A metrological framework for fixture-induced uncertainty quantification in dimensional inspection using bootstrap-validated genetic algorithm optimization

Precision machining of turbine blade cooling channels presents significant challenges in aerospace manufacturing, where micron-level accuracy directly impacts engine performance. This research develops an intelligent fixture optimization system integrating genetic algorithms (GA) with Plücker coordinate mathematics to achieve optimal locator configurations. The methodology maximizes the determinant of the Plückerian location matrix, establishing a stability-optimized reference frame at the blade root while maintaining compatibility with industrial machining processes. Experimental validation on complex turbine airfoil geometries demonstrates a 32 % improvement in positional accuracy (reducing errors from 0.25 mm to 0.17 mm) compared to conventional fixturing methods, verified through coordinate measuring machine (CMM) inspection and destructive cross-section analysis. The optimized design addresses critical production requirements including: (1) enhanced operator visibility for contact verification, (2) improved gauge shim compatibility, and (3) maintained thermal stability during electrochemical drilling operations. This work provides manufacturers with a computationally efficient framework that significantly reduces fixture development time while achieving aerospace-grade precision. The demonstrated success on turbine blade geometries suggests immediate applicability for aeroengine components and adaptability to other complex machining applications in the energy sector.