Analysis and prediction of microstructural effects on long-term fatigue performance of an aluminium aerospace alloy

A program of experimental and analytical tasks has been conducted to define the linkage(s) between microstructural characteristics and fatigue performance in an aluminum alloy typically used for airframe structural applications. The first goal was to develop data for quantitatively linking measurable characteristics of material microstructure with long-term fatigue performance. The second goal was to develop models to predict fatigue performance based on the microstructural characteristics. The work focused on several process variants of Al alloy 7050-T7451 plate. This material was chosen because of its widespread use for flight-critical airframe structural components, and the particular characteristics associated with the manufacturing, service and maintenance of thick section components. Within the framework of this objective, life-limiting microstructural features have been identified and ranked by severity, and models to quantitatively describe the evolution and growth of macrostructural cracks from those features have been developed. The modeling framework has been applied to predict the cyclic lifetime of the 7050 alloy process variants based on the populations of life-limiting microstructural features. In addition, the models have been used to show how changes in the material characteristics may affect the fatigue performance. This includes predictions of the effect of changing the life-limiting microfeature size and shape distributions, and the effect of changing material strength properties. The use of this modeling approach to probabilistically describe the implications of changes in the microstructure has been demonstrated, thereby allowing the effects of material pedigree to be predictively linked with the structural integrity of end components. The modeling framework has potential applications in airframe design support processes, and as a tool for use in material and product form selection processes.