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

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.

[1]  Sami Ekici,et al.  An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM , 2009, Expert Syst. Appl..

[2]  Li-Chih Ying,et al.  Using adaptive network based fuzzy inference system to forecast regional electricity loads , 2008 .

[3]  S. K. Choudhury,et al.  On the machining of alumina and glass , 2002 .

[4]  Biswanath Doloi,et al.  Investigation into the influences of the power circuit on the micro-electrochemical discharge machining process , 2009 .

[5]  Abdulkadir Sengur.  An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases , 2008 .

[6]  P. Venkateswara Rao,et al.  The drilling of Al2O3 using a pulsed DC supply with a rotary abrasive electrode by the electrochemical discharge process , 2008 .

[7]  Chitralekha Mahanta,et al.  A novel approach for ANFIS modelling based on full factorial design , 2008, Appl. Soft Comput..

[8]  V. Fascio,et al.  Machining of non-conducting materials using electrochemical discharge phenomenon – An overview , 2005 .

[9]  Grzegorz Skrabalak,et al.  Building of rules base for fuzzy-logic control of the ECDM process , 2004 .

[10]  A. Şengur.  Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification , 2008 .

[11]  Elif Derya íbeyli.  Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of ophthalmic arterial disorders , 2008 .

[12]  B. Bhattacharyya,et al.  Experimental investigations into electrochemical discharge machining (ECDM) of non-conductive ceramic materials , 1999 .

[13]  Chong Nam Chu,et al.  Micro-structuring of glass with features less than 100 μm by electrochemical discharge machining , 2009 .