Application of Artificial Intelligence Approach in Modeling Surface Quality of Aerospace Alloys in WEDM Process

Abstract

In this work, artificial neural network (ANN) model is developed for prediction of surface roughness (SR) in wire-cut electrical discharge machining (WEDM) of Inconel 825 aerospace alloy. Four process parameters viz., pulse on time ( T on ), pulse off time ( T off ), peak current ( I p ) and servo voltage ( SV ) are investigated using Box Behnken experimental design.Parametric variation shows that improved SR can be obtained at low levels of T on and SV . A multi-layer feed forward ANN architecture 4-16-1 working on gradient descent back propagation algorithm isfound optimum and is statistically validated by conducting hypothesis tests. The developed model predicted with average 6.38% error and model accuracy is recorded as 93.62%. ANOVA showed that T on is the most significant factor affecting SR with 76.12% contribution; followed by SV and T off respectively with 7.18% and 5.3% contributions. The predictive capability of the developed ANN model is found encouraging and can be effectively used for predicting SR in WEDM of Inconel 825.