arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2303.10092cond-mat.mtrl-sci
收藏

从2D学习:基于2D微观结构截面的非均质材料3D有效性能的机器学习

Learning from 2D: machine learning of 3D effective properties of heterogeneous materials based on 2D microstructure sections

Guangyu Hu, Marat I. Latypov

摘要:

微观结构-性能关系是有效设计先进应用结构材料的关键。计算方法的进步使得能够使用3D模型(例如,有限元)。作为这些模型的输入所需的3D微结构数据通常从3D表征实验或基于来自2D微结构图像的统计的数字重建获得。在这项工作中,我们提出了机器学习(ML)的方法来直接从2D微观结构部分建模异质材料的有效属性。为此,我们将基于空间相关性和卷积神经网络的统计学习模型视为两种不同的ML策略。在这两种策略中,模型都是在综合生成的3D微观结构及其从微机械3D模拟获得的特性的数据集上训练的。在训练时,模型从2D微观结构截面预测属性。所提出的模型的优点是,他们只需要2D部分,其实验采集是更容易相比,3D表征。此外,本模型不需要3D微结构的数字重建。

英文摘要:

Microstructure--property relationships are key to effective design of structural materials for advanced applications. Advances in computational methods enabled modeling microstructure-sensitive properties using 3D models (e.g., finite elements) based on microstructure representative volumes. 3D microstructure data required as input to these models are typically obtained from either 3D characterization experiments or digital reconstruction based on statistics from 2D microstructure images. In this work, we present machine learning (ML) approaches to modeling effective properties of heterogeneous materials directly from 2D microstructure sections. To this end, we consider statistical learning models based on spatial correlations and convolutional neural networks as two distinct ML strategies. In both strategies, models are trained on a dataset of synthetically generated 3D microstructures and their properties obtained from micromechanical 3D simulations. Upon training, the models predict properties from 2D microstructure sections. The advantage of the presented models is that they only need 2D sections, whose experimental acquisition is more accessible compared to 3D characterization. Furthermore, the present models do not require digital reconstruction of 3D microstructures.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑