Point Cloud Data Fusion for High-Precision Inspection of Aerospace Parts

This project aims to develop an innovative method to enhance the accuracy and efficiency of inspecting aerospace parts, such as aero-engine blades. These components often have intricate shapes, making precise inspection a significant challenge. To overcome this, the project combines data from two measurement systems: one that captures detailed 3D scans and another that provides sparse but highly accurate reference points. By intelligently merging these datasets using adaptive sampling and advanced algorithms, we create highly accurate digital models of the parts. The research will enable faster and more precise part inspection, reducing production bottlenecks and costs. By advancing inspection techniques, this project will directly support the competitiveness of the Canadian aerospace manufacturing industry, helping it maintain a leadership position in producing high-quality, reliable components for global markets.

Faculty Supervisor:

Farbod Khameneifar;René Mayer

Student:

Partner:

Pratt & Whitney Canada

Discipline:

Engineering

Sector:

Mining

University:

Polytechnique Montréal

Program: