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

时效硬化铝硅铸造合金是一种本质上具有高疲劳强度的材料，然而，由于与铸造有关的缺陷（气孔、沉淀）、微观结构不均匀（DAS、硅形态的变化）和环境影响（空气湿度、加载频率），这种材料只能包含在具有相当大和普遍限制的部件设计中。在建议的项目范围内，在前一个项目中已经确定的影响将在相互作用中定量确定，并在使用寿命评估模型中实施。通过将深度学习与缺陷的工艺相关控制相结合，这些可以以本地化的方式集成到部件尺寸中，因此这些合金的相当大的强度潜力（目前仍未得到充分利用）可以用于要求苛刻的轻量化设计应用。通过将(i)疲劳过程中（ii）不同加载条件下（iii）铸铝微观组织的特定变化与(i)积分和局部解析无损损伤监测（原位）相结合，损伤机制可用于进一步开发和适应基于机制的疲劳寿命预测和应力适宜合金设计的建模方法。

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.