An Intelligent Measurement Framework for Aero-Engine Blades

Aero-engines are composed of hundreds of precisely manufactured components, among which aero-engine blades play a critical role in energy conversion and mechanical power transmission. Ensuring their dimensional accuracy is essential for maintaining performance under extreme operating conditions such as high speed and elevated temperatures. However, certain hard-to-access features remain difficult to measure using conventional measurement techniques. To address this challenge, this paper proposes an AI-assisted hybrid measurement framework for the inspection of such complex features. The framework integrates high-precision optical metrology systems with intelligent data processing algorithms to enable accurate dimensional analysis of inaccessible regions. A case study on turbine blade inspection is presented to validate the feasibility of the proposed approach. Results demonstrate that the combination of advanced hardware and AI-based algorithms can significantly enhance the measurement accuracy of hard-to-reach features, thereby supporting automated, high-fidelity quality control in aerospace manufacturing.