This dataset contains all experimental data that is shown within the paper "Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives".

Rack-and-pinion drives are commonly used in large machine tools to provide linear motion of heavy loads over long travel distances. A key concern in this context is the achievable path accuracy, which is limited by assembly and manufacturing tolerances of the gearing components in conjunction with load-dependent deformation and the inherent backlash of the system. To address this issue, this paper presents a method for robust modeling of the individual and state-dependent transmission errors of a drive utilizing a two-stage machine learning approach. Based on this, the position control is extended to include an error compensation, which suppresses the modeled deviations in the mechanical system including the position-dependent backlash. The achievable increase in path accuracy as well as the robustness of the approach are evaluated and quantified by an experimental validation on a system with industry standard components.

The data are structured to correspond to the figures in the publication and are available in TAB or Excel format:

Fig. 2 TE measurements: Measured transmission errors of the examined rack-and-pinion drive in both directions of motion under varying external load.

Fig. 4 Path errors: Comparison of calculated and measured path errors for different velocities with no external load.

Fig. 6 Model training: Training data for the deformation regression models and the predictions of the trained neural network and the regression tree ensemble.

Fig. 8 Compensation validation sine: Evaluation of the compensation of the transmission errors and backlash for a sinusoidal trajectory.

Fig. 9 Compensation validation overall: Evaluation of the improvement of the path accuracy by the compensation for varying loads and velocities.