Enhancing machining allowance optimization of near-net-shape blades during manufacturing: an adaptive registration framework using curvature-based segmentation

The near-net-shape blanks of aero engine blades pose significant machining challenges due to their complex geometries and shape variations. Precise localization of the blank is crucial for optimizing the machining allowance. Traditional methods often fail to ensure the final machining surfaces are fully enveloped by the blank, leading to undercutting or overcutting. This study introduces an adaptive registration framework to address these issues. Firstly, a robust search algorithm based on curvature feature constraints is proposed to extract four segmentation points that divide the blade cross-section curve into the convex and concave sides, leading and trailing edges. Discrete points are generated on the above four parts to represent the cross-section curve data. Then, three registration models considering machining allowance and tolerance constraints of different regions are established to find the optimal position for the final machining surfaces step-by-step. Finally, The method is validated using a rotor turbine blade, reducing out-of-tolerance regions from 32.43% to 4.43%, demonstrating its effectiveness in localization and machining allowance optimization for near-net-shape blades.