Advanced Tolerance Optimization for Freeform Geometries Using Particle Swarm Optimization: A Case Study on Aeronautical Turbine Blades

Abstract

This study introduces a novel approach to optimizing geometric tolerances on freeform surfaces, specifically turbine blades, by leveraging a global tolerance framework. Unlike traditional methods that rely on multiple local tolerances, this research proposes a unified model to streamline design complexity while maintaining functional integrity and cost efficiency. A turbine blade, reconstructed from 3D-scanned point cloud data, serves as the basis for this investigation. The reconstructed geometry was analyzed to define deviation distributions, followed by the application of a global tolerance model. Using genetic algorithms, the tolerances were optimized to balance manufacturing costs and performance penalties. Results demonstrate a substantial simplification in quality control processes, with a reduction in manufacturing costs by up to 20%, while preserving aerodynamic and structural performance. The study highlights the potential of global tolerance strategies to transform tolerance allocation in industries such as aerospace and energy, where freeform surfaces are prevalent. The integration of optimization techniques and advanced surface analysis offers a forward-looking perspective on enhancing manufacturing precision and efficiency.