Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites-Reference-Cited by-同舟云学术

Data-driven approach for the prediction of mechanical properties of carbon fiber reinforced composites

Published:2022 Issue:19 Volume:3 Page:7319-7327
ISSN:2633-5409
Container-title:Materials Advances
language:en
Short-container-title:Mater. Adv.

Author:

Shah Vade¹^ORCID,Zadourian Steven¹,Yang Charles¹,Zhang Zilan¹,Gu Grace X.¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, University of California, Berkeley, CA 94720, USA

Abstract

Supervised machine learning models are trained on experimental data to predict the mechanical properties of composite materials. Results show that these techniques are reasonably accurate and generalizable.

Funder

Office of Naval Research

General Motors Corporation

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science,Chemistry (miscellaneous)

Link

http://pubs.rsc.org/en/content/articlepdf/2022/MA/D2MA00698G

Reference54 articles.

1. Biomedical applications of polymer-composite materials: a review

2. Review of advanced composite structures for naval ships and submarines

3. Hierarchically Enhanced Impact Resistance of Bioinspired Composites

4. P. K.Mallick , Fiber-reinforced composites: materials, manufacturing, and design , CRC press , 2007

5. Bone-Inspired Materials by Design: Toughness Amplification Observed Using 3D Printing and Testing

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