Conference Tools for 2020 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Determination of Representative Volume Elements for Small Cracks in Heterogeneous Domains via Convolutional Neural Networks
Author(s)	Karen Demille, Ashley Spear
On-Site Speaker (Planned)	Karen Demille
Abstract Scope	Microstructurally small crack (MSC) behavior is strongly dependent on the microstructural features near the crack front. The characterization of microstructural features, through data-intensive characterization techniques such as x-ray imaging and serial sectioning, enables the relationship between MSC behavior and local microstructure to be studied. In such studies, a balance between representing enough microstructural volume and maintaining tractability must be achieved. Quantitative microstructural descriptors, used in conjunction with a convolutional neural network (CNN), can provide insights into materials characterization in terms of the minimum requisite volume for analyzing MSCs. Microstructural descriptors are obtained by sampling microstructural features at points around the crack front and using a CNN to reduce the dimensionality of the sampled features. These microstructural descriptors are used to predict convergence trends of crack-front parameters (e.g. J-integrals) with respect to heterogeneous-domain size. The convergence trends are used to inform the selection of the minimum requisite volume for analyzing MSCs.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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