Exploiting generative design for multi-material inkjet 3D printed cell instructive, bacterial biofilm resistant composites

Yinfeng He, Belen Begines, Gustavo F. Trindade, Meisam Abdi, Jean-Frédéric Dubern, Elisabetta Prina, Andrew L. Hook, Gabriel Choong, Javier Ledesma, Christopher J. Tuck, Felicity R. A. J. Rose, Richard Hague, Clive J. Roberts, Davide De de Focatiis, Ian A. Ashcroft, Paul Williams, Derek J. Irvine, Morgan R. Alexander, Ricky D. Wildman

Research output: Working paper

Abstract

As our understanding of disease grows, it is becoming established that treatment needs to be personalized and targeted to the needs of the individual. In this paper we show that multi-material inkjet-based 3D printing, when backed with generative design algorithms, can bring a step change in the personalization of medical devices. We take cell-instructive materials known for their resistance to bacterial biofilm formation and reformulate for multi-material inkjet-based 3D printing. Specimens with customizable mechanical moduli are obtained without loss of their cell-instructive properties. The manufacturing is coupled to a design algorithm that takes a user-specified deformation and computes the distribution of the materials needed to meet the target under given load constraints. Optimisation led to a voxel map file defining where different materials should be placed. Manufactured products were assessed against the mechanical and cell-instructive specifications and ultimately showed how multifunctional personalization emerges from generative design driven 3D printing.
Original languageEnglish
Place of PublicationCambridge
PublisherCambridge Open Engage
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Multimaterial
  • 3D printing
  • Generative design
  • Cell instructive
  • Bacterial biofilm resistant

Fingerprint

Dive into the research topics of 'Exploiting generative design for multi-material inkjet 3D printed cell instructive, bacterial biofilm resistant composites'. Together they form a unique fingerprint.

Cite this