Predicting mechanical properties of CFRP composites using data-driven models with comparative analysis DOI Creative Commons
Ammar Alsheghri, A. H. Al-Hammadi,

Vassilis Drakonakis

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0319787 - e0319787

Опубликована: Апрель 7, 2025

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 properties of CFRP based on volume fraction carbon nanotubes (CNTs), interlayer fraction, glass transition temperature, manufacturing pressure. Sixty-two samples covering nine different types CFRPs were designed, manufactured, experimentally tested. Three models, namely ridge regression, random forest, support vector trained data compared. The results demonstrated a high prediction accuracy flexural strength (R 2 = 0.966), modulus 0.871), mode-II energy release rate 0.903). highlights effectiveness data-driven in predicting key composites, potentially reducing need extensive experimental testing facilitating more efficient material design.

Язык: Английский

Predicting mechanical properties of CFRP composites using data-driven models with comparative analysis DOI Creative Commons
Ammar Alsheghri, A. H. Al-Hammadi,

Vassilis Drakonakis

и другие.

PLoS ONE, Год журнала: 2025, Номер 20(4), С. e0319787 - e0319787

Опубликована: Апрель 7, 2025

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 properties of CFRP based on volume fraction carbon nanotubes (CNTs), interlayer fraction, glass transition temperature, manufacturing pressure. Sixty-two samples covering nine different types CFRPs were designed, manufactured, experimentally tested. Three models, namely ridge regression, random forest, support vector trained data compared. The results demonstrated a high prediction accuracy flexural strength (R 2 = 0.966), modulus 0.871), mode-II energy release rate 0.903). highlights effectiveness data-driven in predicting key composites, potentially reducing need extensive experimental testing facilitating more efficient material design.

Язык: Английский

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