Tool axis vector optimization for robotic grinding based on measured point cloud of complex curved blade

Ziling Wang, Lai Zou, Junjie Zhang, Heng Li, Wenxi Wang, Yun Huang

Abstract: The tool axis vectors of the path points in the model are prone to mutation because of the curvature characteristic of the measured model of the complex curved blade. It may cause unsteady robotic joint motion and further deteriorate the blade’s machining quality. In order to solve this issue, a strategy for optimizing the tool axis vectors is proposed that will smooth the tool axis vectors dispersed at every complete cross-sectional contour of the blade’s measured point cloud. This approach builds a tool axis smoothing algorithm on top of the surface energy model (TASE). Compared to other typical smoothing methods, TASE improves the tool axis vectors’ smoothness by more than 22%. Furthermore, the profile smoothness with TASE is improved by more than 27% than that with these typical algorithms. In order to generate the uniform robotic joint-motion, a tool axis iteration algorithm (TAI) is further proposed for the smoothed tool axis vectors with TASE at the blade edges. The smoothness of robotic joint-motion with TAI at the blade edges is improved by over 60% than that without TAI. The curve smoothness with TAI is improved by over 12% than that without TAI.