Analysis of stress intensity factor for fatigue crack using bootstrap S-version finite element model

Abstract

PurposeFailure of the materials occurs once the stress intensity factor (SIF) overtakes the material fracture toughness. At this level, the crack will grow rapidly resulting in unstable crack growth until a complete fracture happens. The SIF calculation of the materials can be conducted by experimental, theoretical and numerical techniques. Prediction of SIF is crucial to ensure safety life from the material failure. The aim of the simulation study is to evaluate the accuracy of SIF prediction using finite element analysis.Design/methodology/approachThe bootstrap resampling method is employed in S-version finite element model (S-FEM) to generate the random variables in this simulation analysis. The SIF analysis studies are promoted by bootstrap S-version Finite Element Model (BootstrapS-FEM). Virtual crack closure-integral method (VCCM) is an important concept to compute the energy release rate and SIF. The semielliptical crack shape is applied with different crack shape aspect ratio in this simulation analysis. The BootstrapS-FEM produces the prediction of SIFs for tension model.FindingsThe mean of BootstrapS-FEM is calculated from 100 samples by the resampling method. The bounds are computed based on the lower and upper bounds of the hundred samples of BootstrapS-FEM. The prediction of SIFs is validated with Newman–Raju solution and deterministic S-FEM within 95 percent confidence bounds. All possible values of SIF estimation by BootstrapS-FEM are plotted in a graph. The mean of the BootstrapS-FEM is referred to as point estimation. The Newman–Raju solution and deterministic S-FEM values are within the 95 percent confidence bounds. Thus, the BootstrapS-FEM is considered valid for the prediction with less than 6 percent of percentage error.Originality/valueThe bootstrap resampling method is employed in S-FEM to generate the random variables in this simulation analysis.

Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties

“…{τf′,γf′,bγ,cγ,G} can be estimated using from axial fatigue properties (Yu et al. , 2017; Mohd Noh et al. , 2020) {σf′,εf′,b,c,E} as follows:…”

“…Moreover, the shear fatigue properties i.e. fτ 0 f ; γ 0 f ; b γ ; c γ ; Gg can be estimated using from axial fatigue properties (Yu et al, 2017;Mohd Noh et al, 2020)…”

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“…The study of cracked surfaces is imperative because it has a major significant influence on the mechanical characteristics of an object [20]. Moreover, the study of SIF on cracked surfaces also tenders the stress intensity which subdues it [21]. The outcome could be used to further the scope of manufacturing a more splendid shaft type that could withstand higher residual stress.…”

Prediction and evaluation of fatigue life considering material parameters distribution characteristic

“…, 2017). The bootstrap resampling method is employed in the S-version finite element model to generate the random variables (Mohd Noh et al , 2020). Bayesian Bootstrap has been widely studied in different fields (Aldridge and Bowman, 2005; Fushiki, 2010).…”