Predictive Modeling of Surface Roughness and Material Removal Rate in Turning of UD-GFRP Composites using Carbide (K10) Tool

This work presents an experimental investigation on the influence of six important machining parameters (tool nose radius, tool rake angle, feed rate, cutting speed, cutting environment (dry, wet and cooled) and depth of cut) on surface roughness & material removal rate in the machining unidirectional glass fiber reinforced plastics (ud-gfrp) composite using carbide (k10) cutting tool during turning operation. orthogonal l18 array in taguchi method was employed to carry out the experimental work. anova is performed for significant parameter and later regression model is developed for the significant parameters. validation (confirmatory) results indicate that the model is suitable for surface roughness & material removal rate during the study.