Geometric error self-calibration method of five-axis dispensing machine based on the product of exponentials formula

Abstract

Geometric error identification and kinematic calibration are common and effective methods for improving the machining accuracy of five-axis machines. Compared with the traditional measurement methods characterized by complicated inspection processes and high cost, this paper proposes a self-calibration method for a five-axis dispensing machine based on monocular vision and the product of the exponentials formula. The proposed method can automatically identify geometric errors and complete kinematic calibration, even for simultaneous five-axis motion. First, a ChArUco marker-based pose detection system is designed to achieve a wide range of calibration space for a five-axis dispensing machine. Based on this, the proposed sub-pixel checkpoint detection framework has higher pose detection performance accuracy at different depths of the field. Then, a general kinematic error model of five-axis machines is established using screw theory to avoid singularities. Moreover, the identification method of its geometric error is proposed for an RRTTT-type five-axis dispensing machine. Finally, an arc-shaped dispensing trajectory for the 3D glasses' inner wall is used for experimental validation. The experimental results show that the average orientation quantization error decreases from 0.0112 rad to 0.0039 rad. In contrast, the average position error decreases from 0.1627 mm to 0.0473 mm before and after the self-calibration, respectively. Thus, the convenience, effectiveness, and accuracy of the self-calibration method are validated on this five-axis dispensing machine.