Application of Artificial Intelligence Approach in Modeling Surface Quality of Aerospace Alloys in WEDM Process. (2016) - Devarasiddappa, D.; George, Jees; Chandrasekaran, M.; Teyi, Nabam
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.