Probabilistic method for predicting the variability in fatigue behavior of 7075-T6 aluminum

Three Dimensional Microstructures: Statistical Analysis of Second Phase Particles in AA7075-T651

“…These constituents make up approximately 2% by volume of the total matrix. It has been determined by several groups that the majority of the constituent particles are either iron-aluminum compounds or magnesium silicide [6,7]. During plastic deformation, the non-deforming particles are sites of stress concentration in the matrix and many of them eventually crack or debond from the matrix.…”

On the relationship between statistical distributions of defect size and fatigue life in 7050-T7451 thick plate and A356-T6 castings

“…Laz and Hillberry [10] also used the lognormal distribution for defect sizes in 2024-T3 alloy in their model and found that crack initiating inclusions were primarily from the upper tail of the inclusion size distribution as expected. Gruenberg et al [11] reached the same conclusion in their study on modeling the fatigue variability in 7075-T6 aluminum alloy by using the lognormal distribution. They suggested that an extreme value distribution be used for modeling purposes but did not indicate which extreme value distribution should be used.…”

“…To model the effect of structural defects on the fatigue performance of metals, two main approaches have been taken by researchers in the literature: (i) taking the entire pore size dis- tribution into account [6,10,11], or (ii) modeling the distribution of largest defects and/or inclusions [3,12,13] that initiate fracture. For instance, the model of Yi et al [6,7] assumes that pore size follows the lognormal distribution, which is consistent with their histograms as well as statistical analysis of pore sizes for Mg alloy castings [8].…”

Statistical distributions for the size of fatigue-initiating defects in Al–7%Si–0.3%Mg alloy castings: A comparative study

“…Laz and Hillberry [12] also used the lognormal distribution for defect sizes in 2024-T3 alloy in their model and found that crack initiating inclusions were primarily from the upper tail of the inclusion size distribution as expected. Gruenberg et al [13] reached the same conclusion in their study on modeling the fatigue variability in 7075-T6 aluminum alloy by using the lognormal distribution. They suggested that an extreme value distribution be used for modeling purposes but did not indicate which extreme value distribution should be used.…”

“…To model the effect of structural defects on the fatigue performance of metals, two main approaches have been taken by researchers in the literature: (i) taking the entire pore size distribution into account [11][12][13], or (ii) modeling the distribution of largest defects and/or inclusions [14][15][16] that initiate fracture. For instance, the model of Yi et al [11,17] assumes that pore size follows the lognormal distribution, which is consistent with their histograms as well as statistical analysis of pore sizes for Mg alloy castings [18].…”