摘要叶片具有结构复杂、自由曲面多等特点。为解决叶片密集测量数据与其CAD 模型的预配准定位问题，提出六点优化定位算法进行预配准。结合六点定位原理与预配准分析技术，在榫头基准面上建立原始坐标系，对六点的主次方向进行定位误差分析配准运算，减小每个测点的误差，使CAD 模型与理论模型达到吻合状态，最后完成6个自由度的点分布，得到最终的叶片模型预配准控制点集。避免了ICP 算法对大量点云数据处理的繁琐计算，实现了批量叶片在六点优化定位后的密集测点优化预配准，且定位精度均在0.02 mm 左右。六点优化定位与典型六点定位进行配准结果对比分析表明，叶片六点优化定位算法预配准精度均在0.02 mm 左右，满足叶片模型预配准精度要求。
The blade has the characteristics of complex structure and many free-form surfaces. In order to solve the problem of preregistration of blade dense measurement data and its CAD model, a six-point optimization localization algorithm was proposed for preregistration. Combining the six-point positioning principle and dock planner must, the original coordinate system is established on the tenon reference plane, and the positioning error analysis and registration is performed on the primary and secondary direction of six-point to reduce the error of each measuring point, so that the CAD model and the theoretical model achieve consistent state. Finally, the six degrees of freedom of point distribution is completed, and the final set of blade model dock planner must control points is obtained. It avoids the tedious calculation of ICP algorithm for processing a large number of point cloud data, and realizes the dense measuring point optimization preregistration of batch blades after six-point optimization positioning, and the positioning accuracy is about 0.02 mm. The registration results of six-point optimization positioning and typical six-point positioning are compared and analyzed. The preregistration accuracy of the six-point optimization positioning algorithm for blades is about 0.02 mm, which meets the accuracy requirements of blade model preregistration.
2023, 
