Artificial Neural Network System for Predicting Cutting Forces in Helical-End Milling of Laser-Deposited Metal Materials

Abstract

When machining difficult-to-cut metal materials often used to make sheet metal forming tools, excessive cutting force jumps often break the cutting edge. Therefore, this research developed a system of three neural network models to accurately predict the maximal cutting forces on the cutting edge in helical end milling of layered metal material. The model considers the different machinability of individual layers of a multilayer metal material. Comparing the neural force system with a linear regression model and experimental data shows that the system accurately predicts the cutting force when milling layered metal materials for a combination of specific cutting parameters. The predicted values of the cutting forces agree well with the measured values. The maximum error of the predicted cutting forces is 5.85% for all performed comparative tests. The obtained model accuracy is 98.65%.