Investigation of Thermal, Mechanical and Shape Memory Properties of 3D-Printed Functionally Graded Nanocomposite Materials.

In this study, a 3D-printed photocurable resin was developed by incorporating graphene nanoplatelets functionalised with melamine to investigate the thermal, mechanical, fracture and shape memory behaviours. The objective of this work was to produce a printed functionally graded nanocomposite material that has a smart temperature-responsive structure; presents good thermal stability, strength and fracture toughness; and can demonstrate shape-changing motions, such as sequential transformations, over time. The functionalised graphene nanoplatelets were examined via thermogravimetric analysis, Fourier transform infrared spectroscopy, Raman spectroscopy and ultraviolet-visible spectroscopy. Thermogravimetric analysis showed that the degradation temperature of the nanocomposite containing 0.1 wt% of functionalised graphene nanoplatelets at the weight loss of 5% was 304 °C, greater than that of the neat one by 29%. Dynamic mechanical analysis results showed property enhancements of the storage modulus and glass transition temperature. Fracture toughness, tensile strength and impact resistance were improved by 18%, 35% and 78%, respectively. The shape memory tests were performed to obtain the temperature-time recovery behaviour of the 3D-printed structures. The addition of functionalised graphene nanoplatelets demonstrated an enhancement in the shape recovery ratios. Generally, the five subsequent cycles were notably stable with a high recovery ratio of 97-100% for the flat shape and circular shape of the M-GNP specimens. On the other hand, these values were between 91% and 94% for the corresponding neat specimens.

The Development of a Digital Twin Framework for an Industrial Robotic Drilling Process.

A digital twin is a digital representation of a physical entity that is updated in real-time by transfer of data between physical and digital (virtual) entities. In this manuscript we aim to introduce a digital twin framework for robotic drilling. Initially, a generic reference model is proposed to highlight elements of the digital twin relevant to robotic drilling. Then, a precise reference digital twin architecture model is developed, based on available standards and technologies. Finally, real-time visualisation of drilling process parameters is demonstrated as an initial step towards implementing a digital twin of a robotic drilling process.

Multi-objective optimisation of ultrasonically welded dissimilar joints through machine learning.

The use of composite materials is increasing in industry sectors such as renewable energy generation and storage, transport (including automotive, aerospace and agri-machinery) and construction. This is a result of the various advantages of composite materials over their monolithic counterparts, such as high strength-to-weight ratio, corrosion resistance, and superior fatigue performance. However, there is a lack of detailed knowledge in relation to fusion joining techniques for composite materials. In this work, ultrasonic welding is carried out on a carbon fibre/PEKK composite material bonded to carbon fibre/epoxy composite to investigate the influence of weld process parameters on the joint's lap shear strength (LSS), the process repeatability, and the process induced defects. A 33 parametric study is carried out and a robust machine learning model is developed using a hybrid genetic algorithm-artificial neural network (GA-ANN) trained on the experimental data. Bayesian optimisation is employed to determine the most suitable GA-ANN hyperparameters and the resulting GA-ANN surrogate model is exploited to optimise the welding process, where the process performance metrics are LSS, repeatability and joint visual quality. The prediction for the optimal LSS was subsequently validated through a further set of experiments, which resulted in a prediction error of just 3%.