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 |
Accelerating Microstructurally Small Crack Growth Predictions in Three-dimensional Microstructures Using Deep Learning |
Author(s) |
Vignesh Babu Rao, Brian Phung, Bjorn Johnsson, Ashley Spear |
On-Site Speaker (Planned) |
Brian Phung |
Abstract Scope |
The ability to rapidly predict the growth behavior of microstructurally small cracks (MSCs) has the potential to significantly advance fracture-based designs and structural prognosis. The experimental and numerical difficulties associated with characterizing or predicting MSC growth preclude the applicability of such techniques in industrial design approaches, despite their potential benefits. Here, we propose a framework to accelerate the MSC growth predictions using deep-learning algorithms such as convolutional neural networks (CNNs). The primary research aim is to train CNNs to predict the rules governing MSC growth and to subsequently apply them to make rapid forward predictions of local crack extension given microstructural neighborhood information along a crack front. The training data are acquired from a large number of “virtual” MSC growth observations enabled by high-fidelity finite-element-based simulations. The MSC-growth-simulation framework, data-extraction strategies, and application of deep-learning algorithms for data-driven model development will be presented, and the resulting advantages will be demonstrated. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, Machine Learning, Modeling and Simulation |