IMPROVED PROBABILISTIC LIFE ESTIMATION IN ENGINEERING STRUCTURES: MODELING MULTI-SITE FATIGUE CRACKING
Al Tamimi, Abdallah Moh'd AR. Sh. M.
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The purpose of this paper is to investigate the effect of fatigue, in the presence of neighboring cracks, and to integrate that into a probabilistic physics of failure based model that could be used to predict crack growth. A total of 20 fatigue experiments were performed at different loading conditions using dog-bone samples of API-5L grade B carbon steel containing neighboring cracks. The fatigue testing was conducted to generate the data needed for the probabilistic fatigue life prediction model development. Moreover, these experiments have investigated the impact of both neighboring cracks dimensional variability and the loading conditions on cracks interaction, coalescence and growth. The experiment layout was designed to improve some of the existing experimental layouts presented in the literature. Moreover, a new approach for measuring the neighboring cracks depth and the associated number of cycles in dog-bone shaped samples using different microscopy tools and image-processing techniques was proposed. On the other hand, simulation efforts were also performed to assess the Stress Intensity Factor (SIF) around neighboring cracks. Models discussing how the SIF of single semi-elliptical crack could be corrected to account for the neighboring cracks interaction were discussed in order to better understand the fatigue behavior. A combination of these models was integrated to compute the SIF values necessary for the probabilistic life prediction modeling purposes. Also, a new strategy for investigating ligament failure by detecting when it occurs rather than how it occurs was developed in this work. A demonstration of an improved understanding of the impact of different loading conditions on the ligament failure phenomena both using experiments and simulation was also discussed. Finally, a multi-site fatigue crack growth rate model was developed and its parameters including their uncertainties were estimated. A Bayesian approach was adopted to perform uncertainty characterization and model validation.