Microstructural damage can occur during metal forming, but how and where this happens vary with the local microstructure and strain path. Large-scale analysis of such damage mechanisms is particularly important in advanced steels with a heterogeneous phase distribution. In our previous work, we demonstrated that deep learning enables a mechanism-based, statistical analysis by classifying many individual damage sites. The aim of this work is to generalize this approach to different stress states, e.g., biaxial instead of uniaxial tension, without manually labeling a large new ground-truth dataset of further micrographs and to thereby assess the changes in damage behavior with respect to stress state. Data augmentation and regularization allow us to directly apply our approach to the new, biaxial loading case. Overall, the network performance could be greatly improved and an analysis of changes in damage behavior, here the martensite crack angle distribution, with stress state can now be performed.

中文翻译：

---

使用深度学习的双相钢损伤分析：通过图像数据增强从单轴到双轴应变条件的转换

金属成形过程中可能会发生微观结构损伤，但发生的方式和位置因局部微观结构和应变路径而异。这种损伤机制的大规模分析对于具有异相分布的高级钢尤为重要。在我们之前的工作中，我们证明了深度学习通过对许多单个损坏部位进行分类来实现基于机制的统计分析。这项工作的目的是将这种方法推广到不同的应力状态，例如，双轴而不是单轴张力，而无需手动标记进一步显微照片的大型新地面实况数据集，从而评估损伤行为相对于应力状态的变化. 数据增强和正则化使我们能够将我们的方法直接应用于新的双轴加载情况。全面的，