Particle Swarm Optimization for Advanced Tolerance Control in Freeform Geometries: Case Study on Aeronautical Turbine Blades

Abstract

This research presents an innovative methodology for optimizing geometric tolerances on freeform surfaces, with a particular focus on turbine blades, through the adoption of a global tolerance framework. In contrast to conventional approaches that rely on multiple localized tolerances, the proposed method introduces a unified model aimed at reducing design complexity while ensuring both functional reliability and cost-effectiveness. The case study is based on a turbine blade reconstructed from 3D-scanned point cloud data. The reconstructed geometry was evaluated to characterize deviation distributions, which were then incorporated into a global tolerance model. Genetic algorithms were employed to optimize tolerances, striking a balance between manufacturing costs and performance requirements. The findings reveal significant improvements in quality control efficiency, achieving up to a 20% reduction in manufacturing costs while maintaining aerodynamic and structural integrity. This study underscores the potential of global tolerance strategies to revolutionize tolerance allocation in sectors such as aerospace and energy, where freeform geometries are widely used. By integrating optimization techniques with advanced surface analysis, the approach provides a forward-looking pathway toward greater manufacturing precision and efficiency.