More than 25 years of extreme value statistics for defects: Fundamentals, historical developments, recent applications

Beretta, Stefano

doi:10.1016/j.ijfatigue.2021.106407

Cited by 51 publications

(24 citation statements)

References 63 publications

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“…We found this particularly true here in Figure 12A. However, second to the influence of chemical composition and microstructure, which does not contradict Berretta 104 or even much earlier statements by Murakami and Endo, 105 who did not discuss these influences in isolation but as secondary influences interacting with the projected area. Schönbauer and Mayer 106 asserted that Murakami and Endo 105 are true if the crack propagation phase highly influences the fatigue strength in sharply notched defects.…”

Section: Resultssupporting

confidence: 80%

“…61 Beretta placed a great emphasis on the projected area and its role in fatigue scatter. 104 We found this particularly true here in Figure 12A. However, second to the influence of chemical composition and microstructure, which does not contradict Berretta 104 or even much earlier statements by Murakami and Endo, 105 who did not discuss these influences in isolation but as secondary influences interacting with the projected area.…”

Section: Predicted Influence Of Heterogeneity On Fatigue Strengthsupporting

confidence: 71%

“…Siddique asserted the microstructure's influence on this alloy's fatigue strength in this range of loading 61 . Beretta placed a great emphasis on the projected area and its role in fatigue scatter 104 . We found this particularly true here in Figure 12A.…”

Section: Resultsmentioning

confidence: 54%

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

Awd

Münstermann

Walther

2022

Fatigue Fract Eng Mat Struct

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The posterior statistical distributions of fatigue strength are determined using Bayesian inferential statistics and the Metropolis Monte Carlo method.This study explores how structural heterogeneity affects ultrahigh cycle fatigue strength in additive manufacturing. Monte Carlo methods and procedures may assist estimate fatigue strength posteriors and scatter. The acceptable probability in Metropolis Monte Carlo relies on the Markov chain's random microstructure state. In addition to commonly studied variables, the proportion of chemical composition was demonstrated to substantially impact fatigue strength if fatigue lifetime in crack propagation did not prevail due to high threshold internal notches. The study utilizes an algorithm typically used for quantum mechanics to solve the complicated multifactorial fatigue problem. The inputs and outputs are modified by fitting the microstructural heterogeneities into the Metropolis Monte Carlo algorithm. The main advantage here is applying a general-purpose nonphenomenological model that can be applied to multiple influencing factors without high numerical penalty.

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Section: Resultssupporting

confidence: 80%

Section: Predicted Influence Of Heterogeneity On Fatigue Strengthsupporting

confidence: 71%

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

Awd

Münstermann

Walther

2022

Fatigue Fract Eng Mat Struct

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“…Defects are very detrimental to fatigue performance 8,9 . The existence of defects would limit the fatigue strength and fatigue life of the materials, 10,11 and the size, amount, and location of defects play an important role 12,13 . However, the effect of defects on the FCG rate in SLM Ti6Al4V is not fully understood.…”

Section: Introductionmentioning

confidence: 99%

“…8,9 The existence of defects would limit the fatigue strength and fatigue life of the materials, 10,11 and the size, amount, and location of defects play an important role. 12,13 However, the effect of defects on the FCG rate in SLM Ti6Al4V is not fully understood. Waddell et al 14 found that increasing the porosity in Ti6Al4V by varying the SLM parameters can blunt small cracks and prevent crack growth.…”

Section: Introductionmentioning

confidence: 99%

Different effects of multiscale microstructure on fatigue crack growth path and rate in selective laser melted Ti6Al4V

Wang

Zhang

et al. 2022

Fatigue Fract Eng Mat Struct

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During the selective laser melting (SLM) process of Ti6Al4V, a special structure can be formed with columnar prior β grains along the building direction and fully martensitic α 0 within the β grain. To investigate the influence of such special structure on the fatigue crack growth (FCG) rate, Ti6Al4V specimens fabricated by SLM were heat-treated at two different temperatures in this study. The columnar grains were retained, and the martensite was decomposed when heat-treated below the β transus. It is found that all the SLM features were removed when heat-treated above the β transus. FCG rate tests were subsequently performed at room temperature, and it was found that the prior β grains affected the macroscopic fracture morphology, but there was no discernible influence on the FCG rate. The morphology of the α phase affected the crack growth path and the FCG rate. Changes in the strengthtoughness relationship induced by heat treatment can help understand the decrease in the FCG rate.

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Influences of Manufacturing‐Related Microstructural Variations on Fatigue in Carbide‐Rich Tool Steels

Scholl

Bezold

Broeckmann

2022

steel research int.

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Here, D is a factor depending on the defect location (surface or bulk), while α depends on the hardness. Studies by Meurling et al., [12] Sohar et al., [13][14][15][16][17][18][19][20] Kazymyrovych et al., [21,22] Ekengren et al., [23] Bergström et al., [24] and Randelius et al. [25,26] investigated the influences of microstructure properties on HCF (N G ¼ 10 7 ), very-HCF (VHCF) (N G ¼ 10 8-9 ), and ultra-HCF

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More than 25 years of extreme value statistics for defects: Fundamentals, historical developments, recent applications

Cited by 51 publications

References 63 publications

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

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

Different effects of multiscale microstructure on fatigue crack growth path and rate in selective laser melted Ti6Al4V

Influences of Manufacturing‐Related Microstructural Variations on Fatigue in Carbide‐Rich Tool Steels

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