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.

Function modelling is proposed in the literature from different disciplines, in interdisciplinary approaches, and used in practice with the intention of facilitating system conceptualisation. However, function models across disciplines are largely diverse addressing different function modelling perspectives and using different structures and forms for representing the contained information. This hampers the exchange of information between the models and poses particular challenges to joint modelling and shared comprehension between designers from different disciplines. This article proposes an integrated function modelling framework, which specifically aims at relating between the different function modelling perspectives prominently addressed in different disciplines. It uses interlinked matrices based on the concept of DSM and MDM in order to facilitate cross-disciplinary modelling and analysis of the functionality of a system. The article further presents the application of the framework based on a product example. Finally, an empirical study in industry is presented. Therein, feedback on the potential of the proposed framework to support interdisciplinary design practice as well as on areas of further improvement has been obtained from participants working in industry.

The utilisation of numerical process simulation has greatly facilitated the challenging task of liquid composite moulding (LCM) process optimisation, providing ease of solution evaluation at a significantly reduced cost compared to complete reliance on physical prototyping. However, due to the process complexity, such process simulation is still considerably expensive at present. In this paper, cost-saving approaches to minimising the computational cost of simulation-based optimisation for LCM processes are compiled and discussed. Their specific applicability, efficacy, and suitability for various optimisation/moulding scenarios are extensively explored in detail. The comprehensive analysation and assimilation of their operation alongside applicability for the problem domain of interest are accomplished in this paper to further complement and contribute to future simulation-based optimisation capabilities for composite moulding processes. The importance of balancing the cost-accuracy trade-off is also repeatedly emphasised, allowing for substantial cost reductions while ensuring a desirable level of optimization reliability.

Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and artificial neural network metamodels is proposed to build predictive surrogate models capable of relating the mold-filling process input-output correlations to assist mold designing. The input features considered are the resin injection location and resin viscosity. The corresponding output features investigated are the number of vents required and the resultant maximum injection pressure. Upon training, both investigated metamodels demonstrated desirable prediction accuracies, with a low prediction error range of 5.0% to 15.7% for KNN metamodels and 6.7% to 17.5% for ANN metamodels. The good prediction results convincingly indicate that metamodeling is a promising option for composite molding applications, with encouraging prospects for data-intensive applications such as process digital twinning.

Authors across disciplines propose functional modeling as part of systematic design approaches, in order to support and guide designers during conceptual design. The presented research aims at contributing to a better understanding of the diverse functional modeling approaches proposed across disciplines. The article presents a literature review of 41 modeling approaches from a variety of disciplines. The analysis focuses on what is addressed by functional modeling at which point in the proposed conceptual design process (i.e., in which sequence). The gained insights lead to the identification of specific needs and opportunities, which could support the development of an integrated functional modeling approach. The findings suggest that there is no such shared sequence for functional modeling across disciplines. However, a shared functional modeling perspective has been identified across all reviewed disciplines, which could serve as a common basis for the development of an integrated functional modeling approach.

Sixteen-head automatic tape laying of non-crimped carbon-fibre-reinforced plastic is performed, and the fibre alignment is compared with that produced via hand laying. The effect of fibre alignment is tested via quasi-static and cyclic three-point bending tests. Using the Fill Multilayer (a 16-head tape-laying machine), precision fibre laying of unidirectional fabrics is performed with deliberate misalignment to examine the effect of fibre orientation and investigate the random effect on longitudinal misalignment. The automatic tape-layered coupons are compared with hand-layered carbon fibre tapes to investigate the relationship between the fibre alignment and the flexural strength. A 52% reduction in the fibre alignment scatter is achieved via the Fill Multilayer. Fibre orientation increases lead to a higher flexural strength of 16.08% for Fill Multilayer-made coupons compared with hand-layered samples. An investigation of the correlation between fibre alignment and flexural strength shows that shear-based failure increases exponentially as the alignment decreases. Fill Multilayer-made coupons have a higher void concentration due to ultrasonic welding, but also the highest modulus and flexural strength, as fibre misalignment is reduced to 1.68°, with a modulus degradation of 1.4%.