A vision-based error identification method for separating geometric errors of rotating axes in five-axis platforms. (December 2022) - Yin, Song; Zhou, Haibo
Highlights: Introducing the coordinate search into the identification of orientation errors. Developing a sample regression model to identify position deviations. Proposing an accuracy evaluation criterion for the presented identification models. Abstract: In this paper, a programmable identification method for visually measuring and identifying position-independent and position-dependent geometric errors (PIGEs and PDGEs) of rotary axes in compact five-axis platforms is presented. Firstly, the kinematic model of the five-axis platform is established via screw algebraic theory. Then, considering the measurement data diversity, the orientation errors identification mechanism for PIGEs is proposed. The mechanism optimally integrates the coordinate search and the least-squares method. Additionally, a least-squares sample regression model is developed to identify the positional deviation of the rotation axis. After identifying PIGEs, the PDGE sources are identified based on the right multiplication principle. Furthermore, the error estimation accuracy of the proposed method is investigated. Finally, a five-axis dispensing platform is employed to validate the identification accuracy and evaluate its feasibility.