A coupled multi-linear regression and genetic algorithm-based modelling and optimisation of surface roughness in machining of brass

Abstract

In this paper, the authors present a multi-linear regression-based approach for the modelling of surface roughness during the turning of a commercial brass alloy. Three regression models are developed by utilising the experimental data gathered following a full-factorial-based design-of-experiments (DoEs) methodology. While the conventional practice has been to develop regression models using the entire experimental datasets, we deviate from the same and employ only a subset of the available data for the purpose, the remaining data being used for the model validation. The results obtained herein reveal that the second order regression model is statistically better than the other two in predicting the surface roughness for both the datasets. The global minimum surface roughness is determined by using the developed regression models in conjunction with the genetic algorithm-based single objective optimisation. The regression models serve as candidate objective functions for the genetic algorithm. The optimisation results reveal that the global minimum obtained using the second order regression model is in close agreement (accuracy - 94%) with the experimentally obtained minimum surface roughness and thus reaffirms the effectiveness of the second order regression model in predicting the surface roughness of brass during turning operation.