A machine learning-driven approach to predict mechanical degradation associated with matrix cracks in fiber-reinforced composite laminates
- DOI
- 10.1016/j.nxmate.2025.101209
- Published
- 2025-10
- Container
- Next Materials
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.nxmate.2025.101209,
title = {A machine learning-driven approach to predict mechanical degradation associated with matrix cracks in fiber-reinforced composite laminates},
author = {M.J. Mohammad Fikry and Jason P. Mack and Faizan Mirza and Niken Prasasti Martono and K.T. Tan and Vladimir Vinogradov and Shinji Ogihara},
year = {2025},
journal = {Next Materials},
doi = {10.1016/j.nxmate.2025.101209},
url = {https://doi.org/10.1016/j.nxmate.2025.101209}
}RIS
TY - JOUR TI - A machine learning-driven approach to predict mechanical degradation associated with matrix cracks in fiber-reinforced composite laminates AU - M.J. Mohammad Fikry AU - Jason P. Mack AU - Faizan Mirza AU - Niken Prasasti Martono AU - K.T. Tan AU - Vladimir Vinogradov AU - Shinji Ogihara PY - 2025 JO - Next Materials DO - 10.1016/j.nxmate.2025.101209 UR - https://doi.org/10.1016/j.nxmate.2025.101209 ER -
APA
Fikry, M. M., Mack, J. P., Mirza, F., Martono, N. P., Tan, K., Vinogradov, V., & Ogihara, S. (2025). A machine learning-driven approach to predict mechanical degradation associated with matrix cracks in fiber-reinforced composite laminates. Next Materials. https://doi.org/10.1016/j.nxmate.2025.101209
Source records
- crossref · retrieved 2026-09-26T14:56:07.453Z