Contour error prediction is of great significance for optimizing machining accuracy of machine tools (MTs). However, few commercial computer numerical control (CNC) systems provide continuous interpolation data which is necessary for traditional prediction methods. Instead, only sparse time series data can be collected through the un-realtime interconnection interface of CNC systems, which makes it difficult for accurate prediction. Hence, a digital twin-driven contour error prediction approach (DT-CEPA) is proposed in this study based on hybrid modeling and sparse time series. Firstly, a DT model of each feed axis in MT is constructed to describe its dynamic characteristics based on hybrid modeling method which combines parametric model (PM) and data-based model. Particularly, to adapt to the sparse time series, the eXtreme Gradient Boosting (XGBoost) which is an ensemble learning model is employed in hybrid modeling to compensate the residual error of PM. Meanwhile, a dataset with multi-source features is constructed to improve model accuracy and stability. Therefore, the actual position of feed axis can be predicted accurately with the DT model and the corresponding contour error of actual tool path can be then calculated. Secondly, a practical updating strategy for the DT model integrating incremental learning is proposed to maintain consistency between digital space and physical space, where the updating can be adaptively triggered and implemented with an appropriate scale to ensure the model accuracy while reducing updating cost. Finally, experiments and comparative analysis are preformed to validate the performance of DT-CEPA in terms of prediction accuracy and stability.

中文翻译：

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DT-CEPA：基于混合建模和稀疏时间序列的数字孪生驱动机床轮廓误差预测方法

轮廓误差预测对于优化机床（MT）的加工精度具有重要意义。然而，很少有商用计算机数控（CNC）系统提供传统预测方法所需的连续插值数据。相反，通过数控系统的非实时互连接口只能收集稀疏的时间序列数据，这使得准确预测变得困难。因此，本研究提出了一种基于混合建模和稀疏时间序列的数字孪生驱动轮廓误差预测方法（DT-CEPA）。首先，基于参数化模型（PM）和基于数据的模型相结合的混合建模方法，构建MT中各进给轴的DT模型来描述其动态特性。特别是，为了适应稀疏时间序列，混合建模中采用了集成学习模型——极限梯度提升（XGBoost）来补偿PM的残差。同时，构建了具有多源特征的数据集，以提高模型的准确性和稳定性。因此，利用DT模型可以准确预测进给轴的实际位置，并计算出相应的实际刀具路径轮廓误差。其次，提出了一种集成增量学习的DT模型实用更新策略，以保持数字空间和物理空间的一致性，可以自适应地触发并以适当的规模实施更新，以保证模型的准确性，同时降低更新成本。最后，通过实验和对比分析来验证DT-CEPA在预测准确性和稳定性方面的性能。