Wood biomimetics: Capturing and simulating the mesoscale complexity of willow using cross-correlation reconstruction algorithm and 3D printing

Abstract

Biomimetics -using nature to inspire innovation- has resulted in the development of many novel materials. Wood, nature’s light-weight structural material, defies mimicry at multiple scales, but its design features have been abstracted and used to develop honeycomb composites of limited geometric complexity. In this paper we demonstrate how it is possible to more completely biomimic wood, capture its exquisite mesoscale features and reproduce its mechanical properties. We utilised micro computed tomography (XCT) to image willow wood and used a novel cross-correlation reconstruction algorithm to mimic its 3D microstructure using minimal information. The mechanical properties of the simulated wood were compared with those derived from parent XCT data using finite-element simulations and testing of 3D printed samples of both wood types. The porosity and connectivity of both sample types was also compared. Our reconstruction algorithm was able to capture critical structural elements of willow wood with a high degree of fidelity, while mechanical properties of both wood types were closely matched. We conclude that our image-based stochastic modelling biomimetics offers a unique approach to capturing the mesoscale complexity of wood and will lead to new opportunities for designing novel materials inspired by xylotomy and powered by fast and high-performance computation.