An extended LuGre model for estimating nonlinear frictions in feed drive systems of machine tools

Xi, Tiandong; Fujita, Tomoya; Kehne, Sebastian; Ikeda, Ryosuke; Fey, Marcel; Brecher, Christian

doi:10.1016/j.procir.2022.05.007

Cited by 9 publications

(1 citation statement)

References 12 publications

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“…[28][29][30]. XI et al, for example, used the LuGre model and extended it by a look-up table to account for positional effects [31]. However, the more generic solution is modeling the positional effect and including it into the frictional model.…”

Section: State-of-the-artmentioning

confidence: 99%

A New Grey Box Approach for Friction Modelling of Machine Tool Drives

Rüppel,

Meurer,

Bergs

2024

Journal of Machine Engineering

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Measurement of the process force in milling is usually conducted by using piezo-electric dynamometers which are costly and reduce the stiffness of the system. A less invasive alternative is an indirect estimation of cutting forces based on the power of the servo drives. However, a correction of frictional effects from the transmission system is necessary to achieve accurate results. Most machine tools are equipped with ball-screw drives whose friction behavior is highly nonlinear due to dependency on both velocity and position. This study provides a novel approach to consider all frictional and inertial effects in transmission behavior of ball-screw drives by utilizing the wellestablished generalized MAXWELL slip (GMS) model and combines it with a data-based approach, namely support vector regression (SVR). The approach acquires the internal states of the GMS model and uses them as an additionnal input for the SVR. The model is validated on different experiments conducted on a five-axis machining center and compared to established friction models, as well as a sole SVR. The results show the model to have errors between 7% and 12% over the full working range of the x-and y-axes, respectively, outperforming the benchmark models significantly.

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