Exploiting nonlinearities through geometric engineering to enhance the auxetic behaviour in re-entrant honeycomb metamaterials.

Classical approaches to enhance auxeticity quite often involve exploring or designing newer architectures. In this work, simple geometrical features at the member level are engineered to exploit non-classical nonlinearities and improve the auxetic behaviour. The structural elements of the auxetic unit cell are here represented by thin strip-like beams, or thin-walled tubular beams.

Unveiling the Mechanism of the Formation of 3D Fiber Macroassemblies with Controlled Properties.

Electrospinning technique is well-known for the generation of different fibers. While it is a "simple" technique, it lies in the fact that the fibers are typically produced in the form of densely packed two-dimensional (2D) mats with limited thickness, shape, and porosity. The highly demanded three-dimensional (3D) fiber assemblies have been explored by time-consuming postprocessing and/or complex setup modifications.

Impact behaviour of 3D printed cellular structures for mouthguard applications.

Ethylene-Vinyl Acetate (EVA) is the most popular material for manufacturing mouthguards. However, EVA mouthguards are problematic, for example inconsistent thicknesses across the mouthguard. Additive manufacturing provides a promising solution to this problem, as it can manufacture mouthguards with a greater precision.

A new technique for tensile testing of engineering materials and composites at high strain rates.

A new test technique and bespoke apparatus to conduct high strain rate measurements of the tensile response of materials are presented. The new test method is applicable to brittle solids and composites as well as high-performance fibres, yarns and tapes used in composite construction. In this study, the dynamic response of monolithic poly(methyl methacrylate) and unidirectional composites based on Dyneema® tape, Dyneema® SK75 yarn and Kevlar® 49 yarn are explored.

Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning.

We present an application of data analytics and supervised machine learning to allow accurate predictions of the macroscopic stiffness and yield strength of a unidirectional composite loaded in the transverse plane. Predictions are obtained from the analysis of an image of the material microstructure, as well as knowledge of the constitutive models for fibres and matrix, without performing physically-based calculations. The computational framework is based on evaluating the 2-point correlation function of the images of 1800 microstructures, followed by dimensionality reduction via principal component analysis.