BROWSE

Related Researcher

Author's Photo

Park, Young-Bin
Functional Intelligent Materials Lab (FIMLab)
Research Interests
  • Composites
  • Nanocomposites
  • Smart Materials and Structures
  • Carbon Nanomaterials

ITEM VIEW & DOWNLOAD

Machine learning aided design of smart, self-sensing fiber-reinforced plastics

Cited 0 times inthomson ciCited 0 times inthomson ci
Title
Machine learning aided design of smart, self-sensing fiber-reinforced plastics
Author
Roh, Hyung DohLee, DahunLee, In YongPark, Young-Bin
Issue Date
2021-10
Publisher
Elsevier
Citation
Composites Part C: Open Access, v.6, pp.100186
Abstract
Numerous techniques have been developed for the non-destructive evaluation (NDE) of impact damage in fiber reinforced plastics (FRPs), following the increasing demands for their safety and maintenance. Considering the large-scale detection and the vast amount of data involved, machine learning (ML) can be utilized in NDE for damage type analysis and impact damage localization. Furthermore, self-sensing using carbon fiber in FRPs is an emerging technique for NDE that can be combined with ML. In this study, ML was used to design smart FRPs by selecting the fiber type and electrode distance considering the cost and electromechanical sensitivity. Furthermore, a novel algorithm for structural health self-sensing was suggested using an artificial neural network. The developed ML algorithms are advantageous since they do not require a theoretical model when all the factors and the variables of FRPs, such as the maximum absorbed impact energy, maximum impact force, initial electrical resistance, number of electrodes, fiber types, and electrode distance, are to be considered. The algorithm was trained using given input data and the target, and the output could be successfully obtained when new input data were provided. Therefore, the proposed ML algorithms hold great potential and applicability to FRP design and for NDE methods.
URI
https://scholarworks.unist.ac.kr/handle/201301/58816
DOI
10.1016/j.jcomc.2021.100186
ISSN
2666-6820
Appears in Collections:
MEN_Journal Papers
Files in This Item:
1-s2.0-S2666682021000815-main.pdf Download

find_unist can give you direct access to the published full text of this article. (UNISTARs only)

Show full item record

qrcode

  • mendeley

    citeulike

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

MENU