Open AccessExploiting Generative Design for 3D Printing of Bacterial Biofilm Resistant Composite Devices
Author(s) -
He Yinfeng,
Abdi Meisam,
Trindade Gustavo F.,
Begines Belén,
Dubern JeanFrédéric,
Prina Elisabetta,
Hook Andrew L.,
Choong Gabriel Y. H.,
Ledesma Javier,
Tuck Christopher J.,
Rose Felicity R. A. J.,
Hague Richard J. M.,
Roberts Clive J.,
De Focatiis Davide S. A.,
Ashcroft Ian A.,
Williams Paul,
Irvine Derek J.,
Alexander Morgan R.,
Wildman Ricky D.
Publication year - 2021
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202100249
Subject(s) - personalization , generative design , computer science , 3d printing , composite number , generative grammar , deformation (meteorology) , process (computing) , materials science , fused deposition modeling , biofilm , mechanical engineering , process engineering , nanotechnology , composite material , artificial intelligence , engineering , algorithm , world wide web , compatibility (geochemistry) , biology , bacteria , genetics , operating system
Abstract As the understanding of disease grows, so does the opportunity for personalization of therapies targeted to the needs of the individual. To bring about a step change in the personalization of medical devices it is shown that multi‐material inkjet‐based 3D printing can meet this demand by combining functional materials, voxelated manufacturing, and algorithmic design. In this paper composite structures designed with both controlled deformation and reduced biofilm formation are manufactured using two formulations that are deposited selectively and separately. The bacterial biofilm coverage of the resulting composites is reduced by up to 75% compared to commonly used silicone rubbers, without the need for incorporating bioactives. Meanwhile, the composites can be tuned to meet user defined mechanical performance with ±10% deviation. Device manufacture is coupled to finite element modelling and a genetic algorithm that takes the user‐specified mechanical deformation and computes the distribution of materials needed to meet this under given load constraints through a generative design process. Manufactured products are assessed against the mechanical and bacterial cell‐instructive specifications and illustrate how multifunctional personalization can be achieved using generative design driven multi‐material inkjet based 3D printing.