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Predicting the effects of microstructure on matrix crack initiation in fiber reinforced ceramic matrix composites via machine learning

机译：通过机器学习预测微观结构对纤维增强陶瓷基复合材料中基质裂纹起始的影响

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摘要

A reduced-order, data-driven, probabilistic predictive model to quantify damage initiation in continuous SiC ceramic fiber SiC ceramic matrix composites (CMCs) at pertinent lengths scales using machine learning tools is proposed and explored. A novel framework is developed to characterize the influence of key stochastic microstructure attributes on matrix crack initiation. The approach is illustrated for the case of transverse crack initiation in the matrix surrounding fibers oriented perpendicular to the loading direction. A variety of stochastic microstructure attributes were considered including fiber spacing, fiber diameter, and coating thickness. Statistics of a commercial CMC microstructure were digitally represented and used to instantiate microstructures. In addition, discrete digital instantiations generated over a range of the distributed microstructural attributes were considered. The statistics of the distributed microstructure attributes were quantified using n-point statistics and reduced using principal component analysis. The elastic responses of the instantiated microstructures were characterized using finite element analysis (FEA). Results from the FEA were used as the ground truth to calibrate and validate a data-driven machine learning (ML) model. The quantified stochastic microstructure attributes were correlated with the statistics of the simulated damage response. The predictive capabilities of the model for a new microstructure class were demonstrated.
机译：提出了一种减少，数据驱动的概率预测模型，用于使用机器学习工具在相关长度尺度下量化连续SiC陶瓷纤维SiC陶瓷基复合材料（CMC）的损伤开始的预测模型。开发了一种新颖的框架，以表征关键随机微观结构属性对矩阵裂纹启动的影响。该方法示出了横向裂纹在垂直于装载方向定向的基质周围纤维中的情况。考虑了各种随机微结构属性，包括纤维间距，纤维直径和涂层厚度。商业CMC微观结构的统计数值表示并用于实例化微结构。另外，考虑了在一系列分布式微结构属性上产生的离散数字实例化。使用n点统计量化分布式微结构属性的统计数据，并使用主成分分析减少。使用有限元分析（FEA）表征实例化微结构的弹性响应。 FEA的结果被用作校准并验证数据驱动机器学习（ML）模型的原始真理。量化的随机微结构属性与模拟损伤响应的统计数据相关。证明了一种新的微观结构类模型的预测能力。

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