Modeling of wire electro-discharge machining of TiC/Fe in situ metal matrix composite using normalized RBFN with enhanced k-means clustering technique

In the present work, wire electro-discharge machinability of 5 vol% TiC/Fe in situ metal matrix composite (MMC) has been studied. Four input process parameters such as pulse on-time, pulse off-time, wire feed-rate, and average gap voltage have been considered, while cutting speed and kerf width have been considered as the measure of performance of the process. The presence of nonconductive TiC particles and formation of Fe₂O₃ during machining make the process very much unstable and stochastic. Thus, modeling the process either by an analytical or numerical method becomes extremely difficult. In the present study, modeling of wire electro-discharge machining process by normalized radial basis function network (NRBFN) with enhanced k-means clustering technique has been done. In order to measure the effectiveness of this approach, the process has also been modeled by NRBFN with traditional k-means technique, and a comparison has been made between the two models. It is seen that both the models can predict the cutting speed and kerf width successfully, but NRBFN with enhanced k-means clustering technique yields better results than NRBFN with traditional k-means technique. Both the models have been used to carry out the parametric study and, finally, have been compared with the experimental results.