Milling force prediction using regression and neural networks

This study focuses on developing a good empirical relationship between the cutting force in an end milling operation and the cutting parameters such as speed, feed and depth-of-cut, by using both multiple regression and neural network modeling processes. A regression model was first fitted to experimentally collected data and any abnormal data points indicated by this analysis were filtered out. By repeating this process several times, a final set of filtered data was obtained and analyzed using neural networks to yield a good, final model. This study shows that analyzing milling force data using conventional regression can lead to a more accurate neural networks model for force prediction.