A multi-segment probabilistic fatigue crack growth model to account for reliability in design of components

TLDR: In this article, a probabilistic model that considers crack growth data from multiple constant amplitude tests is studied using continuous and segmented crack growth rate data models, which provides more accurate life predictions with lesser variance with experimental data than the continuous data model.

Abstract: Analysis of fatigue crack growth has undergone several developments in the past few decades. In general, fatigue crack growth is studied using deterministic models from either single or multiple crack growth rate tests. Realizing that several variables influence fatigue crack growth, such as, microstructure, crack length measurement techniques, it is pertinent to assess crack growth using probabilistic models. In the present work, a probabilistic model that considers crack growth data from multiple constant amplitude tests is studied using continuous and segmented crack growth rate data models. It is observed that the prediction of life using Paris constants from continuous data model is accurate only in a finite region of the crack growth. The model based on segmented data provides more accurate life predictions with lesser variance with experimental data than the continuous data model. The proposed modification of probabilistic model for fatigue crack growth can be used to estimate reliability factors based on the scatter in specific crack growth region.