction effects of machining parameters, viz., cutting speed, work piece fiber orientation angle, depth of cut, and feed rate. The adequacy of the developed model is verified by calculating the correlation coefficient. This model can be effectively used to predict the surface roughness of the machined GFRP components.