深度学习用于定量动态碎裂分析
Deep Learning for Quantitative Dynamic Fragmentation Analysis
AI总结：
本研究开发了基于图像的卷积神经网络，用于超高速光学成像下不透明脆性材料碎裂行为的定量时间分辨测量，扩展了U-net模型，通过监督学习训练二元、3类和5类模型，以量化防护应用中结构材料的失效过程。
摘要：
我们开发了一种基于图像的卷积神经网络（CNN），适用于使用超高速光学成像对不透明脆性材料的碎裂行为进行定量时间分辨测量。该模型扩展了先前关于U-net模型的工作，我们使用监督学习在实验测量的各种不透明结构陶瓷材料的动态断裂实验上训练了二元、3类和5类模型，这些陶瓷材料粘附在透明聚合物（聚碳酸酯或丙烯酸）背衬材料上。实验研究的完整细节不在本手稿的范围内，但简而言之，几种不同的陶瓷材料受到空间和时间变化的机械载荷，以诱发非弹性变形和断裂过程，这些过程以高达5 MHz的频率通过高速光学成像记录。这些实验提供了丰富多样的数据集，包括静态和动态断裂中常见的许多断裂模式，包括锥形裂纹、中间裂纹、粉碎以及涉及多个碎裂模式同时激活和传播的复杂组合失效模式。虽然这里呈现的训练数据来自动态碎裂实验，但这项研究适用于这些材料的静态加载，因为在这些材料中，裂纹速度通常高于每秒一公里，无论加载速率如何，都在1-10 km/s的数量级。我们相信这里提出的方法将有助于量化防护应用中结构材料的失效过程，并可用于直接验证设计中使用的工程模型。
英文摘要：
We have developed an image-based convolutional neural network (CNN) that is applicable for quantitative time-resolved measurements of the fragmentation behavior of opaque brittle materials using ultra-high speed optical imaging. This model extends previous work on the U-net model, where we trained binary, 3 and 5 class models using supervised learning on experimentally measured dynamic fracture experiments on various opaque structural ceramic materials that were adhered on transparent polymer (polycarbonate or acrylic) backing materials. Full details of the experimental investigations are outside the scope of this manuscript but briefly, several different ceramics were loaded using spatially and time-varying mechanical loads to induce inelastic deformation and fracture processes that were recorded at frequencies as high as 5 MHz using high speed optical imaging. These experiments provided a rich and diverse dataset that includes many of the common fracture modes found in static and dynamic fracture including cone cracking, median cracking, comminution, and combined complex failure modes that involve effectively simultaneous activation and propagation of multiple fragmentation modes. While the training data presented here was obtained from dynamic fragmentation experiments, this study is applicable to static loading of these materials as the crack speeds typically higher a kilometer per second in these materials are on the order of 1-10 km/s regardless of the loading rate. We believe the methodologies presented here will be useful in quantifying the failure processes in structural materials for protection applications and can be used for direct validation of engineering models used in design.
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