TY - JOUR
T1 - Estimation of spatial uncertainty in material property distributions within heterogeneous structures using optimized convolutional neural networks
AU - Pitz, Emil
AU - Rooney, Sean
AU - Pochiraju, Kishore
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2023/1
Y1 - 2023/1
N2 - Variations in material behaviors within structures built with heterogeneous materials lead to damage initiation and evolution in locally weak regions. Quantifying the property variability within the structure and forward propagation of the impact of the material property uncertainty on the structural response is critical for reliability analysis and structural performance maximization. Commonly, quantification of the variability requires either computationally expensive high-fidelity models of the underlying microstructure or extensive experimental testing. In this paper, we model the uncertainty with spatially correlated random fields and calibrate the model parameters from limited strain field observations using Neural Networks (NNs). The calibration is performed by trained NNs, which outputs best-fit parameters for the spatial correlation model by accepting filtered Digital Image Correlation (DIC) strain distributions as the input. We demonstrate that by training the NNs using simulated data, the resulting networks are able to calibrate the spatial distribution uncertainty models effectively for a set of Fused Filament Fabrication (FFF) printed structures. The methodology requires a limited number of experimental datasets and produces fast estimations of the best-fit parameters of the uncertainty model compared to optimization or inverse fitting methods. This method allows experimentalists to use the same DIC information routinely obtained during modulus or strength testing to calibrate a spatial property distribution uncertainty model for the underlying microstructure.
AB - Variations in material behaviors within structures built with heterogeneous materials lead to damage initiation and evolution in locally weak regions. Quantifying the property variability within the structure and forward propagation of the impact of the material property uncertainty on the structural response is critical for reliability analysis and structural performance maximization. Commonly, quantification of the variability requires either computationally expensive high-fidelity models of the underlying microstructure or extensive experimental testing. In this paper, we model the uncertainty with spatially correlated random fields and calibrate the model parameters from limited strain field observations using Neural Networks (NNs). The calibration is performed by trained NNs, which outputs best-fit parameters for the spatial correlation model by accepting filtered Digital Image Correlation (DIC) strain distributions as the input. We demonstrate that by training the NNs using simulated data, the resulting networks are able to calibrate the spatial distribution uncertainty models effectively for a set of Fused Filament Fabrication (FFF) printed structures. The methodology requires a limited number of experimental datasets and produces fast estimations of the best-fit parameters of the uncertainty model compared to optimization or inverse fitting methods. This method allows experimentalists to use the same DIC information routinely obtained during modulus or strength testing to calibrate a spatial property distribution uncertainty model for the underlying microstructure.
KW - AI-based calibration
KW - Digital image correlation
KW - Karhunen–Loève expansion
KW - Neural network architecture search
KW - Uncertainty quantification
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U2 - 10.1016/j.engappai.2022.105603
DO - 10.1016/j.engappai.2022.105603
M3 - Article
AN - SCOPUS:85142161972
SN - 0952-1976
VL - 117
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 105603
ER -