A hybrid approach for prediction of machining performances of glass fiber reinforced plastic (Epoxy) composites

Abstract

In recent years, Glass Fiber Reinforced Plastic (GFRP) composites are more potential materials for usage in various structural applications in aerospace and automobile industries, owing to its favorable properties, such as light weight, specific strength, high elastic modulus and excellent corrosion resistance. This paper presents a new hybrid approach for multi-objective optimization of machining parameters on GFRP composite using Taguchi coupled grey relational (GR) and desirability function (DF) approach. The end milling of GFRP composite was performed as per Taguchi's L16 orthogonal array by considering three input parameters: viz cutting speed (V), feed rate (F) and depth of cut (D), each at four levels. The material removal rate (MRR), surface roughness (SR) and tool wear (TW) were chosen as output responses. The main effect plot was used to determine the optimum level of machining parameters on the multiple responses. An experimental result revealed that, the optimum level of parameters obtained by GR analysis is V2F3D4. Similarly, the optimum level of parameters obtained by DF approach is V2F4D4. Moreover, analysis of variance (ANOVA) was applied to examine the significance of the parameters. Based on the ANOVA results, it has been proved that the depth of cut was the most significant parameter, trailed by feed rate and cutting speed, respectively. It was also confirmed that the P-values of depth of cut for both analyses are 0.017 and 0.039, which is less than 0.05. Finally, the confirmation tests exhibited that the DA approach was employed better in terms of determining the optimum level of machining parameters as compared to GR analysis.