About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
M-12: Scaling Microstructure-dependent Mechanical Properties to Bulk Material Properties Using 3D Convolutional Neural Networks |
Author(s) |
Laura Z. Vietz, Carter K. Cocke, Ashley D. Spear |
On-Site Speaker (Planned) |
Laura Z. Vietz |
Abstract Scope |
Experimentally characterizing bulk mechanical behavior for certain materials, including nuclear materials used in harsh operating environments, can be challenging and cost prohibitive. In such cases, characterization efforts might be limited to small-scale testing, which can exhibit size effects for sample sizes below that of a representative volume element (RVE), or the smallest volume of material above which a property of interest converges to that of bulk material. Volumes smaller than the RVE, called statistical volume elements (SVEs), exhibit scattered responses. This research aims to link microstructure-dependent SVE-derived properties to bulk material properties by coupling high-throughput numerical simulation with machine learning. SVE microstructures are simulated using an elasto-viscoplastic fast Fourier transform code. Three-dimensional images of the SVE microstructures and their corresponding mechanical responses are used to train a convolutional neural network to predict bulk mechanical response. This research could enable cost-effective methods for characterizing bulk materials by testing micro/nanoscale specimens. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Characterization, Machine Learning |