Relevance Vector Machine Based Analyses of MRR and SR of Electrodischarge Machining Designed by Response Surface Methodology

Abstract

Relevance vector machine is found to be one of the best predictive models in the area of pattern recognition and machine learning. The important performance parameters such as the material removal rate (MRR) and surface roughness (SR) are influenced by various machining parameters, namely, discharge current ( ), pulse on time ( on ), and duty cycle (tau) in the electrodischarge machining process (EDM). In this communication, the MRR and SR of EN19 tool steel have been predicted using RVM model and the analysis of variance (ANOVA) results were performed by implementing response surface methodology (RSM). The number of input parameters used for the RVM model is discharge current ( ), pulse on time ( on ), and duty cycle (tau). At the output, the corresponding model predicts both MRR and SR. The performance of the model is determined by regression test error which can be obtained by comparing both predicted MRR and SR from model and experimental data is designed using central composite design (CCD) based RSM. Our result shows that the regression error is minimized by using cubic kernel function based RVM model and the discharge current is found to be one of the most significant machining parameters for MRR and SR from ANOVA.