An Adaptive Sampling Strategy for Surface Measurement of Complex Curved Workpieces

Abstract

Large complex workpieces are the core components in aerospace, shipbuilding, transportation and other fields. Such components usually have large size, complex shape characteristics and poor stiffness of workpieces, such as large common bottom components of launch vehicles, rocket panels, aircraft radomes, etc. When machining such high-performance parts, it is usually necessary to copy the parts according to the task requirements. The integration technology of complex surface measurement and machining is the organic combination of digital measurement technology and machining technology and is an effective means to ensure the high-precision copying machining of large complex workpieces. Data acquisition technology is the most critical part. For the data acquisition of some large and complex workpieces, laser scanning measurement technology with high reliability and local accuracy is usually used. In the process of data acquisition, the measuring track and the measuring point interval are planned according to the structural characteristics of the workpiece. The quality of the measurement data will directly affect the accuracy and overall quality of the follow-up profiling processing. The two most important factors in curved surface measurement are the scale of sampling quantity and the distribution of sampling points [1]. In the point cloud model, the larger the number of sampling points, the more appearance features are included, and the more accurate the 3D reconstruction results are [2]. However, the sampling scale is directly proportional to the time of data acquisition. An excessive sampling scale will lead to an increase of the measurement time cost, overload of data processing and the increase of overall product manufacturing cost. Therefore, the research on intelligent sampling point planning of complex surfaces is of great significance.