Parametric analysis and a soft computing approach on material removal rate in electrochemical discharge machining

Jinka Ranganayakulu, Somashekhar S. Hiremath and Lijo Paul

International Journal of Manufacturing Technology and Management, 2011, vol. 24, issue 1/2/3/4, 23-39

Abstract: Electrochemical discharge machining (ECDM) is a very recent technique in the field of non-conventional machining to machine electrically non-conducting materials efficiently and effectively using the electrochemical discharge phenomenon. In the present paper, an experimental setup of ECDM has been developed and experiments are performed to optimise the process parameters for higher material removal rate (MRR). As there are many process parameters affecting to the process, the Taguchi methodology of robust design of experiments is used for optimization of these process parameters. From the obtained experimental results it was noticed that the material removal mechanism in ECDM is non-linear - the volume of material removed decreases with increasing machining depth. Hence, mathematical modelling is difficult. A soft computing approach called adaptive neuro fuzzy inference system (ANFIS) is adapted to model the non-linear material removal rate.

Keywords: parametric analysis; soft computing; material removal rate; MRR; electrochemical discharge machining; ECDM; adaptive neuro fuzzy inference system; ANFIS; neural networks; fuzzy logic; Taguchi methods; robust design; design of experiments. (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (1)

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