An adaptive sampling strategy for aero-engine blade surface based on the momentum conservation principle

The spatial distribution and density of inspection points are critical factors influencing the accuracy and efficiency of aero-engine blade quality assessment. This study proposes an adaptive sampling methodology to optimize measurement precision and operational efficiency for blade surface inspections through dynamic adjustment of sampling point locations and quantities based on geometric feature variations. A parametric blade model is established using non-uniform rational B-splines for geometric representation, with reconstruction accuracy evaluated through median Hausdorff distance metrics. An innovative adaptive sampling strategy based on momentum conservation principle is developed, where multi-feature parameters (including curvature, torsion, chord height, and arc length) are incorporated as mass terms to characterize blade geometry. Velocity terms are introduced to dynamically balance the influence weights of different geometric features during the sampling process. Through three comparative experiments, the proposed method demonstrates superior performance compared to conventional approaches including equal arc length sampling, chordal tolerance method, modified equal chord height sampling, equal moment theory sampling, and bending moment theory sampling. Results indicate that under equivalent sampling point quantities, this methodology achieves the minimum deviation between reconstructed and actual blade surfaces.