Buckypaper embedded self-sensing composite for real-time fatigue damage diagnosis and prognosis

Siddhant Datta, Rajesh Kumar Neerukatti, Aditi Chattopadhyay

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In this study, buckypaper (BP) membranes have been used to introduce self-sensing capability in glass fiber reinforced polymer matrix (GFRP) laminates by embedding them in the interlaminar region of the laminates. Piezoresistive characterization studies were conducted by subjecting the self-sensing GFRP (SGFRP) specimens to cyclic loading and high sensitivity to strain was observed. A measurement model for real-time quantification of fatigue crack, developed using in-situ resistance measurements obtained under fatigue loading, was used to quantify fatigue crack length in real time. The fatigue crack growth rates and the nature of crack propagation in baseline and SGFRP specimens were compared. The results show that the introduction of BP reduced the average crack growth rate by an order of magnitude as a result of crack tip blunting during fatigue, while facilitating real time strain sensing and damage quantification. A fully probabilistic prognosis methodology was also developed by combining the in-situ measurement model with a machine learning based prognosis model to accurately predict the real-time fatigue crack propagation using sequential Bayesian techniques.

Original languageEnglish (US)
Pages (from-to)353-360
Number of pages8
JournalCarbon
Volume139
DOIs
StatePublished - Nov 1 2018

Fingerprint

Fatigue damage
Fatigue crack propagation
Polymer matrix
Glass fibers
Laminates
Crack propagation
Composite materials
Fatigue of materials
Crack tips
Learning systems
Membranes
Fatigue cracks
fiberglass

Keywords

  • Buckypaper
  • Carbon nanotubes
  • Fatigue
  • Glass fiber
  • Prognosis
  • Self-sensing

ASJC Scopus subject areas

  • Chemistry(all)

Cite this

Buckypaper embedded self-sensing composite for real-time fatigue damage diagnosis and prognosis. / Datta, Siddhant; Neerukatti, Rajesh Kumar; Chattopadhyay, Aditi.

In: Carbon, Vol. 139, 01.11.2018, p. 353-360.

Research output: Contribution to journalArticle

Datta, Siddhant ; Neerukatti, Rajesh Kumar ; Chattopadhyay, Aditi. / Buckypaper embedded self-sensing composite for real-time fatigue damage diagnosis and prognosis. In: Carbon. 2018 ; Vol. 139. pp. 353-360.
@article{029ed79c50534053bc2ae96d7c57c9eb,
title = "Buckypaper embedded self-sensing composite for real-time fatigue damage diagnosis and prognosis",
abstract = "In this study, buckypaper (BP) membranes have been used to introduce self-sensing capability in glass fiber reinforced polymer matrix (GFRP) laminates by embedding them in the interlaminar region of the laminates. Piezoresistive characterization studies were conducted by subjecting the self-sensing GFRP (SGFRP) specimens to cyclic loading and high sensitivity to strain was observed. A measurement model for real-time quantification of fatigue crack, developed using in-situ resistance measurements obtained under fatigue loading, was used to quantify fatigue crack length in real time. The fatigue crack growth rates and the nature of crack propagation in baseline and SGFRP specimens were compared. The results show that the introduction of BP reduced the average crack growth rate by an order of magnitude as a result of crack tip blunting during fatigue, while facilitating real time strain sensing and damage quantification. A fully probabilistic prognosis methodology was also developed by combining the in-situ measurement model with a machine learning based prognosis model to accurately predict the real-time fatigue crack propagation using sequential Bayesian techniques.",
keywords = "Buckypaper, Carbon nanotubes, Fatigue, Glass fiber, Prognosis, Self-sensing",
author = "Siddhant Datta and Neerukatti, {Rajesh Kumar} and Aditi Chattopadhyay",
year = "2018",
month = "11",
day = "1",
doi = "10.1016/j.carbon.2018.06.059",
language = "English (US)",
volume = "139",
pages = "353--360",
journal = "Carbon",
issn = "0008-6223",
publisher = "Elsevier Limited",

}

TY - JOUR

T1 - Buckypaper embedded self-sensing composite for real-time fatigue damage diagnosis and prognosis

AU - Datta, Siddhant

AU - Neerukatti, Rajesh Kumar

AU - Chattopadhyay, Aditi

PY - 2018/11/1

Y1 - 2018/11/1

N2 - In this study, buckypaper (BP) membranes have been used to introduce self-sensing capability in glass fiber reinforced polymer matrix (GFRP) laminates by embedding them in the interlaminar region of the laminates. Piezoresistive characterization studies were conducted by subjecting the self-sensing GFRP (SGFRP) specimens to cyclic loading and high sensitivity to strain was observed. A measurement model for real-time quantification of fatigue crack, developed using in-situ resistance measurements obtained under fatigue loading, was used to quantify fatigue crack length in real time. The fatigue crack growth rates and the nature of crack propagation in baseline and SGFRP specimens were compared. The results show that the introduction of BP reduced the average crack growth rate by an order of magnitude as a result of crack tip blunting during fatigue, while facilitating real time strain sensing and damage quantification. A fully probabilistic prognosis methodology was also developed by combining the in-situ measurement model with a machine learning based prognosis model to accurately predict the real-time fatigue crack propagation using sequential Bayesian techniques.

AB - In this study, buckypaper (BP) membranes have been used to introduce self-sensing capability in glass fiber reinforced polymer matrix (GFRP) laminates by embedding them in the interlaminar region of the laminates. Piezoresistive characterization studies were conducted by subjecting the self-sensing GFRP (SGFRP) specimens to cyclic loading and high sensitivity to strain was observed. A measurement model for real-time quantification of fatigue crack, developed using in-situ resistance measurements obtained under fatigue loading, was used to quantify fatigue crack length in real time. The fatigue crack growth rates and the nature of crack propagation in baseline and SGFRP specimens were compared. The results show that the introduction of BP reduced the average crack growth rate by an order of magnitude as a result of crack tip blunting during fatigue, while facilitating real time strain sensing and damage quantification. A fully probabilistic prognosis methodology was also developed by combining the in-situ measurement model with a machine learning based prognosis model to accurately predict the real-time fatigue crack propagation using sequential Bayesian techniques.

KW - Buckypaper

KW - Carbon nanotubes

KW - Fatigue

KW - Glass fiber

KW - Prognosis

KW - Self-sensing

UR - http://www.scopus.com/inward/record.url?scp=85053074173&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053074173&partnerID=8YFLogxK

U2 - 10.1016/j.carbon.2018.06.059

DO - 10.1016/j.carbon.2018.06.059

M3 - Article

AN - SCOPUS:85053074173

VL - 139

SP - 353

EP - 360

JO - Carbon

JF - Carbon

SN - 0008-6223

ER -