Modeling performance of response surface methodology and artificial neural network

Abstract

In recent years, response surface methodology (RSM) which is a statistical technique and artificial neural network (ANN) a soft computing technique have been highly used for modelling, simulation and optimization of several physical processes in engineering. Both RSM and ANN strategies have particular computational properties that makes them suitable for making predictions, but differ in their extrapolation and interpolation capabilities on complex non-linear processes, and thus potentially conflict in their predictive accuracy. This study models and compares the capabilities of RSM and ANN in predicting the tensile strength of a 6 mm thick mild steel gas tungsten arc welded plate based on the effects of input variables such as weld current, weld speed, gas flow rate and filler rod. The RSM and ANN based models for prediction were compared using the coefficient of determination criteria. With a higher value of 0.836, the ANN model proved to be a better modeling technique than the RSM model.