CNC milling tool head imbalance prediction using computational intelligence methods

Abstract

In this paper, a mechanical imbalance prediction problem for a milling tool heads used in Computer Numerical Control (CNC) machines was studied. Four classes of the head imbalance were examined. The data set included 27334 records with 14 features in the time and frequency domains. The feature selection procedure was applied in order to extract the most significant attributes. Only 3 out of 14 attributes were selected and utilized for the representation of each signal. Seven computational intelligence methods were applied in the prediction task: K-Means clustering algorithm, probabilistic neural network, single decision tree, boosted decision trees, multilayer perceptron, radial basis function neural network and support vector machine. The accuracy, sensitivity and specificity were computed in order to asses the performance of the algorithms.