Three-Dimensional Characterization of Deformation-induced Damage in Dual Phase Steel using Deep Learning

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. https://doi.org/10.1016/j.matdes.2023.112108具有多相微观结构的高性能薄板金属在成形过程中，不仅其组成相内部会产生变形诱导损伤，尤为重要的是，相界区域同样会出现这类损伤。为了从物理过程层面捕捉变形过程中三维（3D）尺度下的损伤及其随机特性，目前仍需攻克两项挑战：其一，衔接高分辨率分析与大尺度分析以纳入统计数据；其二，在大尺度下实现适配亚微米级孔洞的三维表征。本研究展示了如何通过全景扫描电子显微镜（SEM）、金相连续切片技术以及深度学习辅助自动图像分析来实现上述目标。该方法可整合数千个独立损伤位点的活跃损伤机制三维演化过程、体信息与环境信息。本研究还评估了二维分析中可能存在的制样伪影。总体而言，针对本研究所用的双相（DP800）钢材料，我们发现马氏体开裂是变形诱导损伤的主要成因，但并非唯一成因；同时在损伤密度的定量对比中，金相制样过程引入的额外表面损伤密度远高于单轴拉伸工序中常规产生的损伤密度。 https://doi.org/10.1016/j.matdes.2023.112108