A hybrid prognosis model for predicting fatigue crack propagation under biaxial in-phase and out-of-phase loading

Rajesh Kumar Neerukatti, Aditi Chattopadhyay, Nagaraja Iyyer, Nam Phan

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

A hybrid prognosis model has been developed to predict the crack propagation in aluminum alloys subject to biaxial in-phase and out-of-phase fatigue loading conditions. The novel methodology combines physics-based modeling with machine learning techniques to predict crack growth in aluminum alloys. Understanding the failure mechanisms under these complex loading conditions is critical to developing reliable prognostic models. Therefore, extensive fatigue tests were conducted to study the failure modes of carefully designed cruciform specimens. Energy release rate was used as the physics-based parameter and Gaussian process was used to model the complex nonlinear relationships in the prognosis framework. The methodology was used to predict crack propagation in Al7075-T651 under a range of loading conditions. The predictions from the prognosis model were validated using the data obtained from the biaxial tests. The results indicate that the algorithm is able to accurately predict the crack propagation under proportional, non-proportional, in-phase, and out-of-phase loading conditions.

Original languageEnglish (US)
JournalStructural Health Monitoring
DOIs
StateAccepted/In press - Aug 1 2017

Fingerprint

Fatigue crack propagation
Fatigue
Crack propagation
Physics
Aluminum
Aluminum alloys
Fatigue of materials
Energy release rate
Failure modes
Learning systems
Growth

Keywords

  • Biaxial fatigue
  • fatigue crack propagation
  • Gaussian process
  • prognosis

ASJC Scopus subject areas

  • Biophysics
  • Mechanical Engineering

Cite this

A hybrid prognosis model for predicting fatigue crack propagation under biaxial in-phase and out-of-phase loading. / Neerukatti, Rajesh Kumar; Chattopadhyay, Aditi; Iyyer, Nagaraja; Phan, Nam.

In: Structural Health Monitoring, 01.08.2017.

Research output: Contribution to journalArticle

@article{1b156beca9af4b6084fec6b3041c0c48,
title = "A hybrid prognosis model for predicting fatigue crack propagation under biaxial in-phase and out-of-phase loading",
abstract = "A hybrid prognosis model has been developed to predict the crack propagation in aluminum alloys subject to biaxial in-phase and out-of-phase fatigue loading conditions. The novel methodology combines physics-based modeling with machine learning techniques to predict crack growth in aluminum alloys. Understanding the failure mechanisms under these complex loading conditions is critical to developing reliable prognostic models. Therefore, extensive fatigue tests were conducted to study the failure modes of carefully designed cruciform specimens. Energy release rate was used as the physics-based parameter and Gaussian process was used to model the complex nonlinear relationships in the prognosis framework. The methodology was used to predict crack propagation in Al7075-T651 under a range of loading conditions. The predictions from the prognosis model were validated using the data obtained from the biaxial tests. The results indicate that the algorithm is able to accurately predict the crack propagation under proportional, non-proportional, in-phase, and out-of-phase loading conditions.",
keywords = "Biaxial fatigue, fatigue crack propagation, Gaussian process, prognosis",
author = "Neerukatti, {Rajesh Kumar} and Aditi Chattopadhyay and Nagaraja Iyyer and Nam Phan",
year = "2017",
month = "8",
day = "1",
doi = "10.1177/1475921717725019",
language = "English (US)",
journal = "Structural Health Monitoring",
issn = "1475-9217",
publisher = "SAGE Publications Ltd",

}

TY - JOUR

T1 - A hybrid prognosis model for predicting fatigue crack propagation under biaxial in-phase and out-of-phase loading

AU - Neerukatti, Rajesh Kumar

AU - Chattopadhyay, Aditi

AU - Iyyer, Nagaraja

AU - Phan, Nam

PY - 2017/8/1

Y1 - 2017/8/1

N2 - A hybrid prognosis model has been developed to predict the crack propagation in aluminum alloys subject to biaxial in-phase and out-of-phase fatigue loading conditions. The novel methodology combines physics-based modeling with machine learning techniques to predict crack growth in aluminum alloys. Understanding the failure mechanisms under these complex loading conditions is critical to developing reliable prognostic models. Therefore, extensive fatigue tests were conducted to study the failure modes of carefully designed cruciform specimens. Energy release rate was used as the physics-based parameter and Gaussian process was used to model the complex nonlinear relationships in the prognosis framework. The methodology was used to predict crack propagation in Al7075-T651 under a range of loading conditions. The predictions from the prognosis model were validated using the data obtained from the biaxial tests. The results indicate that the algorithm is able to accurately predict the crack propagation under proportional, non-proportional, in-phase, and out-of-phase loading conditions.

AB - A hybrid prognosis model has been developed to predict the crack propagation in aluminum alloys subject to biaxial in-phase and out-of-phase fatigue loading conditions. The novel methodology combines physics-based modeling with machine learning techniques to predict crack growth in aluminum alloys. Understanding the failure mechanisms under these complex loading conditions is critical to developing reliable prognostic models. Therefore, extensive fatigue tests were conducted to study the failure modes of carefully designed cruciform specimens. Energy release rate was used as the physics-based parameter and Gaussian process was used to model the complex nonlinear relationships in the prognosis framework. The methodology was used to predict crack propagation in Al7075-T651 under a range of loading conditions. The predictions from the prognosis model were validated using the data obtained from the biaxial tests. The results indicate that the algorithm is able to accurately predict the crack propagation under proportional, non-proportional, in-phase, and out-of-phase loading conditions.

KW - Biaxial fatigue

KW - fatigue crack propagation

KW - Gaussian process

KW - prognosis

UR - http://www.scopus.com/inward/record.url?scp=85042198705&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85042198705&partnerID=8YFLogxK

U2 - 10.1177/1475921717725019

DO - 10.1177/1475921717725019

M3 - Article

JO - Structural Health Monitoring

JF - Structural Health Monitoring

SN - 1475-9217

ER -