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- Conflict of Interest
- In relation to this article, we declare that there is no conflict of interest.
- Publication history
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Received January 24, 2023
Revised January 24, 2023
Accepted May 23, 2023
- Acknowledgements
- This work was supported by Korea Institute for Advancement of Technology (KIAT) through the Virtual Engineering Platform of Virtual Test, Data, and AI for Chemical Materials project (P0022334) and the Smart Digital Engineering Education and Training for Lead Engineer project (P0008475) funded by the Ministry of Trade, Industry and Energy (MOTIE).
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An LSTM model with optimal feature selection for predictions of tensile behavior and tensile failure of polymer matrix composites
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Abstract
Mechanical properties such as tensile strength, ductility, and tensile modulus are essential criteria in polymer matrix composites (PMC) design and are determined through the stress-strain curve obtained from the tensile
test. Material designers can examine the stress-strain curve trends based on the combination and composition, but it is
difficult to predict using numerical analysis software due to the complex correlation based on chemical properties. To
address these limitations in PMC design, this study uses feature engineering methods such as principal component
analysis (PCA) and recursive feature elimination with cross validation (RFECV) to find the minimal and optimal set of
features necessary for predicting the tensile behavior of PMC. The Long Short-Term Memory (LSTM) and feedforward neural network (FNN) models are trained using the optimal feature set and 1,270 PMC’s tensile test data to predict the tensile stress-strain curve. The predictive model developed in this study provides stress-strain curves of tensile
tests, including tensile failure of PMC, which can be challenging due to the high nonlinearity of PMC. Material designers can reduce the time and labor costs of PMC design through this tensile behavior prediction model that has an
accuracy of R2
=92% and requires fewer features. In addition, the model can be used as a high-throughput screening
model for PMC inverse design systems.
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