A Multi-Modal Vision Framework for Non-Destructive Evaluation of Thermal Spray Coatings

Thermal spray coatings are thin protective layers used on aircraft engines, turbines, and other high-temperature equipment to prevent damage from heat and wear. Today, checking the quality of these coatings is slow, expensive, and often destructive: samples must be cut, polished, and tested using several separate techniques. This project aims to make coating inspection faster, cheaper, and more reliable by using artificial intelligence to analyze images of the coating microstructure.
The intern will combine advanced microscopy (2D and 3D imaging) with modern machine-learning models to automatically detect pores, cracks, and other defects, and to estimate key properties such as porosity and thermal performance. The project will also explore “super-resolution” methods to enhance lower-quality industrial images, reducing the need for repeated high-resolution measurements.
The results will support smarter manufacturing and maintenance strategies in aerospace and energy sectors. This work will help Canadian partners reduce testing costs and material waste, while training a highly qualified researcher at the interface of AI and advanced materials.

Faculty Supervisor:

Moussa Tembely

Student:

Partner:

Texas A&M University at Qatar

Discipline:

Physics

Sector:

Artificial Intelligence; Aerospace; Nanotechnology

University:

Concordia University

Program: