About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
|
Presentation Title |
Interpretation of Convolutional Neural Networks for Predicting Volume Requirements in Studies of Microstructurally Small Cracks |
Author(s) |
Karen Demille, Ashley Spear |
On-Site Speaker (Planned) |
Karen Demille |
Abstract Scope |
Previously, representative volume elements for microstructurally small cracks (RVEMSC) were established using a finite-element (FE) framework. The process of establishing RVEMSC values—which indicate the minimum volume requirements for studies involving microstructurally small cracks (MSCs)—required thousands of 3D FE simulations. In this work, convolutional neural networks (CNNs) are deployed in response to the prohibitive computational expense of determining RVEMSC values strictly via FE simulations. CNNs are trained to predict RVEMSC values given 3D descriptions of microstructural and geometrical features near points along the crack front. In addition to providing RVEMSC predictions, the CNNs provide insight into the influence of microstructure on RVEMSC values: CNN input sensitivity studies compare the relative importance of microstructural and geometrical features on RVEMSC predictions, and saliency maps highlight regions of microstructure most important to RVEMSC value predictions. Through interpretation of CNN models, the link between RVEMSC requirements and microstructural neighborhoods is clarified. |
Proceedings Inclusion? |
Undecided |