Optimization Of Multi-Performance Characteristics in the Turning Of GFRP(E) Composites using Principle Component Analysis combined with Grey Relational Analysis

Abstract

Glass Fiber Reinforced Polymers are a successful alternative, having numerous advantages over traditional reinforcement methods; giving complex structures a longer service life. With the upcoming applications of composites in various areas, machining of these materials has become a major concern for the manufacturing industries. The current knowledge and state of art of machining of GFRP composites, unfortunately, is seemed inadequate for its optimal economic utilization. This paper presents an optimization study made on machining of randomly oriented glass fiber reinforced (GFRP) polymer composite rods. The influence of machining parameters on surface roughness parameters such as Ra, Rp, Rq, Rv, Rsm; Force and Material removal rate in turning operation of the glass fiber reinforced composite was the followed during the study. Turning parameters such as tool nose radius, cutting speed, feed rate and depth of cut are optimized with consideration of multiple performance characteristics. 27 experimental runs based on L27 orthogonal arrays were performed to determine the best factor level condition.Carbide inserts of fine grade was used as a cutting insert for machining. PCA (Principle component analysis) in combination with Grey relational analysis was applied for the optimization of chosen responses, which gave the parametric combination of A2B2C3D1 i.e. Tool nose radius at Level 2(0.8mm), cutting speed at Level 2(160mm/min), Feed rate at level 3(0.25mm/rev) and depth of cut at level 1(.6mm)as an optimum combination. The confirmatory experiment at these settings gives maximum value of the OPI (Overall Performance Index), validating the results. Based on the mathematical analysis of the experimental results it was found that the responses depend mainly on the feed rate parameter.