Grey Relational Analysis Parameter-Based Predictive Modelling of Surface Roughness

Abstract

Grey relational analysis is a widely used approach for the purposes of decision making, prediction and relational investigation. This study utilizes the grey relational analysis for modelling surface roughness during the single point diamond turning of RSA-443. The utilized parameter in this study is the grey relational grade together with cutting speed, feed, and depth of cut. The Taguchi L9 orthogonal array has been utilized for designing the experiment, with three extra experimental runs being carried out for the purposes of validating the developed model. The developed model indicates that the cutting parameters are insignificant as predictors of surface roughness. Grey relational grade is the only significant predictor of surface roughness. Acoustic emission signal root mean square has been used for determining the grey relational grade in the study. The grey relational analysis-based surface roughness values have been compared to experimentally obtained values by using the Mean Absolute Percentage Error (MAPE). The accuracy levels are an exhibition of high prediction power of the model. Pair t-test results indicate the lack of statistical significance in the difference between the experimentally measured and predicted surface roughness values.