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Design and Fabrication of 3D Printed Tissue Scaffolds Informed by Mechanics and Fluids Simulations

[+] Author Affiliations
Paul F. Egan, Veronica C. Gonella, Max Engensperger, Stephen J. Ferguson, Kristina Shea

Swiss Federal Institute of Technology ETH Zurich, Zurich, Switzerland

Paper No. DETC2017-67602, pp. V02AT03A029; 10 pages
  • ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 2A: 43rd Design Automation Conference
  • Cleveland, Ohio, USA, August 6–9, 2017
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5812-7
  • Copyright © 2017 by ASME


Advances in additive manufacturing are enabling the fabrication of lattices with complex geometries that are potentially advantageous as tissue scaffolds. Scaffold design for optimized mechanics and tissue growth is challenging, due to complicated trade-offs among scaffold structural properties including porosity, pore size, surface-volume ratio, elastic modulus, shear modulus, and permeability. Here, a design for additive manufacturing approach is developed for tuning unit cell libraries as tissue scaffolds through (1) simulation, (2) design automation, and (3) fabrication. Finite element simulations are used to determine elastic and shear moduli of lattices as a function of porosity. Fluids simulations suggest that lattice permeability scales with porosity cubed over surface-volume ratio squared. The design automation approach uses simulation results to configure lattices with specified porosity and pore size. A cubic and octet lattice are fabricated with pore sizes of 1,000μm and porosities of 60%; these lattice types represent unit cells with high unidirectional elastic modulus/permeability and high shear modulus/surface-volume ratio, respectively. Imaging suggests the 3D printing process recreates the form accurately, but distorts microscale features. Future iterations are required to determine how lattices perform in comparison to computational predictions. The developed approach provides the foundations of a design automation approach for optimized 3D printed tissue scaffolds informed by simulation and experiments.

Copyright © 2017 by ASME



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