Automated fatigue crack growth analysis of components

Abstract

A new methodology has been developed for automated fatigue crack growth (FCG) life analysis of components based on finite element (FE) stress 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 appropriate to an irregularly shaped component cross-section with arbitrary stress gradients on the crack plane. The fracture model geometry algorithms are robust enough to accommodate crack origins on the surface or in the interior of the component, as well as finite component dimensions, arbitrarily curved component 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 accommodate univariant and bivariant stress gradients and have been optimized for both computational efficiency and accuracy. The resulting calculations can be used to automatically construct FCG life contours for the component and to identify hot spots. Ultimately, the new algorithms will be used to support automated probabilistic assessments that calculate component reliability considering variability in the initial crack size, initial crack location, crack occurrence rate, applied stress magnitudes, material properties, inspection efficacy, and inspection time.

A new FE post-processor for probabilistic fatigue assessment in the presence of defects and its application to AM parts

“…The propagation of cracks from defects, such as non-metallic inclusions and pores, tends to grow in a direction perpendicular to the (local) maximum principal stress range [30]. In fact, this is the preferred growth orientation of fatigue cracks beyond microstructural size scales (i.e., mode I cracks) [44]. This hypothesis is adopted in most fatigue models dealing with defects (e.g., DARWIN, P-FAT), and is also exploited in the present model.…”

Machine learning‐based efficient stress intensity factor calculation for aeroengine disk probabilistic risk assessment under polynomial stress fields

“… The second step is conducted as McClung et al 22 indicate. In this example, the irregular rotational symmetry plane of the aeroengine disk has been generated to the rectangular plate as Figure 16 shows. …”

“…Then, the stress with maximum stress gradient is extracted and approximated as follows (a is the crack size in the SB-6 disk; the x axis of the stress is parallel to the direction with maximum stress gradient): ii. The second step is conducted as McClung et al 22 indicate. In this example, the irregular rotational symmetry plane of the aeroengine disk has been generated to the rectangular plate as Figure 16 shows.…”

“…21 Thus, a certain disk plane is under discussion, as shown in Figure 1A. If the crack location in the plane is known, by a simplification method mentioned in McClung et al, 22 the extracted plane under complex stress field can be simplified to be a type of model of a crack in a rectangular plate under polynomial stress, which is used to calculate the SIFs, thus the life of the disk.…”

Benchmark of a probabilistic fatigue software based on machined and as-built components manufactured in AlSi10Mg by L-PBF

“…Many different approaches are available in the literature for probabilistic assessment based on a FE structural analysis and the presence of defects/anomalies: i) approaches based on weakest-link concepts and the underlying assumption of Weibull distributions [43,44,45]; ii) weakest-link approach based on a fatigue model combined with extreme value statistics for defects [46]; iii) explicit crack-growth simulations combined with Monte Carlo simulations [47,48,49,50,51,52]. The weakest-link approaches have the advantage of implicit analytical formulations that drastically reduce the computational time, while the explicit crack growth simulations can precisely describe the life from the local stress field and they can be combined with analyses of defect detectability [53].…”