PREDICTION OF TITANIUM WORKPIECE QUALITIES MACHINED BY EDM DIE SINKING USING NEURAL NETWORK

Abstract

Titanium superior qualities make the material is much preferred than Steel in manufacturing industries. Though the industries need to select the machining process wisely due to the material price is so expensive. In this study, ANN model is used to predict Titanium workpiece qualities machined by EDM Die Sinking. The model is proposed to assist in selecting the machining process wisely. Based on repetitive simulation tests on the model architectures and functions, the best detection rate is determined. The best proposed model with two output features showed good detection rate i.e. surface roughness at 83.3% and dimensional accuracy at 91.7%.