Reference number : 172-2023-2024-Q4-1014106, amendment number : , agreement type : G, recipient legal name : Université du Québec à Rimouski, recipient country : CA, recipient province : QC, recipient city : Rimouski|Rimouski, recipient postal code : G5L 3A1, program name english : Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives, program name french : Programme de collaboration en science, en technologie et en innovation - Initiatives de collaboration en R-D, program purpose english : 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., program purpose french : Collaborer à des programmes multipartites de recherche-développement dans le but de profiter de la synergie entre la recherche transformatrice à haut risque et à haut rendement et le potentiel de découvertes scientifiques et de percées technologiques révolutionnaires dans des secteurs d’intervention prioritaires., agreement title english : Microstructure reconstruction for data augmentation and study of process-structure-property relationships with deep learning models., agreement title french : Reconstruction de microstructure pour augmentation des données et étude des relations procédé-structure-propriété avec des modèles d’apprentissage profond., agreement number : 1014106, agreement value : 99996.0, agreement start date : 2024-03-21, agreement end date : 2026-03-31, description english : 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., description french : Reconstruction de microstructure pour augmentation des données et étude des relations procédé-structure-propriété avec des modèles d’apprentissage profond., expected results english : 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., expected results french : À court terme, les résultats prévus seront des collaborations renforcées entre l'industrie, le milieu universitaire et le gouvernement pour soutenir l'excellence en recherche. À moyen terme, les résultats attendus seront le développement de nouvelles technologies potentiellement perturbatrices avec des collaborateurs. À long terme, trouver des solutions collaboratives aux défis des politiques publiques et créer des systèmes d'innovation plus solides., additional information english : nan, additional information french : nan, owner organization : nrc-cnrc, owner organization title : National Research Council Canada | Conseil national de recherches Canada