Title: Multi response optimisation of machining parameters in EDM of dual particle (MWCNT + B₄C) reinforced sintered composites

Authors: S. Dhandapani; T. Rajmohanr; D. Vijayan; K. Palanikumar

Addresses: Department of Mechanical Engineering, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Enathur, Kanchipuram – 631561, India ' Department of Mechanical Engineering, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Enathur, Kanchipuram – 631561, India ' Department of Mechanical Engineering, Sri Chandrasekharendra Saraswathi Viswa Maha Vidyalaya, Enathur, Kanchipuram – 631561, India ' Sairam Institute of Technology, Chennai, India

Abstract: Metal matrix composites (MMC) reinforced with nano particles (<100 nm), termed as metal matrix nano composites (MMNC), can conquer the complications associated with the conventional MMCs. Especially multi wall carbon nano tubes (MWCNT) reinforced MMCs can progress the properties of matrix material in terms of frictional resistance and tensile strength. The present paper discusses the multi response optimisation of machining parameter in electrical discharge machining (EDM) of aluminium matrix reinforced with boron carbide (B₄C) and MWCNT prepared by sintering. Experiments were performed on EDM machine using central composite design (CCD). EDM parameters such as of pulse on time (T_on), pulse off time (T_off), current (I), voltage (V) and wt% of MWCNT are selected as process parameters. The EDM performances for MMNCs are evaluated by using the indicators such as meal removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). The second order quadratic models were developed by regression analysis. These mathematical models were then optimised using multi-objective optimisation technique based on genetic algorithm (GA). Surface morphology and the effects of nano particle reinforcement in the drilled holes were studied through SEM micrographs and atomic force microscope (AFM).

Keywords: metal matrix nano composites; MMNC; electrical discharge machining; EDM; metal removal rate; MRR; surface roughness; SR; tool wear rate; TWR; genetic algorithm; GA.

DOI: 10.1504/IJMMM.2018.096048

International Journal of Machining and Machinability of Materials, 2018 Vol.20 No.5, pp.425 - 446

Received: 11 Dec 2017
Accepted: 13 Mar 2018
Published online: 09 Nov 2018 *

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