Spatial–temporal feature fusion for intelligent foreknowledge of robotic machining errors

Abstract

In recent years, robotic machining has been widely noticed, especially in the manufacturing of large and complex parts, where large workspaces and flexible movements give it an even greater advantage. However, significant intrinsic errors, compliance errors due to weak stiffness of the joints, and spatially dependent nonlinear properties lead to significant challenges in high-precision machining. In this case, the dynamically changing contact area during the material removal process triggers a time-varying cutting force, which in combination with the characteristics of the robot body leads to a typical spatial–temporal coupling process that maps the error onto the workpiece. To address this process, an intelligent foreknowledge method for robot machining error with spatial–temporal feature coupling is proposed by considering the robot ontology error and the machining process. The proposed method carries out joint extraction of robot-related structured features and time-related serialized features and feature-level fusion mapping, respectively, and thus achieves accurate prediction of part machining errors. The proposed method is experimentally validated on eight inner wall workpieces of a cabin segment. Overall, the model achieved an optimal 0.026 mm RMSE on three test sub-workpieces. The ability of the proposed method to accurately extract spatial–temporal features and accurately predict machining errors is also verified through ablation experiments, parameter influence analysis experiments, and intermediate feature analysis. The proposed method takes data-driven as the core idea and spatial–temporal feature extraction as the dual perspective to achieve accurate prediction of robot machining error. It is of great significance for prediction-based accuracy compensation.