Optimization of electrical discharge machining process parameters for Al6061/cenosphere composite using grey-based hybrid approach
(1. Department of Mechanical Engineering, National Institute of Technology Silchar, Assam 788010, India;
2. Department of Production Engineering, National Institute of Technology Agartala, Tripura 799055, India)
2. Department of Production Engineering, National Institute of Technology Agartala, Tripura 799055, India)
Abstract: Fly ash has congregated considerable attention as a potential reinforcement for aluminum matrix composites (AMCs) to enhance selective properties and reduce the cost of fabrication. However, poor machinability of such AMCs limits their application. The present study focuses on the preparation of cenosphere fly ash reinforced Al6061 alloys by compo casting method. X-ray diffraction analysis of the prepared AMCs exposes the presence of cenosphere particles without any formation of other intermetallic compounds. In this study, electrical discharge machining (EDM) was engaged to examine the machinability of the prepared metal matrix composite (MMCs). The measured performance characteristics for the various combinations of input process parameters were considered to be MRR, EWR and SR. Face centered central composite design (CCD) of response surface method (RSM) was employed to design the number of experimental trials required and a hybrid approach of grey-based response surface methodology (GRSM) was imposed for predicting the optimal combination of processing parameter in EDM process. Generous improvement was observed in the performance characteristics obtained by employing both the optimal setting of machining parameters. The optical 3D surface profile graphs of the ED machined surface also revealed the improvement in surface quality and texture employing the optimal processing conditions proposed by hybrid GRSM approach.
Key words: compo casting; electrical discharge machining; response surface method; grey-based response surface methodology; analysis of variance; desirability analysis