Document Type
Article
Author ORCID Identifier
Juhyeong Lee https://orcid.org/0000-0001-9383-3869
Scott L.J. Millen https://orcid.org/0000-0002-9399-9620
Xiaodong Xu https://orcid.org/0000-0003-1732-9323
Journal/Book Title/Conference
Advanced Composite Materials
Publisher
Taylor & Francis
Publication Date
10-21-2024
Journal Article Version
Accepted Manuscript
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
First Page
1
Last Page
34
Abstract
The present study assesses the potential of using machine learning (ML) methods to predict the extent of lightning thermal damage in fiber-reinforced composite laminates using three supervised machine learning (SML) algorithms: (1) linear regression (LR), (2) decision tree (DT)-based, and (3) MLP models. These models were based on the 10 most significant factors that influence the severity of lightning damage, including three current waveform parameters, four material configurations, and three orthogonal electrical conductivities of each composite. All models demonstrated good performance with coefficient of determination (R2) values between 0.84 ~ 0.96. The multilayer perceptron (MLP) regression model trained with the lightning matrix damage dataset showed the most promising results (R2 > 0.94). Additional hyperparameter optimization was performed to improve the prediction performance of the baseline MLP model. The hyperparameter optimization (Adam optimizer, tanh activation function, and three hidden layers with 234 neurons) slightly improved the performance of the baseline MLP model by ~0.02, but achieved faster convergence. This result suggests that the baseline MLP model trained with the lightning matrix damage dataset is sufficiently accurate and robust. This paper highlights that ML-informed regression models can serve as an efficient first pass-estimator of lightning matrix damage in composite laminates, potentially reducing the amount of extremely time-consuming and expensive laboratory-scale lightning tests or streamlining the development of complex lightning damage models for future design.
Recommended Citation
Lee, J., Millen, S. L. J., & Xu, X. (2024). Critical comparison of potential machine learning methods for lightning thermal damage assessment of composite laminates. Advanced Composite Materials, 1–21. https://doi.org/10.1080/09243046.2024.2416169
