Prediction of Surface Roughness in Turning of Ud- Gfrp Using Mathematical Model and Simulated Annealing

Glass fiber reinforced plastic (GFRP) composite materials are a feasible alternative to engineering materials and are being extensively used in variety of engineering applications. Accordingly, the need for accurate machining of composites has increased enormously. During machining, the obtained surface roughness is an important aspect. The present investigation deals with the study and development of a surface roughness prediction model for the machining of unidirectional glass fiber reinforced plastics (UD-GFRP) composite using regression modeling and optimization by simulated annealing. The process parameters considered include tool nose radius, tool rake angle, feed rate, cutting speed, cutting environment (dry, wet, cooled) and depth of cut. The predicted values from surface roughness model are compared with the experimental values. The results of prediction are quite close with the experimental values.