|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Convolutional Neural Networks to Expedite Predictions of Volume Requirements in Studies of Microstructurally Small Cracks
||Karen Demille, Ashley Spear
|On-Site Speaker (Planned)
In a previous study, representative volume elements for microstructurally small cracks (RVEMSC) were established for heterogeneous, linear-elastic materials via a finite element (FE)-based framework. The framework involved the simulation of various heterogeneous microstructures and proved to be prohibitively expensive for applying to more complex material systems. Despite important outcomes from the study, two major shortcomings of the framework were the large number of required FE simulations and the inefficient, brute-force approach of selecting microstructures to simulate. In this work, convolutional neural networks (CNNs) are harnessed to address the FE-based framework’s shortcomings and expedite the determination of RVEMSC. As compared to the previous brute-force selection of microstructures, CNNs provide a more informed selection of microstructural configurations that are critical with respect to RVEMSC size, thereby improving the efficiency of determining RVEMSC in future studies.
||Machine Learning, Other,