Application of Artificial Intelligence Approach in Modeling Surface Quality of Aerospace Alloys in WEDM Process. (2016)

• Record Type: Journal Article
• Authors: Devarasiddappa, D.; George, Jees; Chandrasekaran, M.; Teyi, Nabam
• Is Part Of: Procedia technology. Volume 25(2016)
• Page Start: 1199
• Page End: 1208
• Publication Date: 2016
• Subjects: Artificial neural network -- WEDM -- Inconel 825 -- Surface roughness -- ANOVA; Technology -- Congresses; Technology -- Periodicals; Engineering -- Congresses; Engineering -- Periodicals; Engineering; Technology; Conference proceedings; Periodicals; 605
• DOI: 10.1016/j.protcy.2016.08.239
• Languages: English
• ISSNs: 2212-0173

Abstract

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.