Artificial neural network modeling of cutting force in turning of Ti-6Al-4V alloy and its comparison with response surface methodology

Abstract

Ti-6Al-4V alloy is widely used in aerospace industry, automotive industry, medical implants, sports industry, etc. due to its high strength to weight ratio and corrosion resistance. However, the specific characteristics of the material makes it difficult to machine. In this work, an attempt has been made to model the cutting force in turning operation under flood cooling environment using Artificial Neural Network (ANN). The experiments were conducted using Box Behnken design of Response Surface Methodology (RSM). The ability of ANN to capture complex interrelationship between input and output dataset is well proved with a large number of data set. In this work ANN is used to model the small but statistically well distributed data and it was found that ANN performs better than RSM even with small data sets. © 2012 Springer India Pvt. Ltd.