Prediction of cutting forces in ball-end milling of multi-layered metal materials

Abstract

This paper outlines the experimental exploration of cutting forces produced during ball-end milling of multi-layered metal materials manufactured by the laser engineered net shaping (LENS) process. The research employs an artificial neural network (ANN) technique for predicting the cutting forces during the machining of 16MnCr5/316L four-layered metal material with a solid carbide ball-end mill. Hardness and thickness of the particular manufactured layer in above mentioned advanced material have been considered during training of the ANN model. Model predictions were compared with experimental data and were found to be in good agreement. Experimental results demonstrate that this method can accurately predict cutting force within a maximum prediction error of 4.8 %.