Title: Optimisation of processing parameters in ECM of AISI 202 using multi objective genetic algorithm

Authors: V. Sathiyamoorthy; T. Sekar

Addresses: Department of Mechanical Engineering, Dr. Navalar Nedunchezhiyan College of Engineering, Tholudur 606 303, India ' Department of Mechanical Engineering, Government College of Engineering, Salem, Anna University, India

Abstract: This paper attempts to optimise the predominant or influencing machining parameters during electrochemical machining (ECM) of AISI 202 austenitic stainless steel which is commonly used in railway rolling stock. The selected influencing parameters are: applied voltage, electrolyte discharge rate and tool feed rate with three levels. Twenty seven experiments were conducted through Design Expert 7.0 software and genetic algorithm (GA) tool was applied to identify the optimum conditions which turn into the best material removal rate (MRR) and surface roughness (SR). The experimental analyses of NaCl aqua's solution reveal that applied voltage of 18 V, tool feed rate of 0.54 mm/min and electrolyte discharge rate of 12 lit/min would be the optimum values in ECM of AISI 202 under the selected conditions, comparing to NaNO3 aqua's solution. For checking the optimality of the developed equation, MRR of 398.666 mm3/min and surface roughness Ra of 2.299135 µm were predicted at applied voltage of 18 V, tool feed rate of 0.54 mm/min and electrolyte discharge rate of 11.99 lit/min. Confirmatory tests showed that the actual performance at the optimum conditions were 391.351 mm3/min and 2.37 µm, the deviation from the predicted performance is less than 4% which has proves the composite desirability of the developed models for MRR and surface roughness.

Keywords: electrochemical machining; ECM; material removal rate; MRR; genetic algorithms; GAs; optimisation; surface roughness; surface quality; process parameters; austenitic stainless steel; railway rolling stock; applied voltage; electrolyte discharge rate; tool feed rate.

DOI: 10.1504/IJENM.2016.077528

International Journal of Enterprise Network Management, 2016 Vol.7 No.2, pp.133 - 141

Received: 20 Oct 2014
Accepted: 02 Dec 2014

Published online: 05 Jul 2016 *

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