A Probabilistic Design Method for Fatigue Life of Metallic Component

[+] Author and Article Information
Danial Faghihi

Institute for Computational Engineering and Sciences, The University of Texas at Austin

Subhasis Sarkar

Technical Data Analysis, Inc., Falls Church, VA

Mehdi Naderi

Technical Data Analysis, Inc., Falls Church, VA

Jon R. Rankin

Curtiss-Wright, MIC-Laser Peening Division, Livermore CA

Lloyd Hackel

Curtiss-Wright, MIC-Laser Peening Division, Livermore CA

Nagaraja Iyyer

Technical Data Analysis, Inc., Falls Church, VA

1Corresponding author.

ASME doi:10.1115/1.4038372 History: Received July 17, 2017; Revised November 02, 2017


In the present study, a general probabilistic design framework is developed for cyclic fatigue life prediction of metallic hardware using methods that address uncer- tainty in experimental data and computational model. The methodology involves (i) fatigue test data conducted on coupons of Ti6Al4V material (ii) continuum damage mechanics based material constitutive models to simulate cyclic fatigue behavior of material (iii) variance-based global sensitivity analysis (iv) Bayesian framework for model calibration and uncertainty quantification and (v) computa- tional life prediction and probabilistic design decision making under uncertainty. The outcomes of computational analyses using the experimental data prove the feasibility of the probabilistic design methods for model calibration in presence of incomplete and noisy data. Moreover, using probabilistic design methods result in assessment of reliability of fatigue life predicted by computational models.

Copyright (c) 2017 by ASME
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