Title: Artificial neural networks and multi response optimisation on EDM of aluminium (A380)/fly ash composites

Authors: V.S. Sreebalaji; K. Ravi Kumar

Addresses: Department of Mechanical Engineering, Dr. NGP Institute of Technology, Coimbatore, India ' Department of Mechanical Engineering, Dr. NGP Institute of Technology, Coimbatore, India

Abstract: Aluminium metal matrix composites reinforced with fly ash particles of three different particle size ranges [(53-75) µm, (75-103) µm and (103-125) µm] were fabricated using stir casting technique. Electrical discharge machining was employed to machine the composite materials with copper electrode. The influence of EDM process parameters namely peak current, pulse-on-time, pulse-off-time, particle size and the percentage fly ash on material removal rate, tool wear rate and surface roughness were investigated. Artificial neural network model was employed to predict the material removal rate, tool wear rate and surface roughness of the composites. The experimental values coincide with the predicted values of the proposed networks. The process parameters are then optimised using desirability-based multi response optimisation technique to maximise the MRR and minimise both TWR and SR. Increase in peak current and pulse-on time increased the MRR while increase in pulse-off time, percentage fly ash and fly ash particle size decreased the MRR. The experimental results along with the ANN model and multi response optimisation will serve as a technical database for aerospace, automotive, military and commercial applications.

Keywords: metal matrix composites; MMCs; electrical discharge machining; EDM; artificial neural networks; ANNs; desirability approach; multiresponse optimisation; aluminium; percentage fly ash; fly ash particles; particle size; stir casting; electro-discharge machining; peak current; pulse-on-time; pulse-off-time; material removal rate; MRR; tool wear rate; surface roughness; surface quality.

DOI: 10.1504/IJCMSSE.2016.081690

International Journal of Computational Materials Science and Surface Engineering, 2016 Vol.6 No.3/4, pp.244 - 262

Received: 12 Oct 2015
Accepted: 12 Apr 2016
Published online: 20 Jan 2017 *

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