Comparison of Machine Learning Algorithms in Predicting the Cutting Force for Thermal Assisted Machining of Ti6Al4V

Abstract

This paper is focused to predict cutting force of the titanium alloy (Ti–6Al–4V) due to  the effect of feed rate, speed and temperature during the machining operation. The design of experiments was used to generate various combinations of feed rate, speed and temperature. Finite element modeling (FEM) using Abaqus used for simulate the machining parameters. The obtained results from FEM model and the experimental work are used to train the model using machine learning algorithm. The trained model is allowed to predict cutting force for various combinations of feed rate, speed and temperature. To verify the accuracy, regression analysis has been adopted in the paper to develop a second prediction model for cutting force.