Abstract
In this research work, a feed forward back propagation neural network modeling technique is presented for the prediction of machining characteristics (machining rate and surface roughness) in finish cut WEDM process using pure titanium (workpiece material). The experimentation strategy was planned using Taguchi’s L-18 orthogonal array consisting of seven process parameters to evaluate the effect on machining characteristics such as machining rate (MR) and surface roughness (SR). Two different training algorithms (Gradient Descent for MR and LM Levenberg Marquartd for SR) of artificial neural network methodology have been employed for obtaining best results for modeling the machining characteristics.
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