Stochastic characterisation methodology for 3-D textiles based on micro-tomography

Abstract

A recently developed framework to quantify variability of common textile reinforcements of unit cell size is extended to allow for a stochastic description of complex three-dimensional (3-D) textile architectures spanning multiple unit cells. The reinforcement geometry is characterised from synchrotron micro-tomography images in terms of centroid coordinates and tow cross-section. The statistical information includes an average trend, standard deviation and correlation information. A general representation of correlation information is proposed to account for the different tow correlations depending on the location inside the 3-D architecture.The methodology is applied to the characterisation of a 3-D carbon fabric considered for NASA’s Adaptive Deployable Entry Placement Technology (ADEPT) system. Determining geometrical variability in the weave is of importance during the process of setting design margins and risk analysis. Statistical analysis demonstrates strong dependency on the crossover positions for the average trends and correlation data, with a substantially higher variation for the Z-interconnecting tows.