TY - JOUR
T1 - Predicting mechanical properties of CFRP composites using data-driven models with comparative analysis
AU - Alsheghri, Ammar
AU - Alhammadi, Amna
AU - Drakonakis, Vassilis
AU - Doumanidis, Haris
AU - Barsoum, Imad
AU - Maalouf, Maher
N1 - Publisher Copyright:
© 2025 Alsheghri et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2025/4
Y1 - 2025/4
N2 - Carbon fiber reinforced polymer (CFRP) composites are increasingly utilized for their lightweight and superior mechanical properties. This study uses machine learning models to predict the mechanical properties of CFRP composites based on the volume fraction of carbon nanotubes (CNTs), interlayer volume fraction, glass transition temperature, and manufacturing pressure. Sixty-two samples covering nine different types of CFRPs were designed, manufactured, and experimentally tested. Three machine learning models, namely ridge regression, random forest, and support vector regression, were trained on the data and compared. The results demonstrated a high prediction accuracy for the flexural strength (R2 = 0.966), flexural modulus (R2 = 0.871), and the mode-II energy release rate (R2 = 0.903). The study highlights the effectiveness of data-driven models in predicting key mechanical properties of CFRP composites, potentially reducing the need for extensive experimental testing and facilitating more efficient material design.
AB - Carbon fiber reinforced polymer (CFRP) composites are increasingly utilized for their lightweight and superior mechanical properties. This study uses machine learning models to predict the mechanical properties of CFRP composites based on the volume fraction of carbon nanotubes (CNTs), interlayer volume fraction, glass transition temperature, and manufacturing pressure. Sixty-two samples covering nine different types of CFRPs were designed, manufactured, and experimentally tested. Three machine learning models, namely ridge regression, random forest, and support vector regression, were trained on the data and compared. The results demonstrated a high prediction accuracy for the flexural strength (R2 = 0.966), flexural modulus (R2 = 0.871), and the mode-II energy release rate (R2 = 0.903). The study highlights the effectiveness of data-driven models in predicting key mechanical properties of CFRP composites, potentially reducing the need for extensive experimental testing and facilitating more efficient material design.
UR - https://www.scopus.com/pages/publications/105002159421
U2 - 10.1371/journal.pone.0319787
DO - 10.1371/journal.pone.0319787
M3 - Article
C2 - 40193342
AN - SCOPUS:105002159421
SN - 1932-6203
VL - 20
JO - PLoS ONE
JF - PLoS ONE
IS - 4 April
M1 - e0319787
ER -
