This work represents the first step towards the application of machine learning techniques in the prediction of statistical design allowables of composite laminates. Building on data generated analytically, four machine algorithms (XGBoost, Random Forests, Gaussian Processes and Artificial Neural Networks) are used to predict the notched strength of composite laminates and their statistical distribution, associated to the uncertainty related to the material properties and geometrical features. This work focuses not only on the so-called Legacy Quad Laminates (0°/90°/$\pm$45°), typically used in the design of composite aerostructures, but also on the newer concept of double-double (or double-angle ply) laminates. Very good representations of the design space, translating in low generalization relative errors of around $\pm$10%, and very accurate representations of the distributions of notched strengths around single design points and corresponding B-basis allowables are obtained. All machine learning algorithms, with the exception of the Random Forests, show very good performances, with Gaussian Processes outperforming the others for very small number of data points while Artificial Neural Networks have better performance for larger training sets. This work serves as basis for the prediction of first-ply failure, ultimate strength and failure mode of composite specimens based on non-linear finite element simulations, providing further reduction of the computational time required to virtually obtain the design allowables for composite laminates.

这项工作代表了将机器学习技术应用于预测复合材料层压板的统计设计允许值的第一步。基于分析生成的数据，四种机器算法（XGBoost、随机森林、高斯过程和人工神经网络）用于预测复合材料层压板的缺口强度及其统计分布，与材料特性和几何特征相关的不确定性相关。这项工作不仅侧重于所谓的 Legacy Quad Laminates (0°/90°/$\pm$45°），通常用于复合材料航空结构的设计，但也用于双双（或双角层）层压板的新概念。设计空间的非常好的表示，转化为大约的低泛化相对误差$\pm$10%，并且非常准确地表示了单个设计点周围的缺口强度分布和相应的 B 基许用值。除了随机森林之外，所有机器学习算法都表现出非常好的性能，高斯过程在极少数数据点上的表现优于其他算法，而人工神经网络在较大的训练集上有更好的性能。这项工作作为基于非线性有限元模拟预测复合材料试样的第一层失效、极限强度和失效模式的基础，进一步减少了虚拟获得复合层压板设计允许所需的计算时间。