Support vector regression to correct motor current of machine tool drives

Nonlinear friction is the limiting factor in using motor current signals to estimate the load of machine tools. The inertia of the axis and the positional dependency of the friction add another degree of complexity. The work focuses on industrial machining centers with ball-screw driven stages as they are used in metal cutting. The approach uses Internal low-frequency signals from the NC controller to keep the barriers for an industrial application at a minimum. The contribution of this study is twofold: First, it extends conventional analytic friction models so that they incorporate positional dependency of friction, as well as the contribution of the inertia of the axis. Second, it proposes how to model the both effects jointly through support vector regression. This data-driven model outperforms the extended Stribeck and the generalized Maxwell-slip friction models, which serve as a representative benchmark for static and dynamic friction models respectively. However, this comes with the need for a careful selection of the data, on which the support vector machine is trained, in order to obtain an accurate and general model.