Abstract:
To run any machine effectively and economically, the effect of different
process variables on the performance must be known. An experimental
investigation is costly, time-consuming as well as difficult for
a complete understanding of the EDM process. Hence, several
researchers have developed various models of the process using different
approaches like mathematical modeling, finite element analysis,
regression modeling, dimensional analysis, etc. based on certain
assumptions and simplifications, which limit the accuracy of prediction.
In this article, a novel modeling approach is presented to predict
material removal rate (MRR) and tool wear rate (TWR) during machining
of AISI D2 tool steel by the copper electrode using the full factorial
design. The validation was carried out using Taguchi’s L9 orthogonal
array-based confirmation experiments. The results showed that these
models can be used for prediction of MRR and TWR at any set of
process parameters for
