论文标题
机器学习和计算机视觉技术可预测颗粒复合材料的热性能
Machine Learning and Computer Vision Techniques to Predict Thermal Properties of Particulate Composites
论文作者
论文摘要
复合材料和多孔介质的准确热分析需要小规模详细表征局部热性能。对于某些重要的应用，例如锂离子电池，操作过程中的性质变化使分析更具挑战性，因此需要快速表征。我们提出了一种基于实际微图像的颗粒复合材料的热性能的新方法。我们的基于计算机视觉的方法从2D SEM图像的堆栈构造了3D图像，然后从随机位置的重建图像中提取几个代表性的元素量（REVS），从而导致不同转速具有一系列几何特征。深度学习算法是基于卷积神经网的设计，以采用几何形状，并导致Rev的有效电导率。网络的训练是通过两种方法进行的：首先，基于实现细网格的粗网格，该网格使用细网格的平均电导率值，并从细网格的DNS解决方案中产生的有效电导率。另一种方法在不同方向上的每个Rev的横截面上使用电导率值。基于平均的培训结果表明，在网络中使用更粗的网格对网络错误没有有意义的影响。但是，它最多将训练时间降低到三个数量级。我们表明，一个通用网络可以使用不同类型的电极图像做出准确的预测，代表几何和成分的差异。此外，基于平均的培训比基于横截面的培训更准确。在预测热渗透时实现机器学习技术的鲁棒性的研究表明，基于体积分数的预测，预测误差几乎是误差的一半。
Accurate thermal analysis of composites and porous media requires detailed characterization of local thermal properties in small scale. For some important applications such as lithium-ion batteries, changes in the properties during the operation makes the analysis even more challenging, necessitating a rapid characterization. We propose a new method to characterize the thermal properties of particulate composites based on actual micro-images. Our computer-vision-based approach constructs 3D images from stacks of 2D SEM images and then extracts several representative elemental volumes (REVs) from the reconstructed images at random places, which leads to having a range of geometrical features for different REVs. A deep learning algorithm is designed based on convolutional neural nets to take the shape of the geometry and result in the effective conductivity of the REV. The training of the network is performed in two methods: First, based on implementing a coarser grid that uses the average values of conductivities from the fine grid and the resulted effective conductivity from the DNS solution of the fine grid. The other method uses conductivity values on cross sections from each REV in different directions. The results of training based on averaging show that using a coarser grid in the network does not have a meaningful effect on the network error; however, it decreases the training time up to three orders of magnitude. We showed that one general network can make accurate predictions using different types of electrode images, representing the difference in the geometry and constituents. Moreover, training based on averaging is more accurate than training based on cross sections. The study of the robustness of implementing a machine learning technique in predicting the thermal percolation shows the prediction error is almost half of the error from predictions based on the volume fraction.
