Comparative regression and neural network modeling of roughness and kerf width in co2 laser cutting of aluminium

Abstract

Laser cutting is the most promising thermal-based unconventional manufacturing process which can cut complex shapes on different materials. Surface roughness and kerf width are the important characteristics that determine the product quality and rely on the rational selection of the input parameters. The present work focuses on comparing surface roughness and the kerf width predicted using regression and artificial neural network model intended for cutting aluminium by CO2 laser. The independent parameters like laser power, assist gas pressure and cutting speed are varied up to three levels and the proposed Box-Behnken design constitutes 17 experiment runs for data acquisition and further modeling. The coefficient of correlation and the absolute mean error percentage are used for the study and comparison of regression and artificial network models. The artificial neural network has a lower mean absolute percentage error (MAPE) than the regression models. In addition, the R-value of the artificial neural network is greater than those of the regression models. The regression modeling methodology has been shown to be inadequate in predicting desired parameters while more reliable results have been obtained with the use of artificial neural network.