一种估算晶粒长大动力学的替代方法
An alternate approach for estimating grain-growth kinetics
AI总结：
本文提出通过计数并追踪三叉与四叉结点数量变化来估算晶粒长大速率，借助回归目标检测算法减少复杂晶界图像处理，并在多相多晶组织中验证其与传统方法一致。
摘要：
晶粒长大速率有助于使多晶材料获得所需性能，传统上是通过测量晶粒尺寸，并在反映时间演化的显微图像中追踪其变化来估算的。采用这种传统方法的技术需要在晶粒与分隔晶粒的界面之间作出绝对区分，才能得到准确结果。边缘检测、分割和其他深度学习算法正越来越多地被用于精确揭示晶界网络及相关晶粒。本文提出了一种测量晶粒长大动力学的替代方法，可减少对高级图像处理的需求。在当前技术中，晶粒长大速率是通过“计数”三叉（和四叉）结点的数量，并监测其在显微组织演化过程中的变化来确定的。这种基于结点的处理方式转移了关注点，降低了对定义清晰的晶界网络的重要性，从而也减少了对揭示这些网络的复杂技术的依赖。本文扩展了一种基于回归的目标检测算法，以识别并计数多晶显微组织中的结点。通过考察结点数量随时间的变化，随后确定长大速率。在广泛的多相多晶显微组织中，由本研究提出的基于结点的方法估算的长大动力学与传统处理的结果令人信服地一致。除了提供一种新颖的晶粒长大测量技术外，本研究附带的分析还揭示了三叉结点计数在渐进演化过程中与拓扑事件相容的趋势。通过其底层算法，本方法为原位研究中监测晶粒长大提供了一种很有前景的选择。
英文摘要：
Rate of grain growth, which aides in achieving desired properties in polycrystalline materials, is conventionally estimated by measuring the size of grains and tracking its change in micrographs reflecting the temporal evolution. Techniques adopting this conventional approach demand an absolute distinction between the grains and the interface separating them to yield an accurate result. Edge-detection, segmentation and other deep-learning algorithms are increasingly adopted to expose the network of boundaries and the associated grains precisely. An alternate approach for measuring grain-growth kinetics, that curtails the need for advanced image-processing treatment, is presented in this work. Grain-growth rate in the current technique is ascertained by \textit{counting} the number of triple-( and quadruple-) junctions, and monitoring its change during the microstructural evolution. The shifted focus of this junction-based treatment minimises the significance of a well-defined grain-boundary network, and consequently, the involvement of the sophisticated techniques that expose them. A regression-based object-detection algorithm is extended to realise, and count, the number of junctions in polycrystalline microstructures. By examining the change in the number of junctions with time, the growth rate is subsequently determined.Growth kinetics estimated by the present junction-based approach, across a wide-range of multiphase polycrystalline microstructures, agree convincingly with the outcomes of the conventional treatment.Besides offering a novel technique for grain-growth measurement, the analysis accompanying the current work unravels a trend, compatible with the topological events, in the progressive evolution of the triple-junctions count. The present approach, through its underlying algorithm, provides a promising option for monitoring grain-growth during in-situ investigations.
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