Feature fusion and distillation embedded sparse Bayesian learning model for in-situ foreknowledge of robotic machining errors

In recent years, robotic machining of complex surface has become a research hotspot for intelligent manufacture. Considering the weak rigidity characteristics of industrial robot structure, the machining errors will be generated during the robotic machining process, which will further induce the undesirable impact on machining quality and production cycles. Therefore, this work proposes an in-situ foreknowledge model for robotic machining errors of complex curved parts. The static and dynamic features during the robotic manufacture process are obtained, where the robots’ stiffness concerning end effect operator is extracted as the static features, as well as the dynamic features of the monitored dynamic cutting forces. Furthermore, the structured fusion features are utilized as the dataset to train the foreknowledge model based on sparse Bayesian learning method. In order to adapt to the rapid response in the industrial field, the principal component analysis strategy is adopted to reduce feature dimension, by which the complexity of fusion features is reduced by 50.51 %. In addition, based on the adjustment of sparse weights, the complexity of the sparse Bayesian foreknowledge model is reduced by 74.31 %. According to the validation experiment on the blade shaped workpiece of marine propeller, the results indicate that the prediction accuracy of the proposed in-situ foreknowledge model is in the range of 60.03–87.07 µm, meeting the engineering requirements of robotic machining errors. The proposed in-situ foreknowledge sparse model displays the potential prospects for robotic machining errors prediction and control in the industrial application field.