Probabilistic modeling of the size effect and scatter in High Cycle Fatigue using a Monte-Carlo approach: Role of the defect population in cast aluminum alloys

Effect of microstructural heterogeneity on fatigue strength predicted by reinforcement machine learning

“…This model may be a viable solution for the dispersion problem of testing data in the VHCF regime, which was handled by consideration of heat dissipation 102 . However, using the Metropolis model without extensive experimental data showed good scatter prediction outcomes without underestimation of scattering; as shown earlier, a simpler Monte Carlo was used 103 . In this study, the Metropolis Monte Carlo algorithm in a Bayesian setting, along with the character of the RL algorithm, promoted the traditional Monte Carlo algorithm to a ML character with sufficient predictive power.…”

“…102 However, using the Metropolis model without extensive experimental data showed good scatter prediction outcomes without underestimation of scattering; as shown earlier, a simpler Monte Carlo was used. 103 In this study, the Metropolis Monte Carlo algorithm in a Bayesian setting, along with the character of the RL algorithm, promoted the traditional Monte Carlo algorithm to a ML character with sufficient predictive power. It is planned to enhance this power in the future by coupling it with phenomenological modeling and continuum damage theories.…”

Effect of laser‐scan strategy on microstructure and fatigue properties of 316L additively manufactured stainless steel

“…In addition, X‐ray tomography measurements reveal high differences, as there is an order of magnitude of difference in porosity ratio between each strategy. These high differences probably play a key role in fatigue crack initiation 51,52 …”

“…These high differences probably play a key role in fatigue crack initiation. 51,52 F I G U R E 4 Grain size measurements for the three microstructures SLM_SR, SLM_Rot, and SLM_Chess, along different directions at an angle of 0 -180 with the building direction. 0 and 180 correspond to the building direction; 90 corresponds to the horizontal direction.…”

Statistical study of the size and spatial distribution of defects in a cast aluminium alloy for the low fatigue life assessment

“…In the past ten years a great effort has been made by the fatigue community in order to propose microstructure sensitive numerical methods [4][5][6] that allow to consider more precisely the role of key microstructural features. In the literature, many works related to the defect sensitivity of the fatigue behaviour of aluminium alloys focus on the role of the size and the shape of defects [7,8], but very little work are dedicated to the precise description and modelling of the spatial distribution of defects, especially in 3D [9,10].…”