A sparse knowledge embedded configuration optimization method for robotic machining system toward improving machining quality

In recent years, robotic machining has become one of the most important paradigms for the machining of large and complex parts due to the advantages of large workspaces and flexible configurations. However, different configurations will correspond to very different system performances, influenced by the position-dependent properties. Therefore, the configuration optimization of robotic machining system is the key to ensure the quality of robotic operation. In response to the fact that little attention has been paid in current research to the effect of mapping model distribution differences on the optimization results, a sparse knowledge embedded configuration optimization method for robotic machining systems toward improving machining quality is proposed. The knowledge of theoretical model-based optimization in terms of stage, density and redundancy is embedded into high-fidelity data by three steps sparse and real measurement. Pre-training and domain adaptation fine-tuning strategies are used to reconstruct the real mapping model accurately. The reconstructed mapping model is re-optimized to obtain a more accurate system configuration. The effectiveness of the proposed method is verified by machining experiments on space segment parts. The proposed method reduces the absolute position error and machining error by 48.67 % and 28.73 %, respectively, compared to the current common theoretical model-based optimization. This is significant for more accurate and reliable robot system optimization. Furthermore, this work confirms the influence of mapping model distribution differences on the optimization effect, providing a new and effective perspective for subsequent research on the optimization of robotic machining system configurations.