Predicting failure characteristics of structural materials via deep
learning based on nondestructive void topology
- URL: http://arxiv.org/abs/2205.09075v1
- Date: Tue, 17 May 2022 05:59:42 GMT
- Title: Predicting failure characteristics of structural materials via deep
learning based on nondestructive void topology
- Authors: Leslie Ching Ow Tiong, Gunjick Lee, Seok Su Sohn, Donghun Kim
- Abstract summary: We report a novel method for predicting material failure characteristics that combines nondestructive X-ray computed tomography (X-CT), persistent homology (PH), and deep multimodal learning (DML)
The method exploits the microstructural defect state at the time of material examination as an input, and outputs the failure-related properties.
- Score: 0.5735035463793008
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Accurate predictions of the failure progression of structural materials is
critical for preventing failure-induced accidents. Despite considerable
mechanics modeling-based efforts, accurate prediction remains a challenging
task in real-world environments due to unexpected damage factors and defect
evolutions. Here, we report a novel method for predicting material failure
characteristics that uniquely combines nondestructive X-ray computed tomography
(X-CT), persistent homology (PH), and deep multimodal learning (DML). The
combined method exploits the microstructural defect state at the time of
material examination as an input, and outputs the failure-related properties.
Our method is demonstrated to be effective using two types of fracture datasets
(tensile and fatigue datasets) with ferritic low alloy steel as a
representative structural material. The method achieves a mean absolute error
(MAE) of 0.09 in predicting the local strain with the tensile dataset and an
MAE of 0.14 in predicting the fracture progress with the fatigue dataset. These
high accuracies are mainly due to PH processing of the X-CT images, which
transforms complex and noisy three-dimensional X-CT images into compact
two-dimensional persistence diagrams that preserve key topological features
such as the internal void size, density, and distribution. The combined PH and
DML processing of 3D X-CT data is our unique approach enabling reliable failure
predictions at the time of material examination based on void topology
progressions, and the method can be extended to various nondestructive failure
tests for practical use.
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