About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Assessing the Robustness of an EBSD-Data-Based U-Net Model to Classify Phase Transformation Products in Steels |
Author(s) |
Tomas Martinez Ostormujof, Simon Breumier, Nathalie Gey, Mathieu Salib, Lionel Germain |
On-Site Speaker (Planned) |
Tomas Martinez Ostormujof |
Abstract Scope |
Quantitative characterization of complex steels microstructures requires a considerable amount of time, effort and expertise. To address this issue, we propose to couple Artificial Intelligence techniques with Electron Backscattering Diffraction (EBSD) to improve and simplify the task.
We have trained a supervised Deep Learning model capable of classifying and quantifying Dual Phase (DP) steel microstructures based on a semantic segmentation strategy using Convolutional Neural Networks and the UNET architecture. DP steel microstructures were classified in seconds with an accuracy of ~98%.
In this contribution, we assess the robustness of our model against the variability of the input (influence of sample preparation and EBSD acquisition set-up). Additionally, we discuss the amount of data needed to train an accurate model, including the contribution of simulated EBSD microstructures and data augmentation. Finally, we demonstrate the applicability of our model to analyze more complex microstructures including Widmanstätten ferrite and Bainite. |
Proceedings Inclusion? |
Undecided |