High performance sheet metals with a multi-phase microstructure suffer from deformation induced damage formation during forming in the constituent phases but importantly also where these intersect. To capture damage in terms of the physical processes in three dimensions (3D) and its stochastic nature during deformation, two challenges remain to be tackled: First, bridging high resolution analysis towards large scales to consider statistical data and, second, characterising in 3D with a resolution appropriate for sub-micron sized voids at a large scale. Here, we present how this can be achieved using panoramic scanning electron microscopy (SEM), metallographic serial sectioning, and deep-learning assisted automatic image analysis. This brings together the 3D evolution of active damage mechanisms with volumetric and environmental information for thousands of individual damage sites. We also assess potential surface preparation artefacts in 2D analyses. Overall, we find that for the material considered here, a dual phase (DP800) steel, martensite cracking is the dominant but not sole origin of deformation induced damage and that for a quantitative comparison of damage density, metallographic preparation can induce additional surface damage density far exceeding what is commonly induced between uniaxial straining steps.

具有多相微观结构的高性能金属板材在各组成相的成形过程中会遭受变形引起的损伤，但在这些相交的地方也很重要。为了捕捉三维 (3D) 物理过程的损伤及其变形过程中的随机性质，仍然需要解决两个挑战：首先，将高分辨率分析与大尺度结合起来以考虑统计数据，其次，用 3D 来表征适用于大规模亚微米尺寸孔隙的分辨率。在这里，我们介绍如何使用全景扫描电子显微镜 (SEM)、金相连续切片和深度学习辅助自动图像分析来实现这一目标。这将主动损伤机制的 3D 演化与数千个单独损伤部位的体积和环境信息结合在一起。我们还评估二维分析中潜在的表面处理伪影。总的来说，我们发现对于这里考虑的材料，双相 (DP800) 钢，马氏体开裂是变形引起的损伤的主要来源，但不是唯一的来源，并且为了定量比较损伤密度，金相制备可以引起额外的表面损伤密度远远超过单轴应变步骤之间通常引起的。