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arXiv 2112.03072cond-mat.mtrl-scieess.IV
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深度学习用于EBSD地图中的自动相位分割。双相钢显微组织的实例研究

Deep Learning for automated phase segmentation in EBSD maps. A case study in Dual Phase steel microstructures

T Martinez Ostormujof, Rrp Purushottam Raj Purohit, S Breumier, Nathalie Gey, M Salib, L Germain

摘要:

电子背散射衍射(EBSD)为钢中相变产物的鉴别提供了重要信息。这项任务通常由专家执行,专家具有高度的主观性,需要时间和精力。在本文中,我们质疑卷积神经网络(CNN)是否能够从基于EBSD的数据中提取有意义的特征,以便自动对钢显微组织中的当前相进行分类。所选的研究案例是铁素体-马氏体的歧视和U-网络已被选为网络架构的工作。当输入原始方向数据时,已经获得了约95%的像素精度,而通过方向导出的参数(例如核平均方向误差(KAM)或图案质量)已经达到了约98%。与文献中其他可用的相位鉴别方法相比,本文提出的模型在更短的时间内提供了更高的精度。这些令人鼓舞的结果为研究更复杂的钢组织提供了可能性。

英文摘要:

Electron Backscattering Diffraction (EBSD) provides important information to discriminate phase transformation products in steels. This task is conventionally performed by an expert, who carries a high degree of subjectivity and requires time and effort. In this paper, we question if Convolutional Neural Networks (CNNs) are able to extract meaningful features from EBSD-based data in order to automatically classify the present phases within a steel microstructure. The selected case of study is ferrite-martensite discrimination and U-Net has been selected as the network architecture to work with. Pixel-wise accuracies around ~95% have been obtained when inputting raw orientation data, while ~98% has been reached with orientation-derived parameters such as Kernel Average Misorientation (KAM) or pattern quality. Compared to other available approaches in the literature for phase discrimination, the models presented here provided higher accuracies in shorter times. These promising results open a possibility to work on more complex steel microstructures.

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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