Sensitivity Analysis of Cutting Force on Milling Process using Factorial Experimental Planning and Artificial Neural Networks

This paper aim to investigate and to compare the capabilities of the Artificial Neural Networks (ANN) and the Factorial Experimental Planning (FEP) to measure the most significant variables on milling process that influence the cutting force. The force data were acquired by an experimental apparatus and the statistical inference of the force were set by the Root Mean Squared value. The FEP used the multiple linear regression technics to evaluate the variable significance and then get a model that could predict new responses over designed experiment. On the ANN were applied the Profile Method on a supervised Multilayer Perceptron optimized with the Levenberg-Marquardt algorithm. The results showed good agreement with a confidence level of 90%, that the axial depth of cut, feed per tooth and cutting speed in this order were the most significant variables. This is in accordance with the literature and can open more applications using the ANN approach to obtain a variable significance over other systems.