Artificial neural network modelling of cutting force components in milling

Abstract

The following paper will give an account of experimental tests and simulation of the cutting force components Fx, Fy and Fz in down milling of AZ91D magnesium alloy. The milling operation employed two milling cutters with a different helix angle, λs = 20° and λs = 50°, and was conducted with changeable milling machining parameters: cutting speed, feed per tooth, axial depth of cut. The simulation part of the study was conducted in Statistica software environment with the application of Multi-Layered Perceptron neural network architecture, and using a “black box” approach, which guarantees a good fit of input and output data obtained from the experimental tests.