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Initial failure strength prediction of woven composites using a new yarn failure criterion constructed by deep learning

机译：使用深度学习构造的新纱线破坏准则来预测编织复合材料的初始破坏强度

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摘要

A new failure criterion for fiber tows (i.e. yarns) is developed based on a micromechanical model using the mechanics of structure genome (MSG) and a deep learning neural network model. The proposed failure criterion can be applied to yarns in mesoscale textile composites modeling while capturing the failure initiation at the fiber and matrix level. A plain weave fiber reinforced composite material example is used to compute the initial failure strength constants of a woven lamina based on the proposed yarn failure criterion. To study the accuracy and efficiency of the failure criterion, a comparison to a meso-micro coupled model explicitly capturing the failure initiation at fiber and matrix level is performed. Moreover, the differences between the mesoscale modeling results based on the proposed criterion and other yarn failure criteria (i.e. maximum stress, Tsai-Wu, and Hashin) are studied. Lastly, the failure envelope analysis of the mesoscale plain weave example is carried out using the MSG solid model to further demonstrate the accuracy and efficiency of the new yarn failure criterion under combined loading conditions.
机译：基于微机械模型，使用结构基因组（MSG）的机理和深度学习神经网络模型，开发了纤维束（即纱线）的新失效准则。所提出的失效准则可应用于中尺度纺织品复合材料建模中的纱线，同时捕获纤维和基体水平的失效始端。基于所提出的纱线破坏准则，以平纹纤维增强复合材料为例来计算编织层的初始破坏强度常数。为了研究失效准则的准确性和效率，进行了与中微耦合模型的比较，该模型明确捕获了光纤和基体级别的失效始端。此外，研究了基于建议标准和其他纱线破坏标准（即最大应力，Tsai-Wu和Hashin）的中尺度建模结果之间的差异。最后，使用MSG实体模型对中尺度平纹实例的破坏包络线进行了分析，以进一步证明在组合载荷条件下新纱线破坏准则的准确性和效率。

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