The ultimate strength prediction of textile composite materials requires high-fidelity FE modeling with information-passing multiscale schemes and damage initiation and propagation algorithms. The numerical demand of this procedure together with the complexity of the observed response surface, hampers the quantification of uncertainties contributing to the scatter of strength values. This study proposes a surrogate methodology able to efficiently emulate the nonlinear multiscale procedure, based on a combination of artificial neural networks and Kriging modeling under a variable-fidelity framework. A triaxially braided textile under longitudinal tension is used as a use-case and the methodology is employed to identify the most critical parameters in terms of variance via a global sensitivity analysis technique. Results show strong interaction effects between the uncertain parameters. The approach is non-intrusive and can be easily extended to other types of textiles and load cases.

纺织品复合材料的极限强度预测需要具有保真度的FE建模以及信息传递的多尺度方案以及损伤的引发和传播算法。此过程的数字要求以及所观察到的响应面的复杂性阻碍了不确定性的量化，从而导致强度值的分散。这项研究提出了一种替代方法，能够在可变逼真度框架下结合人工神经网络和Kriging建模，有效地模拟非线性多尺度过程。下三轴编织的纺织品使用纵向张力作为用例，并通过全局敏感性分析技术，采用该方法来确定方差方面最关键的参数。结果表明不确定参数之间有很强的交互作用。这种方法是非侵入性的，可以轻松地扩展到其他类型的纺织品和负载箱。