Data-driven stress and strain curves of the unidirectional composites by deep neural networks with principal component analysis and selective-data augmentation-Reference-Cited by-同舟云学术

Data-driven stress and strain curves of the unidirectional composites by deep neural networks with principal component analysis and selective-data augmentation

Published:2023-06 Issue: Volume:313 Page:116902
ISSN:0263-8223
Container-title:Composite Structures
language:en
Short-container-title:Composite Structures

Author:

Kim Do-Won,Go Myeong-Seok,Lim Jae Hyuk,Lee Seungchul

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Subject

Civil and Structural Engineering,Ceramics and Composites

Reference45 articles.

1. A new method for generating random fibre distributions for fibre reinforced composites;Yang;Compos Sci Technol,2013

2. Efficient generator of random fiber distribution with diverse volume fractions by random fiber removal;Park;Compos B Eng,2019

3. A microstructure modeling scheme for unidirectional composites using signed distance function based boundary smoothing and element trimming;Lim;Adv Eng Softw,2017

4. Random Fiber Array Generation Considering Actual Noncircular Fibers with a Particle-Shape Library;Go;Appl Sci,2020

5. A virtual experimental approach to evaluate transverse damage behavior of a unidirectional composite considering noncircular fiber cross-sections;Jeong;Compos Struct,2019

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