Intelligent modelling and prediction of slotted-electrical discharge diamond grinding of aluminium-silicon carbide-graphite composite Online publication date: Sat, 05-Jul-2014
by Ravindra Nath Yadav; Vinod Yadava
International Journal of Abrasive Technology (IJAT), Vol. 6, No. 2, 2013
Abstract: The aim of this paper is to develop an intelligent model for prediction of input-output relationship of slotted-electrical discharge diamond grinding (S-EDDG) process using back propagation neural network (BPNN) architecture. The experiments were conducted on aluminium-silicon carbide-graphite (Al/SiC/Gr) composite workpiece using a slotted abrasive metallic wheel. The pulse current, pulse on-time, pulse off-time, wheel speed and abrasive grit number are taken as input parameters while material removal rate (MRR) and average surface roughness (Ra) are taken as performance parameters. It has been found that the developed BPNN model is capable to predict the MRR and Ra with absolute average percentage errors as 9.46% and 7.94%, respectively. It has also been found that higher MRR and better surface finish can be achieved at pulse current value as 14 A, pulse off-time as 100 µs and wheel speed as 1,300 RPM.
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