In this work, shear-thickening-gel applied CFRP (SACFRP) composite laminates were developed to enhance the impact resistance of the composites under low-velocity impact (LVI) conditions, where the incorporated shear thickening gel (STG) worked as the interphase material between fibres and resin matrix. To analyse the effects of STG in its composites, static tensile and shear tests were first conducted on longitudinally and transversely positioned unidirectional (UD) SACFRP and its CFRP reference, respectively. Experimental results indicated that the corresponding reduction of the resin matrix due to the incorporation of the relatively soft STG weakened the interlaminar behaviour of the SACFRP laminates during static mechanical tests. However, the transverse tensile toughness of the SACFRP exhibited a remarkable 139 % improvement compared to the CFRP reference, demonstrating significant interfacial toughening of the developed composites, as verified through SEM analysis. To leverage the effects of the STG on the composites, this work modified the stacking sequences of SACFRP laminates. LVI tests and recurring LVI tests demonstrated the substantial improvement of impact performance for layup-designed SACFRP laminates since the impact-resistant mechanism transitioned from the local damage of CFRPs to the global flexural behaviour of SACFRPs. Timoshenko's analytical model validated the resistant mechanism transition of layup-designed SACFRP during LVI tests. Therefore, the SACFRP laminates with modified stacking sequences demonstrate outstanding potential for use under extreme loading conditions involving complex and unavoidable impacts, highlighting their broad applicability across various industries.

Carbon fiber reinforced plastics (CFRPs) are attracting growing attention in industry because of their enhanced properties. Preforming of thermoset carbon fiber prepregs is one of the most common production techniques of CFRPs. To simulate preforming, several computational methods have been developed. Most of these methods, however, obtain the material properties directly from experiments such as uniaxial tension and bias-extension where the coupling effect between tension and shear is not considered. Neglecting this coupling effect deteriorates the prediction accuracy of simulations. To address this issue, we develop a Bayesian model calibration and material characterization approach in a multiscale finite element preforming simulation framework that utilizes mesoscopic representative volume element (RVE) to account for the tension-shear coupling. A new geometric modeling technique is first proposed to generate the RVE corresponding to the close-packed uncured prepreg. This RVE model is then calibrated with a modular Bayesian approach to estimate the yarn properties, test its potential biases against the experiments, and fit a stress emulator. The predictive capability of this multiscale approach is further demonstrated by employing the stress emulator in the macroscale preforming simulation which shows that this approach can provide accurate predictions.