Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites
Advanced Deep Learning‐Based 3D Microstructural Characterization of Multiphase Metal Matrix Composites
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DOI：
10.1002/adem.201901197
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发表时间：
2020-01
影响因子：
3.6
通讯作者：
S. Evsevleev;S. Paciornik;G. Bruno
中科院分区：
文献类型：
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作者：
S. Evsevleev;S. Paciornik;G. Bruno
定量分析金属基复合材料的微观组织特征是了解金属基复合材料微观力学行为的关键，也是金属基复合材料实际应用的前提。本文采用同步加速器X射线计算机断层扫描（SXCT）对五相MMC进行了三维微观结构表征。使用U - net架构的全卷积神经网络，展示了基于高级深度学习的SXCT数据中所有单个阶段分割的工作流程。用少量的训练数据实现了较高的分割精度。这使得提取前所未有的精确微观结构参数（例如，体积分数和颗粒形状）输入，例如，在微观力学模型中。
The quantitative analysis of microstructural features is a key to understanding the micromechanical behavior of metal matrix composites (MMCs), which is a premise for their use in practice. Herein, a 3D microstructural characterization of a five‐phase MMC is performed by synchrotron X‐ray computed tomography (SXCT). A workflow for advanced deep learning‐based segmentation of all individual phases in SXCT data is shown using a fully convolutional neural network with U‐net architecture. High segmentation accuracy is achieved with a small amount of training data. This enables extracting unprecedently precise microstructural parameters (e.g., volume fractions and particle shapes) to be input, e.g., in micromechanical models.