We develop a Bayesian Inference (BI) of a non-linear multiscale model and material parameters using experimental composite coupons tests as observation data. In particular we consider non-aligned Short Fibers Reinforced Polymer (SFRP) as a composite material system and Mean-Field Homogenization (MFH) as a multiscale model. Although MFH is computationally efficient, when considering non-aligned inclusions, the evaluation cost of a non-linear response for a given set of model and material parameters remains too prohibitive to be coupled with the sampling process required by the BI. Therefore, a Neural-Networktype (NNW) is first trained using the MFH model, and is then used as a surrogate model during the BI process, making the identification process affordable.

We present a stochastic approach combining Bayesian Inference (BI) with homogenization theories in order to identify, on the one hand, the parameters inherent to the model assumptions and, on the other hand, the composite material constituents behaviors, including their variability. In particular, we characterize the model parameters of a Mean-Field Homogenization (MFH) model and the elastic matrix behavior, including the inherent dispersion in its Young's modulus, of non-aligned Short Fibers Reinforced Polymer (SFRP) composites. The inference is achieved by considering as observations experimental tests conducted at the SFRP composite coupons level. The inferred model and material law parameters can in turn be used in Mean-Field Homogenization (MFH)-based multi-scale simulations and can predict the confidence range of the composite material responses.

Carbon fiber sheet molding compounds (C-SMCs) are discontinuous fiber reinforced composite materials. Among them, epoxy-based C-SMCs are becoming relevant materials due to their high thermomechanical performance and better formability than continuous fiber reinforced composites. The thermomechanical performance of epoxy resins and epoxy based continuous carbon fiber composites have shown to be influenced by hygrothermal aging. In this work, this influence is studied for an epoxy-based C-SMC. Epoxy-based C-SMC samples were hygrothermally aged by means of accelerated conditioning, exposing them to 65% relative humidity, and 80 C in a climatic chamber. The equilibrium moisture content, as well as the moisture diffusion coefficient has been determined. The thermomechanical properties of epoxy C-SMC have been analyzed by dynamic mechanical analysis, tensile, 3-point bending, and short beam tests in dry and aged samples. The results showed that epoxy C-SMC is affected by hygrothermal aging in the cases of moisture intake and its effects on T g value, but interestingly, the hygrothermal aging did not generate any degradation effects in the mechanical response of epoxy C-SMC.

Sheet molding compounds (SMCs) have considerable potential as lightweight alternative to traditional materials used in automotive components. However, despite their outstanding mechanical properties, their vibration damping characteristics are often relatively poor for several applications. Therefore, enhancing the vibration damping capability of SMCs represents a field with increasing interest. Application of viscoelastic layers to high stiffness materials, such as SMCs, represents an effective approach to add vibration damping functionalities to lightweight structural components. In this work, the incorporation of thermoplastic elastomers (TPEs) is studied as a novel strategy to enhance the structural vibration damping capability of SMCs. Several types of SMC and TPEs have been considered and the effect of TPE‐SMC thickness ratio on the damping and stiffness properties is investigated. The viscoelastic properties and vibration damping performance were evaluated by dynamic mechanical analysis. The effect of TPE addition on stiffness was studied by three‐point bending. Results reveal the potential of the incorporation of thin TPE layers as cost‐effective strategy to enlarge the vibration damping efficiency of SMCs while maintaining their overall high stiffness. It is expected that the bi‐material configurations presented in the current study will contribute to advance the development of new lightweight multi‐functional solutions for modern transport applications.

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