A physics-informed machine learning model for global-local stress prediction of open holes with finite-width effects in composite structures

IF 2.3 3区 材料科学 Q3 MATERIALS SCIENCE, COMPOSITES
Omar Ahmed Imran Azeem, Silvestre T Pinho
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引用次数: 0

Abstract

Fast and accurate methods are required to predict stresses in the vicinity of open and closed holes in composite structures, especially in a global-local modelling context as applied during the design of airframe structures. Fast analytical solutions for infinite-width anisotropic plates with open holes do not consider finite-width effects. Heuristic methods and semi-analytical solutions can be used to towards addressing such effects. To improve the accuracy and speed of these respective methods, we use machine learning (ML) methods trained on high-fidelity finite element analyses to make finite-width corrections. However, such methods require large amounts of training data to reduce errors to satisfactory levels. Therefore, in this study, the fusion of analytical solutions with machine learning is performed. We develop an analytical solution-informed ML model that is as fast as an analytical solution and superior in accuracy to analytical solutions with heuristic finite-width scaling. Our informed ML model offers accuracies equal to analytical solutions for the infinite-width case, and it is capable for use in a global-local modelling context, under uniaxial and biaxial loading. Our informed ML model outperforms prediction accuracy across all cases compared to uninformed ML models and requires a significantly lower size training dataset size.
用于预测复合材料结构中具有有限宽度效应的开孔的全局-局部应力的物理信息机器学习模型
需要快速准确的方法来预测复合材料结构中开孔和闭孔附近的应力,特别是在机身结构设计中应用的全局局部建模环境下。对于具有开孔的无穷宽各向异性板材,快速分析解决方案并未考虑有限宽度效应。启发式方法和半解析解法可用于解决此类效应。为了提高这些方法的准确性和速度,我们使用在高保真有限元分析中训练的机器学习(ML)方法来进行有限宽度修正。然而,这些方法需要大量的训练数据才能将误差降低到令人满意的水平。因此,在本研究中,我们将分析求解与机器学习进行了融合。我们开发了一种以分析解决方案为基础的 ML 模型,其速度与分析解决方案不相上下,精度则优于采用启发式有限宽度缩放的分析解决方案。我们的知情 ML 模型在无限宽度情况下的精确度与分析解决方案相当,并且能够在单轴和双轴载荷条件下用于全局-局部建模。与无信息的 ML 模型相比,我们的有信息 ML 模型在所有情况下的预测精度都更高,而且所需的训练数据集规模也大大降低。
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来源期刊
Journal of Composite Materials
Journal of Composite Materials 工程技术-材料科学:复合
CiteScore
5.40
自引率
6.90%
发文量
274
审稿时长
6.8 months
期刊介绍: Consistently ranked in the top 10 of the Thomson Scientific JCR, the Journal of Composite Materials publishes peer reviewed, original research papers from internationally renowned composite materials specialists from industry, universities and research organizations, featuring new advances in materials, processing, design, analysis, testing, performance and applications. This journal is a member of the Committee on Publication Ethics (COPE).
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