The Resin Transfer Moulding process receives great attention from both academia and industry, owing to its superior manufacturing rate and product quality. Particularly, the progression of its mould filling stage is crucial to ensure a complete reinforcement saturation. Contemporary process simulation methods focus primarily on physics-based approaches to model the complex resin permeation phenomenon, which are computationally expensive to solve. Thus, the application of machine learning and data-driven modelling approaches is of great interest to minimise the cost of process simulation. In this study, a comprehensive dataset consisting of mould filling patterns of the Resin Transfer Moulding process at different injection locations for a composite dashboard panel case study is presented. The problem description and significance of the dataset are outlined. The distribution of this comprehensive dataset aims to lower the barriers to entry for researching machine learning approaches in composite moulding applications, while concurrently providing a standardised baseline for evaluating newly developed algorithms and models in future research works.

In this study, a eutectic gallium–indium (EGaIn) alloy and graphene nanoplatelets (GnPs) were employed as reinforcements for a comonomer vinyl ester (cVE) resin at different weight fractions up to 2% via a direct polymerization process. First, the effect of EGaIn on the curing kinetics of cVE was evaluated. The thermal and mechanical properties, and the fracture toughness of two types of cVE composites consisting of EGaIn and GnPs were then studied. The results showed that sub-micron sized EGaIn (≤1 wt.%) could promote the curing reaction of cVE without changing the curing mechanism. However, with further increases in EGaIn loading between 1 and 2 wt.%, the curing reaction rate tends to decrease. Both EGaIn and GnPs showed a significant enhancement in strengthening and toughening the cVE matrix with the presence of filler loading up to 1 wt.%. EGaIn was more effective than GnPs in promoting the flexural and impact strength. An increase of up to 50% and 32% were recorded for these mechanical properties, when EGaln was used, as compared to 46%, and 18% for GnPs, respectively. In contrast, the GnPs/cVE composites exhibited a greater improvement in the fracture toughness and fracture energy by up to 50% and 56% in comparison with those of the EGaIn/cVE ones by up to 32% and 39%, respectively. Furthermore, the stiffness of both the EgaIn/cVE and GnPs/cVE composites showed a significant improvement with an increase of up to 1.76 and 1.83 times in the normalized storage modulus, respectively, while the glass transition temperature (T_g) values remained relatively constant. This work highlights the potential of EGaIn being employed as a filler in creating high-performance thermoset composites, which facilitates its widening applications in many structural and engineering fields, where both higher toughness and stiffness are required.