About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
A Graph Based Workflow for Extracting Grain-Scale Toughness from Meso-Scale Experiments. |
Author(s) |
Stylianos Tsopanidis, Shmuel Osovski |
On-Site Speaker (Planned) |
Stylianos Tsopanidis |
Abstract Scope |
Extracting the micro-scale material toughness is very challenging. This information is only accessible through delicate experiments, where the overall sampled volume and the experimental complexity limit the statistical assessment of the results and thus their validity. We introduce a novel machine learning computational framework that aims to compute the micro-scale material toughness, after a short training process on a limited meso-scale experimental dataset. This framework relies on the ability of a graph neural network to perform high accuracy predictions of the micro-scale material toughness, utilizing a limited size dataset. The merit of the proposed framework arises from the capacity to enhance its performance in different material systems with limited additional training on data obtained from experiments that do not require complex measurements. We demonstrate the algorithm’s high efficiency in predicting the crack growth resistance in micro-scale level, using a crack path trajectory limited to 200-300 grains for the network’s training. |
Proceedings Inclusion? |
Undecided |