Institute of Physical Metallurgy and Metal Physics, RWTH Aachen University, Aachen, Germany.
IUBH University of Applied Sciences, Bad Honnef, Germany.
PLoS One. 2019 May 8;14(5):e0216493. doi: 10.1371/journal.pone.0216493. eCollection 2019.
High performance materials, from natural bone over ancient damascene steel to modern superalloys, typically possess a complex structure at the microscale. Their properties exceed those of the individual components and their knowledge-based improvement therefore requires understanding beyond that of the components' individual behaviour. Electron microscopy has been instrumental in unravelling the most important mechanisms of co-deformation and in-situ deformation experiments have emerged as a popular and accessible technique. However, a challenge remains: to achieve high spatial resolution and statistical relevance in combination. Here, we overcome this limitation by using panoramic imaging and machine learning to study damage in a dual-phase steel. This high-throughput approach now gives us strain and microstructure dependent insights into the prevalent damage mechanisms and allows the separation of inclusions and deformation-induced damage across a large area of this heterogeneous material. Aiming for the first time at automated classification of the majority of damage sites rather than only the most distinct, the new method also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality.
高性能材料，从天然骨到古代大马士革钢再到现代高温合金，通常在微观尺度上具有复杂的结构。它们的性能超过了单个组件的性能，因此，要想提高其性能，就需要超越对单个组件行为的理解。电子显微镜在揭示共变形的最重要机制方面发挥了重要作用，原位变形实验已成为一种流行且易于使用的技术。然而，仍然存在一个挑战：如何在结合高空间分辨率和统计学相关性的同时实现这一目标。在这里，我们通过使用全景成像和机器学习来研究双相钢中的损伤，克服了这一限制。这种高通量方法现在使我们能够深入了解应变和微观结构对流行损伤机制的影响，并能够在这种异质材料的大面积上分离夹杂和变形诱导的损伤。该新方法旨在首次对大多数损伤部位进行自动化分类，而不仅仅是对最明显的损伤部位进行分类，这也促使我们将当前的研究从对明显损伤部位的典型案例的解释扩展到对不那么明显的现实情况的研究。
