High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel
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DOI：
10.1017/s1431927618015635
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发表时间：
2019-02-01
影响因子：
2.8
通讯作者：
Holm, Elizabeth A.
中科院分区：
文献类型：
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作者：
DeCost, Brian L.;Lei, Bo;Holm, Elizabeth A.
我们应用深度卷积神经网络分割模型，为通常手动和主观评估的复杂微结构实现新的自动微结构分割应用。我们探讨了两个微观结构分割任务，在一个公开的高碳钢微观结构数据集：分割球化基体中的碳化物颗粒，并分割更大的视野，具有晶界碳化物，球化颗粒基体，无颗粒晶界洁净区，和魏氏合金。我们还演示了如何结合联合收割机这些数据驱动的微观结构分割模型，以获得经验的结晶粒度和洁净区的宽度分布，从更复杂的显微照片包含多个微观成分。完整的注释数据集可在materialsdata.nist.gov上查阅。
We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstatten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.