3D-printed continuous carbon fiber reinforced composites (CCFRCs) are promising for various engineering applications due to high strength-to-weight ratios and design flexibility. However, the large variations in their mechanical properties pose a considerable challenge to their widespread applications. Here we develop a multiscale Bayesian method to quantify uncertainties in the constitutive parameters and microstructural parameters of 3D-printed CCFRCs. Based on the characterized microstructure of CCFRCs, a multiscale micromechanical model is developed to reveal the relationship between the properties of constituent materials, the microstructural parameters, and the macroscopic constitutive parameters. Furthermore, the joint posterior probability distribution of these parameters is formulated, and the Markov Chain Monte Carlo method (MCMC) is used to compute the posterior distributions of constitutive and microstructural parameters, enabling assessment of parameter uncertainty, correlation, and model calibration error. The inferred microstructural parameters are consistent with those measured by experiments. The posterior predictive distributions of the constitutive response are further computed to validate the probability model. Our method quantifies uncertainties in the constitutive parameters of 3D-printed CCFRCs and identifies their origins, which can optimize constituent material properties and microstructural parameters to achieve more robust composites.

中文翻译：

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一种多尺度贝叶斯方法，用于量化 3D 打印复合材料本构和微观结构参数的不确定性


3D 打印的连续碳纤维增强复合材料 （CCFRC） 具有高强度重量比和设计灵活性，有望用于各种工程应用。然而，其机械性能的巨大差异对其广泛的应用构成了相当大的挑战。在这里，我们开发了一种多尺度贝叶斯方法来量化 3D 打印 CCFRCs 的本构参数和微观结构参数的不确定性。基于 CCFRCs 的特征微观结构，开发了一个多尺度微观力学模型来揭示组成材料的性能、微观结构参数和宏观本构参数之间的关系。此外，还建立了这些参数的联合后验概率分布，并使用马尔可夫链蒙特卡洛法 （MCMC） 计算本构和微观结构参数的后验分布，从而能够评估参数不确定性、相关性和模型校准误差。推断的微观结构参数与实验测量的参数一致。进一步计算本构反应的后验预测分布以验证概率模型。我们的方法量化了 3D 打印 CCFRC 本构参数的不确定性并确定了它们的来源，这可以优化组成材料特性和微观结构参数，以实现更坚固的复合材料。