A novel machine learning framework informed by the fractional calculus dynamic model of hybrid glass/jute woven composite

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F46747885%3A24210%2F24%3A00012428" target="_blank" >RIV/46747885:24210/24:00012428 - isvavai.cz</a>

Result on the web

<a href="https://onlinelibrary.wiley.com/doi/epdf/10.1002/app.55888" target="_blank" >https://onlinelibrary.wiley.com/doi/epdf/10.1002/app.55888</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/app.55888" target="_blank" >10.1002/app.55888</a>

Result language

angličtina

Original language name

A novel machine learning framework informed by the fractional calculus dynamic model of hybrid glass/jute woven composite

Original language description

This study investigates the influence of glass-jute fiber hybridization on the dynamic viscoelastic performance of woven natural fiber composites. Experiments elucidated the effects of low-frequency vibration and glass/jute ratio on the storage modulus and loss factor. Results exhibited nonlinear escalations in storage modulus and loss factor with increasing frequency and glass fiber content. The loss factor also demonstrated nonlinear rises with frequency and jute content. A fractional calculus-based two-branch model successfully captured these behaviors by incorporating frequency and hybrid ratio as key variables, showing excellent agreement with measurements. To further improve predictive accuracy, an artificial neural network informed by the fractional calculus dynamic model is implemented by enforcing physical constraints during training. The physics-informed artificial neural network achieved higher correlation to experiments than unconstrained models, affirming the value of fusing physics knowledge into data-driven models. This study highlights the promise of physics-guided machine learning for predicting the intricate dynamics of sustainable natural fiber hybrid composites. The integrated analytical and data-driven techniques provide a pathway to comprehensively model the mechanical performance of advanced multiphase materials over diverse conditions.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10404 - Polymer science

Project

<a href="/en/project/EF16_025%2F0007293" target="_blank" >EF16_025/0007293: Modular platform for autonomous chassis of specialized electric vehicles for freight and equipment transportation</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Applied Polymer Science

ISSN

0021-8995

e-ISSN

—

Volume of the periodical

3

Issue of the periodical within the volume

2024

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

1-13

UT code for WoS article

001247867400001

EID of the result in the Scopus database

2-s2.0-85196210569