Optimizing ANN performance using DOE: application on turning of a titanium alloy

Abstract

A methodology is presented to optimize the performance of an Artificial Neural Network (ANN) using Design of Experiments (DOE). 8 different feed forward back propagation (FFBP) ANNs were developed and tested according to the L8 full factorial orthogonal array. The 3 parameters tested were: Number of Hidden Neurons, Learning rate, and Momentum; each one having two levels. By utilizing the analysis of means (ANOM) and the analysis of variances (ANOVA), the optimum levels of ANN parameters were determined. The developed ANN was applied for predicting cutting forces and average surface roughness in turning Ti-6Al-4V alloy.