Version 1
: Received: 4 July 2024 / Approved: 5 July 2024 / Online: 5 July 2024 (09:26:32 CEST)
How to cite:
Moya, B. J.; Rivas, M.; Quiza, R.; Davim, J. P. Computer Simulation-Based Multi-objective Optimisation of Additively Manufactured Cranial Implants. Preprints2024, 2024070500. https://doi.org/10.20944/preprints202407.0500.v1
Moya, B. J.; Rivas, M.; Quiza, R.; Davim, J. P. Computer Simulation-Based Multi-objective Optimisation of Additively Manufactured Cranial Implants. Preprints 2024, 2024070500. https://doi.org/10.20944/preprints202407.0500.v1
Moya, B. J.; Rivas, M.; Quiza, R.; Davim, J. P. Computer Simulation-Based Multi-objective Optimisation of Additively Manufactured Cranial Implants. Preprints2024, 2024070500. https://doi.org/10.20944/preprints202407.0500.v1
APA Style
Moya, B. J., Rivas, M., Quiza, R., & Davim, J. P. (2024). Computer Simulation-Based Multi-objective Optimisation of Additively Manufactured Cranial Implants. Preprints. https://doi.org/10.20944/preprints202407.0500.v1
Chicago/Turabian Style
Moya, B. J., Ramón Quiza and J. Paulo Davim. 2024 "Computer Simulation-Based Multi-objective Optimisation of Additively Manufactured Cranial Implants" Preprints. https://doi.org/10.20944/preprints202407.0500.v1
Abstract
Driven by the growing interest of the scientific community and the proliferation of research in this field, cranial implants have seen significant advancements in recent years regarding design techniques, structural optimisation, appropriate material selection and fixation system method. Custom implants not only enhance aesthetics and functionality but are also crucial for achieving proper biological integration and optimal blood irrigation, critical aspects in bone regeneration and tissue health. This research aims to optimize the properties of implants designed from triply periodic minimal surface structures. The gyroid architecture is employed for its balance between mechanical and biological properties. Experimental samples were designed varying three parameters of the surface model: cell size, isovalue and shape factor. Computational simulation tools were used for determining the relationship between those parameters and the response variables: the surface area, permeability, porosity and Young modulus. These tools include computer aided design, finite element method and computational fluid dynamics. With the simulated values, the corresponding regression models were fitted. Using the NSGA-II, a multi-objective optimisation was carried out, finding the Pareto set which includes surface area and permeability as targets, and fulfil the constraints related with the porosity and Young modulus. From these non-dominated solutions, the most convenient for a given application was chosen, and an optimal implant was designed, from a patient computed tomography scan. An implant prototype was additively manufactured for validating the proposed approach.
Engineering, Industrial and Manufacturing Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
