arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1809.09657cond-mat.mtrl-sci
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使用人工智能对DP钢的损伤进行高分辨率但统计相关的分析

High-resolution, yet statistically relevant, analysis of damage in DP steel using artificial intelligence

Carl Kusche, Tom Reclik, Martina Freund, Talal Al-Samman, Ulrich Kerzel, Sandra Korte-Kerzel

摘要:

高性能材料,从天然骨到古代的碳钢到现代的超级合金,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,基于知识的改进需要对组件的单个行为之外的理解。电子显微镜已在解开最重要的机制,共同变形和原位变形实验已成为一种流行的和可访问的技术。然而,挑战仍然存在:同时实现高空间分辨率和统计相关性。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来克服这一限制。这种高通量方法不仅为我们提供了这种异质材料大面积应变和微观结构依赖的见解,而且还鼓励我们将当前的研究扩展到对不同损伤部位的示例性案例的解释,以实现不太明确的现实。

英文摘要:

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 not only gives us strain and microstructure dependent insights across a large area of this heterogeneous material, but also encourages us to expand current research past interpretation of exemplary cases of distinct damage sites towards the less clear-cut reality.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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