The transverse strength of unidirectional carbon fiber reinforced polymer (UD-CFRP) composites is a high dimensional and nonlinear function of microstructure due to the wide scatter in mechanical properties and complex failure mechanisms, which is a challenging task to develop a general microstructure dependent strength criterion (MDSC) in theory or computation. Volume fraction and distribution of fibers are among the crucial influencing factors. A computational micromechanics and machine learning (ML) combined method is employed to uncover the transverse mechanical response of UD-CFRP composites. High-throughput finite element analyses (FEA) are performed to obtain the transverse behaviors of composites with varying fiber distribution and volume fraction under different loading states. They showed that fiber distribution has different effects on strengths in different failure modes, while the failure modes are closely related to loading states and fiber volume fractions. An ML model is then trained to characterize the relations between composite microstructure and composite strength. Then, the transverse strengths of 1000 new microstructures are predicted, which shows good agreement with FEA results, so that the MDSC of UD-CFRP is constructed by fully accounting for the influence of fiber distribution. Reliability of the method is verified by considering composites with various fiber volume fractions.

中文翻译：

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通过计算微观力学和机器学习计算 UD-CFRP 复合材料的微观结构相关横向强度标准

由于机械性能的广泛分散和复杂的失效机制，单向碳纤维增强聚合物（UD-CFRP）复合材料的横向强度是微观结构的高维和非线性函数，这是开发通用的微观结构相关强度准则的一项具有挑战性的任务（MDSC）理论或计算。纤维的体积分数和分布是关键的影响因素之一。采用计算微力学和机器学习 (ML) 相结合的方法来揭示 UD-CFRP 复合材料的横向机械响应。进行高通量有限元分析（FEA）以获得不同载荷状态下具有不同纤维分布和体积分数的复合材料的横向行为。他们表明，纤维分布对不同失效模式下的强度有不同的影响，而失效模式与载荷状态和纤维体积分数密切相关。然后训练机器学习模型来表征复合材料微观结构和复合材料强度之间的关系。然后，预测了1000个新微结构的横向强度，与有限元分析结果吻合良好，从而充分考虑了纤维分布的影响构建了UD-CFRP的MDSC。通过考虑具有不同纤维体积分数的复合材料来验证该方法的可靠性。