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.

The increasing application of composite materials in various industrial sectors is driven by their lightweight nature, high strength-to-stiffness ratio, and corrosion resistance. Effective monitoring of the curing process is crucial for ensuring quality and performance. Electro-Mechanical Impedance (EMI) offers promising, non-destructive, real-time monitoring, but the complexity of EMI signals poses challenges. Convolutional Neural Networks (CNNs) have the potential to enhance EMI-based monitoring accuracy. However, training CNNs on multi-modal EMI signals requires addressing data heterogeneity, class imbalance, and computational complexity at present. This study develops the Importance Sampling Algorithm-optimized Multi-Modal CNNs (ISA-MM-CNNs) paradigm for EMI-based evaluation of composite curing processes. By prioritizing informative samples and capturing complementary information from diverse EMI signal modalities, we aim to improve the robustness and efficiency of CNNs in evaluating curing degrees. This study outlines EMI monitoring challenges, details the ISA-MM-CNNs paradigm, and discusses data preprocessing, network architecture, and training optimization. Experimental results demonstrate the superiority of the developed ISA-MM-CNNs and suggest further studies for the curing monitoring of composites.