Advanced Steel Microstructural Classification by Deep Learning Methods.
Advanced Steel Microstructural Classification by Deep Learning Methods.
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DOI：
10.1038/s41598-018-20037-5
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发表时间：
2018-02-01
影响因子：
4.6
通讯作者：
Mücklich F
中科院分区：
文献类型：
--
作者：
Azimi SM;Britz D;Engstler M;Fritz M;Mücklich F
材料的内部结构称为微观结构。它存储了材料的起源，并决定了其所有的物理和化学性质。虽然微观结构表征广泛传播且众所周知，但微观结构分类主要由人类专家手动完成，这由于主观性而产生不确定性。由于微观结构可能是具有复杂子结构的不同相或成分的组合，因此其自动分类非常具有挑战性，并且只有少数先前的研究存在。以前的工作集中在设计和工程的功能，由专家和分类的微观结构分别从特征提取步骤。最近，深度学习方法通过从数据中学习特征以及分类步骤，在视觉应用中表现出了强大的性能。在这项工作中，我们提出了一种深度学习方法，用于低碳钢某些显微组织成分的显微组织分类。这种新方法通过全卷积神经网络（FCNN）结合最大投票方案进行逐像素分割。我们的系统实现了93.94%的分类准确率，大大优于48.89%的准确率的最先进的方法。除了我们的方法的强大性能之外，这条研究路线为钢铁质量评估的艰巨任务提供了一种更强大和更客观的方法。
The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.
影响因子：
1.1
作者：
Britz, D.;Hegetschweiler, A.;Muecklich, F.
通讯作者：
Muecklich, F.