A framework for adaptive reconstruction of aero-engine blade surfaces based on geometric features

Abstract The coordinate measuring machine (CMM) is commonly used to measure aero-engine blades with precision, and is an important tool for ensuring blade quality. Research on blade surface reconstruction methods has been highly regarded since the accuracy of surface reconstruction directly impacts manufacturing quality assessment. However, when addressing sparse CMM sampling points, mainstream point-cloud-based surface reconstruction methods frequently fail to achieve ideal reconstruction accuracy. This work proposes a framework for blade surface reconstruction based on geometric features, resulting in precise reconstruction in three steps. Firstly, adaptive sampling planning models are developed based on span theory, which consider local distortions and curvature variations. Secondly, to handle the unordered distribution of measurement points, a method of adaptive sorting combining angle and distance constraints is proposed. Finally, by matching measurement points across different cross-sections, a non-uniform rational B-spline reconstruction (NURBS) model is constructed. Using two blade examples, the proposed framework is demonstrated to be effective. Compared to the state-of-the-art method, the maximum error between the reconstructed surface and the design surface is 0.0235 mm.