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Stochastic design for additive manufacture of true biomimetic populations

Groth, Jan-Hendrik; Magnini, Mirco; Tuck, Christopher; Clare, Adam

Authors

Jan-Hendrik Groth

CHRISTOPHER TUCK CHRISTOPHER.TUCK@NOTTINGHAM.AC.UK
Professor of Materials Engineering

ADAM CLARE adam.clare@nottingham.ac.uk
Professor of Manufacturing Engineering



Abstract

Current biomimetic designs do not incorporate naturally occurring variance. Instead, the same unit cell is repeatedly copied and pasted to create a pattern. Existing stochastic designs use the same randomness for all parameters. However, nature teaches us that unit cells vary over several geometry defining parameters and that unit cells rarely overlap. Here, we present a methodology where a Gaussian distribution can be superimposed on any regular array of parametric unit cells. The standard deviation controls the randomness. The distance between unit cells is evaluated and used to define different parameters. This allows using different randomness for each parameter and prevents overlaps. Aspect ratios are introduced to define interdependent parameters. The methodology presented here provides a template for researchers to more accurately mimic the engineering performance of biological structures across multi-physics problems. This new design methodology lends itself perfectly to additive manufacturing.

Journal Article Type Article
Acceptance Date Mar 4, 2022
Online Publication Date Apr 1, 2022
Publication Date Jul 1, 2022
Deposit Date May 4, 2022
Journal Additive Manufacturing
Print ISSN 2214-8604
Electronic ISSN 2214-8604
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 55
Article Number 102739
DOI https://doi.org/10.1016/j.addma.2022.102739
Keywords Industrial and Manufacturing Engineering; Engineering (miscellaneous); General Materials Science; Biomedical Engineering
Public URL https://nottingham-repository.worktribe.com/output/7655514
Publisher URL https://www.sciencedirect.com/science/article/pii/S2214860422001439