Modelling of electrical discharge machining process using regression analysis, adaptive neuro-fuzzy inference system and genetic algorithm

Kuntal Maji, Dilip Kumar Pratihar and Suprakash Patra

International Journal of Data Mining, Modelling and Management, 2010, vol. 2, issue 1, 75-94

Abstract: Input-output relationships of an electrical discharge machining process have been determined based on some experimental data (collected according to a non-rotatable and face-centred central composite design) using statistical regression analysis and adaptive neuro-fuzzy inference system. Three input parameters, such as peak current, pulse-on-time and pulse-duty-factor and two outputs, namely material removal rate and surface roughness have been considered for the said modelling. The performances of the developed models have been checked using some test cases collected through the real experiments. Both single- as well as multi-objective optimisation problems have been formulated and solved using genetic algorithm. A set of optimal input parameters has been identified to ensure the maximum material removal rate and minimum surface roughness. An interesting Pareto-optimal front of solutions has also been obtained.

Keywords: electrical discharge machining; EDM; electro-discharge machining; regression analysis; optimisation; genetic algorithms; adaptive neuro-fuzzy inference system; ANFIS; material removal rate; MRR; surface roughness. (search for similar items in EconPapers)
Date: 2010
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