  In the present study, a general probabilistic design framework is developed
for cyclic fatigue life prediction of metallic hardware using methods that
address uncertainty 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) computational 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.

Computational Engineering, Finance, and Science