Collapse of clamped and simply supported composite sandwich beams in three-point bending

Abstract Composite sandwich beams, comprising glass -vinylester face sheets and a PVC foam core, have been manufactured and tested quasistatically. Clamped and simply supported beams were tested in three-point bending in order to investigate the initial collapse modes, the mechanisms that govern the post-yield deformation and parameters that set the ultimate strength of these beams. Initial collapse is by three competing mechanisms: face microbuckling, core shear and indentation. Simple formulae for the initial collapse loads of clamped and simply supported beams along with analytical expressions for the finite deflection behaviour of clamped beams are presented. The simply supported beams display a softening post-yield response, while the clamped beams exhibit hardening behaviour due to membrane stretching of the face sheets. Good agreement is found between the measured, analytical and finite element predictions of the load versus deflection response of the simply supported and clamped beams. Collapse mechanism maps with contours of initial collapse load and energy absorption are plotted. These maps are used to determine the minimum mass designs of sandwich beams comprising woven glass face sheets and a PVC foam core.

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. Finite element (FE) simulations are performed on 1800 corresponding statistical volume elements (SVEs) representing cylindrical fibres in a continuous matrix, loaded in the transverse plane. A supervised machine learning (ML) exercise is performed, employing a gradient-boosted tree regression model with 10-fold cross-validation strategy. The model obtained is able to accurately predict the homogenized properties of arbitrary microstructures

Rate Dependence of the Compressive Response of Ti Foams

Titanium foams of relative density ranging from 0.3 to 0.9 were produced by titanium powder sintering procedures and tested in uniaxial compression at strain rates ranging from 0.01 to 2,000 s−1. The material microstructure was examined by X-ray tomography and Scanning Electron Microscopy (SEM) observations. The foams investigated are strain rate sensitive, with both the yield stress and the strain hardening increasing with applied strain rate, and the strain rate sensitivity is more pronounced in foams of lower relative density. Finite element simulations were conducted modelling explicitly the material’s microstructure at the micron level, via a 3D Voronoi tessellation. Low and high strain rate simulations were conducted in order to predict the material’s compressive response, employing both rate-dependant and rate-independent constitutive models. Results from numerical analyses suggest that the primary source of rate sensitivity is represented by the intrinsic sensitivity of the foam’s parent material