Performance Improvement in Robotic Milling: Hybrid Compliance Error Compensation and Fast-Chirp Identification Approach

Industrial robots have been used in machining over the past two decades due to advantages such as lower cost and a greater ability to machine large parts and sculpted geometries compared to CNC machine tools. However, their low stiffness causes deflection and vibration during high-force tasks, which limits their application in machining. This study aims to address these challenges by developing a dynamic model of industrial robots. A dynamic model incorporating joint tri-axial flexibility is developed. The robot’s rigid-body model is identified, and a new friction model is proposed to reduce the identification error. The limitations of the physics-based model, with a focus on friction nonlinearities, are investigated. To further reduce the identification error and accurately predict joint torques, a new hybrid torque estimator is introduced. The estimator includes a data-driven scheme based on a recursive linear regression method that uses the output of the identified model along with the joint motion data. This hybrid approach reduces the joints torque estimation error by up to 55 percent. The torque estimator is incorporated into a nonlinear disturbance observer to predict the external torques applied to the joints due to cutting forces in real time, without using any external sensors. The compliance error is calculated from the estimated external torques and the identified joint stiffness, and compensated in real time using a compliance error compensation method. Multi-axis aluminum milling experiments show that the average TCP deflection decreases by 78 percent in the x direction and 77 percent in the y direction. Another contribution of this study is introducing a novel automated approach, termed “fast chirp”, for identifying the joints dynamic parameters, specifically stiffness and damping, in moving robots. A chirp centrifugal force generated by a customized offset mass tool is utilized for two-directional excitation of the robot, providing full control over the excitation within a desired frequency range, and eliminating the need for manual tests. To capture the moving-joints frictional behavior, the robot moves continuously along an identification trajectory which can cover a large portion of workspace in a single experiment, while excitation is applied in multiple zones. A new formulation is developed to calculate vibration responses under simultaneous two-directional excitation, using direct and cross FRFs. This formulation is experimentally validated, and static and quasi-static conditions are compared. The importance of cross-coupling terms and joints tri-axial flexibility assumption in industrial robots is also investigated. The dynamic model and measured vibration responses are used to identify joint stiffness and damping parameters. The identified model is validated and subsequently employed to generate chatter stability lobe diagrams. The accuracy of predicted SLDs is validated through milling chatter experiments. The effect of incorporating the indirect vibration response in the local z direction, which cannot be directly excited using the fast chirp approach, on predicting z-direction dynamics is formulated. Experimental results show that including the z-direction response in the fast chirp identification approach improves the accuracy of the identified model in predicting z-direction dynamics. The identified model is used to predict and avoid chatter in machining applications.