OPTIMIZATION OF PROCESSING PARAMETERS IN ELECTROCHEMICAL MACHINING OF AISI 202 USING RESPONSE SURFACE METHODOLOGY

This paper attempts to optimize the predominated machining parameters in Electro Chemical Machining (ECM) of AISI 202 Austenitic stainless steel using Response Surface Methodology (RSM). The chosen material has been used in railway rolling stock. The selected influencing parameters are applied volta...

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Bibliographic Details
Published inJournal of engineering science & technology Vol. 10; no. 6; pp. 780 - 789
Main Authors V. SATHIYAMOORTHY, T. SEKAR, P. SURESH, R. VIJAYAN, N. ELANGO
Format Journal Article
LanguageEnglish
Published Taylor's University 01.06.2015
Subjects
Electrochemical machining
Material removal rate
Optimization
Response surface methodology
Surface roughness
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Summary:This paper attempts to optimize the predominated machining parameters in Electro Chemical Machining (ECM) of AISI 202 Austenitic stainless steel using Response Surface Methodology (RSM). The chosen material has been used in railway rolling stock. The selected influencing parameters are applied voltage, electrolyte discharge rate with three levels and tool feed rate with four levels. Thirty six experiments were conducted through design of experiments and central composite design in RSM was applied to identify the optimum conditions which turn into the best Material Removal Rate (MRR) and Surface roughness (SR). The experimental analyses reveal that applied voltage of 16 V, tool feed rate of 0.54 mm/min and electrolyte discharge rate of 10 L/min would be the optimum values in ECM of AISI 202 under the selected conditions. For checking the optimality of the developed equation, MRR of 298.276 mm3/min and surface roughness Ra of 2.05 µm were predicted at applied voltage of 12.5 V, tool feed rate of 0.54 mm/min and electrolyte discharge rate of 11.8 L/min with composite desirability of 98.05%. Confirmatory tests showed that the actual performance at the optimum conditions were 291.351 mm3/min and 2.17 µm. The deviation from the predicted performance is less than 6% which proves the composite desirability of the developed models for MRR and surface roughness.
ISSN:1823-4690