Abstract
Corrosion in aluminium alloys is initiated and sustained at constituent particles within the metal matrix via a localized galvanic process. These particles are also known to play a critical role in fatigue crack initiation and growth. Consequently, statistical characterization of particle geometrical features is critical when modelling corrosion and fatigue. A key statistic is particle size distribution, which was extensively modelled here via imaging of unstressed and fatigued 7075-T6 aluminium alloys. Fatigued samples were obtained from the outer wing panels of teardown specimens from retired military aircraft. The purpose of this effort was therefore to analyse extensive sets of particle geometry data obtained via microscopy using advanced multimodal statistical modelling and to appropriately characterize the properties of constituent particles and fatigue cracks found in these specimens. The resulting distribution functions for the underlying modes are assumed to be three-parameter Weibull distribution functions.
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