Replication Data for: Learning Compensation of the State-Dependent Transmission Errors in Rack-and-Pinion Drives

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.

本数据集收录了论文《学习齿轮齿条传动系统中状态相关传输误差的补偿》中所展示的所有实验数据。齿轮齿条传动系统在大型机床中广泛应用，用于在长距离内提供重载的线性运动。在此背景下，路径精度是实现的关键考量，其受限于齿轮组件的装配和制造公差、负载相关的形变以及系统的固有间隙。为解决此问题，该论文提出了一种利用双阶段机器学习方法对传动系统的个别和状态相关传输误差进行鲁棒建模的方法。据此，位置控制被扩展以包含误差补偿，该补偿可抑制包括位置相关间隙在内的机械系统中的模型偏差。通过在配备行业标准组件的系统上进行的实验验证，评估并量化了路径精度提升的可行性和方法的鲁棒性。数据结构旨在与出版物中的图表相对应，并提供TAB或Excel格式：图2 TE测量：在变化的外部负载下，所考察的齿轮齿条传动系统在运动两个方向上的测量传输误差。图4 路径误差：无外部负载时，不同速度下计算和测量路径误差的比较。图6 模型训练：形变回归模型的训练数据和训练好的神经网络及回归树集成预测。图8 补偿验证正弦：正弦轨迹下传输误差和间隙补偿的评估。图9 补偿验证总体：评估通过补偿不同负载和速度所实现的路径精度提升。