New Methods for Automated Fatigue Crack Growth and Reliability Analysis

Abstract

A new methodology has been developed for automated fatigue crack growth (FCG) life and reliability analysis of components based on finite element (FE) stress and temperature models, weight function stress intensity factor (SIF) solutions, and algorithms to define idealized fracture geometry models. The idealized fracture geometry models are rectangular cross sections with dimensions and orientation that satisfactorily approximate an irregularlyshaped component cross section. The fracture model geometry algorithms are robust enough to accommodate crack origins on the surface or in the interior of the component, along with finite component dimensions, curved surfaces, arbitrary stress gradients, and crack geometry transitions as the crack grows. Stress gradients are automatically extracted from multiple load steps in the FE models for input to the fracture models. The SIF solutions accept univariant stress gradients and have been optimized for both computational efficiency and accuracy. The resulting calculations are used to automatically construct FCG life contours for the component and to identify hot spots. Finally, the new algorithms are used to support automated probabilistic assessments that calculate component reliability considering the variability in the size, location, and occurrence rate of the initial anomaly; the applied stress magnitudes; material properties; probability of detection; and inspection time. The methods are particularly useful for determining the probability of component fracture due to fatigue cracks forming at material anomalies that can occur anywhere in the volume of the component. The automation significantly improves the efficiency of the analysis process while reducing the dependency of the results on the individual judgments of the analyst. The automation also facilitates linking of the life and reliability management process with a larger integrated computational materials engineering (ICME) context, which offers the potential for improved design optimization.