Neural Networks for Stress Intensity Factor Vertex Prediction
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Hokaj_AIA_Paper.pdf
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Main Report
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Author(s)
Hokaj, Ian
Ghanem, Janelle
Date Issued
March 20, 2026
Abstract
Structural fatigue in aging metallic aircraft is a
primary driver of sustainment costs for the U.S. Air Force,
significantly impacting fleet readiness. Fatigue life prediction tools
like AFGROW depend on interpolating between computationally
expensive stress intensity factors (K-solutions) to approximate
unknown values. However, interpolation errors in the current
approach introduce uncertainty and force overly conservative
maintenance schedules. This paper investigates the use of a
machine learning surrogate to replace AFGROW’s dimensionreduction
interpolation for the finite-width c orner-cracked hole
geometry. We developed a robust data processing pipeline for a
large FEA dataset and trained a neural network model.
Our results reveal a critical insight: the surrogate model offers
substantial performance gains over AFGROW’s interpolation
in low-data regimes, emphasizing both the potential of the
model and its sensitivity to dataset size. For the original, sparse
dataset—which is characteristic of computationally expensive
problems—the neural network significantly o utperformed the
baseline interpolation, reducing the mean absolute percentage
error (MAPE) by over 40% (from 2.77% to 1.60%) and achieving
an R² value exceeding 0.99. However, experiments on synthetically
generated dense datasets showed that the traditional interpolation
method becomes more accurate as the data grid becomes less
sparse.
This study concludes that while neural network surrogates
offer a powerful, high-fidelity solution for computationally intensive
engineering problems, their adoption should be guided by
a careful analysis of data density after dataset has been cleaned
of outliers. It also highlights the necessity of employing rigorous,
application-relevant validation strategies that move beyond
simplistic random splits to accurately assess model performance.
Subjects
Fracture Mechanics, Surrogate Modeling, Neural Networks, Fatigue, AFGROW, Machine Learning, RANSAC.
MIT Department
Lincoln Laboratory
Persistent DSpace Link
