Grants and Contributions:

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Title:
Microstructure reconstruction for data augmentation and study of process-structure-property relationships with deep learning models.
Agreement Number:
1014106
Agreement Value:
$99,996.00
Agreement Date:
Mar 21, 2024 - Mar 31, 2026
Description:
The project aims to increase metallographic laboratory autonomy and facilitate the development and optimization of processes, such as high-pressure vacuum casting (HPVDC) of aluminum, cold spraying of aluminum (CSAM) and steel made by selective laser melting (SLM), by applying generative artificial intelligence (AI) models. The project will focus first on increasing efficiency by quickly and cost-effectively generating machine learning datasets with adjustable features, making it possible to generate aluminum and steel microstructures with variations in process parameters and sample preparation conditions. Next, the application of models trained on generated images to increase the efficiency and autonomy of metallographic laboratories will be investigated, overcoming the scarcity of out-of-distribution image data by reconstruction using generative AI models. Finally, the use of AI models to reconstruct microstructure images and extract new features will be explored, with the aim of improving the prediction of mechanical properties. These developments could eventually lead to a product implemented by companies specializing in computer vision.
Organization:
National Research Council Canada
Expected Results:

In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.

Location:
Rimouski, Quebec, CA G5L 3A1
Reference Number:
172-2023-2024-Q4-1014106
Agreement Type:
Grant
Report Type:
Grants and Contributions
Recipient Business Number:
119278919
Recipient Type:
Academia
Recipient's Legal Name:
Université du Québec à Rimouski
Federal Riding Name:
Rimouski-Neigette–Témiscouata–Les Basques
Federal Riding Number:
24018
Program:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:

Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.

NAICS Code:
541710 - R&D in the physical, engineering and life sciences