To address the coupled geometric and deformation errors in RV reducers, this study proposes a high-precision transmission error prediction method using Stacking ensemble learning, overcoming the limitations of rigid assumptions and inefficient finite element analysis. A finite element model incorporating machining errors is built to generate a dataset via Latin hypercube sampling. Then, based on orthogonal experiments and variance analysis methods, eight typical machining errors of the RV reducer were analyzed to identify the three machining errors that have the greatest impact on the peak-to-peak value of the transmission error. On this basis, A Stacking model is then constructed with SVR, XGBoost, RF, and KNN as base learners and XGBoost as the meta-learner. Experimental results show this model increases the determination coefficient R 2 by 14.3% and reduces RMSE by 27.4% compared to the best single model. Furthermore, it enables an average 52.48% reduction in the transmission error peak-to-peak value, significantly enhancing prediction accuracy and robustness for RV reducer performance optimization.
Wang et al. (Sun,) studied this question.