2017/03/22 by Danial Faghihi, Faghihi, Danial, Subhasis Sarkar +7
Decision Sciences · Engineering · #Computational Engineering #FOS: Computer and information sciences #Fatigue and fracture mechanics #Finance #High Temperature Alloys and Creep #Probabilistic and Robust Engineering Design #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.1703.07770
openalex publication_date 2017/03/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.