Model Predictive Control in Milling based on Support Vector Machines

Abstract

Today's manufacturing systems are either optimized for flexible or individualized manufacturing. The machine operator determines the optimal setup for the machine variables that are accurately implemented by the machine controllers. However, the overall objective is productivity under the restriction of product quality, where a model-based predictive controller is used to rather control the process than machine settings. This approach requires an accurate model of the dynamic behavior of the machine tool. Therefore, the Support Vector Machines algorithm is applied to identify and model the dynamic behavior under unknown nonlinearities. This model is compared to a classical modeling approach to predict the future system behavior in a model-based predictive controller. Based on the prediction, an optimization problem is solved in order to determine an optimal feed velocity. The presented approach outperforms the previous control strategy with 15 % shorter manufacturing time. Although the identified nonlinear SVM model is far more accurate for the analyzed system than previous models, further research has to be conducted regarding the application within a model predictive control strategy.