Blade profile extraction and edge completion method based on structured light measurement point cloud

Abstract

Blade characteristic parameters evaluation plays an important role in quality monitoring and processing. In this research, a new blade profile extraction and edge completion strategy is proposed to deal with structured light measurement steam turbine blade. To handle the point cloud discretization and local data missing, the blade cross-sectional profiles are extracted adaptively at different heights, providing accurate and non-redundant data. Using NURBS and circular curves, the missing edge completion combines actual measurement curve trends and blade design geometric constraints, achieving a higher success rate and accuracy. Compared with the Perproj method and Virtual Edge method, the profile extraction accuracy of the proposed method is improved by 65 % and 36 % respectively, and the whole process of a sampled blade with additional noise is simulated to verify the effectiveness. Finally, more than two hundred blades are verified on the structured light measurement system, and the average absolute error of all parameters is less than 0.120 mm, which meets the accuracy requirements of blade quality evaluation in engineering applications.