arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2401.01147cond-mat.mtrl-sci
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利用人工智能自动分割大型图像数据集,用于显微组织表征、损伤分析和高通量建模输入

Automated Segmentation of Large Image Datasets using Artificial Intelligence for Microstructure Characterisation, Damage Analysis and High-Throughput Modelling Input

Setareh Medghalchi, Joscha Kortmann, Sang-Hyeok Lee, Ehsan Karimi, Ulrich Kerzel, Sandra Korte-Kerzel

AI总结:

针对阈值法难以分割复杂显微组织的问题,提出基于 nnU-Net 与 GAN 的两种 AI 分割方法,并在双相钢 SEM 图像中验证其泛化性及面向表征、损伤分析和有限元建模的价值。

摘要:

常用材料的许多性能由其显微组织驱动,而显微组织会受到成分和制造工艺的影响。为优化未来的材料,理解显微组织至关重要。本文提出两种基于人工智能的新方法,可对阈值分割等简单数值方法不适用的显微组织相进行分割:一种基于 nnU-Net 神经网络,另一种基于生成对抗网络(GAN)。以双相钢的大型全景扫描电子显微镜图像为案例研究,我们展示了两种方法如何有效分割复杂的显微组织细节,包括马氏体、铁素体和损伤位置,以供后续分析。两种方法在一系列图像尺寸和条件下均表现出显著的泛化能力,包括具有不同相配置的热处理显微组织。nnU-Net 在绘制大图像区域方面表现优异;相比之下,基于 GAN 的方法在较小图像上运行可靠,并对分割过程提供更强的逐步控制和灵活性。本研究强调了分割后的显微组织数据在多种用途上的优势,例如计算相分数、通过有限元模拟对材料行为建模,以及对损伤位置及其周围显微组织的局部性能进行几何分析。

英文摘要:

Many properties of commonly used materials are driven by their microstructure, which can be influenced by the composition and manufacturing processes. To optimise future materials, understanding the microstructure is critically important. Here, we present two novel approaches based on artificial intelligence that allow the segmentation of the phases of a microstructure for which simple numerical approaches, such as thresholding, are not applicable: One is based on the nnU-Net neural network, and the other on generative adversarial networks (GAN). Using large panoramic scanning electron microscopy images of dual-phase steels as a case study, we demonstrate how both methods effectively segment intricate microstructural details, including martensite, ferrite, and damage sites, for subsequent analysis. Either method shows substantial generalizability across a range of image sizes and conditions, including heat-treated microstructures with different phase configurations. The nnU-Net excels in mapping large image areas. Conversely, the GAN-based method performs reliably on smaller images, providing greater step-by-step control and flexibility over the segmentation process. This study highlights the benefits of segmented microstructural data for various purposes, such as calculating phase fractions, modelling material behaviour through finite element simulation, and conducting geometrical analyses of damage sites and the local properties of their surrounding microstructure.

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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