A genetic algorithmic approach for optimization of surface roughness prediction model in turning using UD-GFRP composite

Abstract

Machining of glass fiber reinforced plastic composite materials is an active area of research in current manufacturing processes. Achieving an improved surface finish is of high priority while machining the polymer based plastics composites due to the poor machinability of glass fibers. The present work deals with the study and development of a surface roughness prediction model for machining unidirectional glass fiber reinforced plastics (UD-GFRP) composite using multiple regression methodology and genetic algorithm approach. The experimentation is carried out with polycrystalline diamond tool, covering a wide range of machining conditions. A second order mathematical model in terms of machining parameters is developed for predicting the surface roughness using multiple regression methodology. An attempt has also been made to optimize machining parameters to minimize surface roughness. The model developed has been further validated.