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Research Papers

Fatigue Life Prediction Based on Probabilistic Fracture Mechanics: Case Study of Automotive Parts

[+] Author and Article Information
Mahboubeh Yazdanipour

Department of Mechanical Engineering,
Sahand University of Technology,
Tabriz 51335, Iran
e-mail: m_yazdanipour@sut.ac.ir

Mohammad Pourgol-Mohammad

Department of Mechanical Engineering,
Sahand University of Technology,
Tabriz 51335, Iran
e-mail: pourgol-mohamadm2@asme.org

Naghd-Ali Choupani

Department of Mechanical Engineering,
Sahand University of Technology,
Tabriz 51335, Iran
e-mail: choupani@sut.ac.ir

Mojtaba Yazdani

Department of Mechanical Engineering,
Sahand University of Technology,
Tabriz 51335, Iran
e-mail: m.yazdani.sut@gmail.com

1Corresponding author.

Manuscript received February 18, 2015; final manuscript received June 10, 2015; published online November 20, 2015. Assoc. Editor: Chimba Mkandawire.

ASME J. Risk Uncertainty Part B 2(1), 011002 (Nov 20, 2015) (6 pages) Paper No: RISK-15-1014; doi: 10.1115/1.4030946 History: Received February 18, 2015; Accepted June 29, 2015

This paper studies the stochastic behavior of fatigue crack growth analytically and empirically by employing basic models in fracture mechanics. The research estimates the crack growth rate probabilistically, quantifies the uncertainty of probabilistic models under fatigue loading in automotive parts, and applies the simulations on W319 aluminum alloy, which has vast applications in automotive components’ products. Walker and Forman correlations are used in the paper. The deterministic simulations of these models are verified with afgrow code and validated experimentally with fatigue data of W319 aluminum. Then, the models are treated probabilistically by considering the models’ parameters stochastic. Monte Carlo (MC) simulation is employed to investigate the models under stochastic conditions. The paper is quantifies the propagation of uncertainty with calculating the standard deviations of crack lengths via cycles. The proposed procedure is useful for selecting a proper probabilistic fatigue crack growth model in specific applications and can be used in future fatigue studies not only in the automotive industry but also in other critical fields, to obtain more reliable conclusions.

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Figures

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Fig. 1

Research flowchart

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Fig. 3

matlab code and afgrow comparison—Walker

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Fig. 4

matlab code and afgrow comparison—Forman

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Fig. 5

Models’ validation

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Fig. 6

Propagation of uncertainty in Forman model

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MC simulation with different numbers of iterations

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Fig. 8

STD under six different conditions: (a) a0=0.0009 and (b) a0=0.009

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