Data- and model-driven quantification of the interaction of defect distribution, atmosphere, and stress parameters during the HCF/VHCF damage evolution of age-hardenable Al-Si cast alloys

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Age-hardenable Al-Si casting alloys are materials with an intrinsically high fatigue strength which, however, can only be included in component design with considerable and generalized restrictions due to casting-related defects (pores, precipitates), microstructure inhomogeneities (variations in DAS, silicon morphology) and environmental effects (air humidity, loading frequency). Within the scope of the proposed project, the effects already identified in the previous project are to be quantitatively determined in their interaction and implemented in a service life assessment model. By including Deep-Learning in combination with the process-related control of the defects, these can be integrated into the component dimensioning in a localized approach, so that the considerable strength potential of these alloys, which is still mainly unused today, can be used for demanding lightweight-design applications. By combining (i) integral and locally resolved nondestructive damage monitoring (in-situ) during fatigue (ii) under different loading conditions with (iii) a specific variation of the cast aluminum microstructures, the damage mechanisms can be used in the further development and adaptation of modeling approaches for mechanism-based fatigue life prediction and the stress-appropriate alloy design.