A multiscale Bayesian method to quantify uncertainties in constitutive and microstructural parameters of 3D-printed composites

• Published in: Journal of the mechanics and physics of solids 2024-12, Vol.193, p.105881, Article 105881
• Main Authors: Hong, Xiang, Wang, Peng, Yang, Weidong, Zhang, Junming, Chen, Yonglin, Li, Yan
• Format: Article
• Language: English
• Subjects: 3D-printed CCFRCs; Bayesian calibration; Multiscale micromechanical model; Uncertainty quantification
• ISSN: 0022-5096
• DOI: 10.1016/j.jmps.2024.105881

•The microstructural parameters of 3D-printed continuous carbon fiber reinforced composites are quantitatively characterized.•A multiscale micromechanical model is developed to map constitutive parameters with the microstructural parameters and constituent material properties.•A multiscale Bayesian method is developed to quantify uncertainties in constitutive and microstructural parameters.•The uncertainty origins of macroscopic constitutive parameters of composites are identified. 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. [Display omitted]