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 |
Modeling a Monte Carlo Potts Solidification Model Using a Generative Adversarial Network |
Author(s) |
Gregory D. Wong, Anthony D. Rollett, Gregory S. Rohrer |
On-Site Speaker (Planned) |
Gregory D. Wong |
Abstract Scope |
Machine learning can be used to reduce the order of microstructure and grain orientation modeling. These reduced order models can be used to produce many synthetic microstructures at greatly reduced computational expense. The generative adversarial network (GAN) class of neural networks is used in this work to simulate a simple Monte Carlo Potts solidification model with an end goal of modeling solidification in AM. The GAN model consists of a generator network that creates artificial images and a discriminator network that labels the produced images as belonging to a training set of images or not. Additionally, a layer of conditionality can be added to the GAN to generate images that belong to a specific labeled class. Such a class could be the material’s processing conditions, allowing for artificial microstructures to be generated based on specific processing conditions. Training and output images along with model structure will be presented. |
Proceedings Inclusion? |
Undecided |