论文标题
基于深度学习的凹痕分割，用于定量分离学
Deep Learning based Dimple Segmentation for Quantitative Fractography
论文作者
论文摘要
在这项工作中，我们尝试使用机器学习方法，尤其是神经网络来解决钛合金中的凹痕检测和分割的具有挑战性的问题。图像即使用扫描选举显微镜（SEM）获得分子仪。为了确定金属中骨折的原因，我们解决了分裂片段分割的问题，即使用监督的机器学习方法，即金属的断裂表面。确定裂缝的原因将有助于我们获得材料特性，机械性能预测和新型抗裂缝材料的开发。该方法还将有助于将断裂表面的地形与材料的机械性能相关联。与以前的其他方法相比，我们提出的小说模型取得了最佳性能。据我们所知，这是使用完全卷积神经网络具有自我注意力的分裂学上的第一项作品，以监督凹痕术的学习，尽管它也很容易扩展以说明脆性特征。
In this work, we try to address the challenging problem of dimple detection and segmentation in Titanium alloys using machine learning methods, especially neural networks. The images i.e. fractographs are obtained using a Scanning Election Microscope (SEM). To determine the cause of fracture in metals we address the problem of segmentation of dimples in fractographs i.e. the fracture surface of metals using supervised machine learning methods. Determining the cause of fracture would help us in material property, mechanical property prediction and development of new fracture-resistant materials. This method would also help in correlating the topography of the fracture surface with the mechanical properties of the material. Our proposed novel model achieves the best performance as compared to other previous approaches. To the best of our knowledge, this is one the first work in fractography using fully convolutional neural networks with self-attention for supervised learning of dimple fractography, though it can be easily extended to account for brittle characteristics as well.
