Parametric analysis and a soft computing approach on material removal rate in electrochemical discharge machining Online publication date: Thu, 27-Nov-2014
by Jinka Ranganayakulu; Somashekhar S. Hiremath; Lijo Paul
International Journal of Manufacturing Technology and Management (IJMTM), Vol. 24, No. 1/2/3/4, 2011
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.
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