Stiffness performance index based posture and feed orientation optimization in robotic milling process. (February 2019) - Chen, Chen; Peng, Fangyu; Yan, Rong; Li, Yuting; Wei, Dequan; Fan, Zheng; Tang, Xiaowei; Zhu, Zerun
Highlights: A task-dependent performance indexes is proposed to evaluate industrial robot stiffness. Based on stiffness performance index, the posture and tool feed orientation are optimized during milling application. Higher machining accuracy is achieved through proposed optimization models. Properties of robot compliance in Cartesian space are analyzed. Milling experiments validated the feasibility and effectiveness of the proposed optimization methods. Abstract: Industrial robots are promising and competitive alternatives for performing machining operations. A relatively low stiffness is the major constraint for the widespread use of industrial robots in machining applications. In this study, the stiffness properties of an industrial robot are analyzed to improve the machining accuracy of robotic milling, and optimization methods for the robot posture and tool feed orientation are established. First, based on the relationship between the external force and deformation of the robot end effector (EE), the normal stiffness performance index (NSPI) of the surface, which is derived from the comprehensive stiffness performance index (CSPI), is proposed to evaluate the robot stiffness performance for a given posture. The NSPI is proven to be independent of the magnitudes of the external forces and dependent on the directions of these forces. A distribution rule is then proposed for the NSPI with respect to any direction in the Cartesian space for a given posture, which clearly reveals the anisotropic property of the robot stiffness. By maximizing the NSPI, an optimization model is established to optimize the posture of a six degree-of-freedom (DOF) industrial robot in a milling application. Using the NSPI, the optimized tool feed orientation for robot planar milling is obtained. Finally, the results of the robot milling experiments are discussed to illustrate the feasibility and effectiveness of the proposed optimization methods.