About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
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Computational Techniques for Multi-Scale Modeling in Advanced Manufacturing
|
Presentation Title |
Predicting Mechanical Performance in Additive Manufacturing Components Using Deep Learning |
Author(s) |
Kyle L. Johnson, John M. Emery, Demitri Maestas, Matthew D. Smith, Carianne Martinez, Mircea Grigoriu |
On-Site Speaker (Planned) |
Kyle L. Johnson |
Abstract Scope |
Across a range of disciplines, Deep Learning (DL) has shown tremendous success in discovering features and patterns within input data by detecting structures and hierarchies. This talk will present results of a recent effort to utilize DL algorithms to predict microstructure-dependent mechanical performance in synthetic metal coupons representative of additively manufactured material. To train the DL network, a large database of synthetic data was developed based on physical material measurements of AlSi10Mg. First, finite element meshes of tensile specimens containing voids were generated based on statistical distributions measured through high-resolution X-ray tomography. The samples were then loaded in tension, with different pore distributions leading to strain localization in different regions over a range of peak loads. The resulting data was used to train a 3D Convolutional Neural Network to predict mechanical properties in different stress states and geometries. Progress to date will be discussed, along with challenges and future work. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, Computational Materials Science & Engineering |