Estructuras ogánicas para el espacio.
Design algorithms for additive manufacturing
PINN3D
Additive manufacturing (AM) technologies are considered an essential tool since the latest industrial revolution, Industry 4.0. For space applications, it is a key enabling technology, as fairing volume and cargo load limitations are critical drivers in numerous mission scenarios. AM techniques can enable lightweight, in-situ, on-demand manufacturing, thereby producing mass-optimised parts and reducing the amount of spares shipped from Earth. This applies to structural spacecraft parts, but also to future in-situ bioprinting, to support long-term crewed missions. The first step required in AM, is the development of a 3D model of the part. Classic CAD design involves a time-consuming loop, where this model is created manually, tested, and then corrected for the next analysis iteration. In addition, 3D slicer programs used in AM, usually allow only regular lattice patterns for the internal architecture of parts (infill), which facilitates crack propagation and failure of structures, in particular when manufacturing defects are present. Regular patterns are also not representative of most biological structures. Our aim within this activity is to develop a set of algorithms that allow us to create more robust graph-based organic structures in an autonomous way, given a basic set of initial parameters. Henceforth, we aim to use physics-informed neural networks (PINNs) which will not require initial big data, as internal relations replicate physical laws. It will be an iterative computational design model, representing a cyclic data flow pipeline. The outcome of this project will help to obtain better infills for 3D objects that avoid crack propagation and fit the desired physical environment (in regard to internal flow and external forces). The final application of such structures may be a biological scaffold for tissue engineering or any other lightweight porous structure (especially for space: structural parts for spacecraft, habitats or other infrastructure).
ENTIDADES FINANCIADORAS / FUNDING BODIES
European Space Agency (ESA) — Programa OSIP Ideas, Co-Sponsored Research Agreement. Referencia: I-2022-03197. Research Agreement No. 4000140350/23/NL/GLC/my. Discovery Program. Página oficial del proyecto en ESA: https://activities.esa.int/4000140350
Universidad Nebrija — Cofinanciación del proyecto de investigación doctoral, desarrollado en la Escuela Politécnica Superior bajo el Doctorado en Tecnologías Industriales e Informática, con participación del grupo ARIES.
RESULTADOS Y PUBLICACIONES / OUTPUTS & PUBLICATIONS
Artículo en revista / Journal Article A Systematic Review on the Generation of Organic Structures through Additive Manufacturing Techniques. Bernadí-Forteza, A.; Mallon, M.; Velasco-Gallego, C.; Cubo-Mateo, N. Polymers, 2024, 16(14), 2027. https://doi.org/10.3390/polym16142027
Artículo en preparación / Manuscript in preparation A Novel Method for Generating Self-Supporting Stochastic Porous Structures for Bone Tissue Engineering. Bernadí-Forteza, A.; Mallon, M.; Makaya, A.; Cubo-Mateo, N.
Comunicación oral / Oral Presentation — WAMS 2026 Novel Method for Generating Self-Supporting Graph-Based Organic Structures for Additive Manufacturing for Space Applications. Bernadí-Forteza, A. Worldwide Advanced Manufacturing Symposium (WAMS) 2026, ESA ESTEC, Noordwijk, junio 2026.
Póster / Poster — ESB 2025 Advanced non-regular lattice designs for supportless bone scaffold manufacturing. Bernadí-Forteza, A.; Mallon, M.; Cubo-Mateo, N. European Society of Biomaterials Annual Conference, Torino, septiembre 2025.
Comunicación oral / Oral Presentation — SIBB'24 Advanced non-regular lattice designs for supportless bone scaffold manufacturing. Bernadí-Forteza, A.; Uriarte, J.J.; Cubo-Mateo, N. XLVI Congreso de la Sociedad Ibérica de Biomecánica y Biomateriales (SIBB'24), Valencia, diciembre 2024.
Comunicación oral premiada / Award-winning Oral Presentation — Jornadas Nebrija 2024 Diseños avanzados de estructuras no regulares para la fabricación de andamios óseos sin soporte. Bernadí-Forteza, A. I Jornadas de Jóvenes Investigadores, Universidad Nebrija, noviembre 2024. 2.º mejor presentación oral.
Código abierto / Open Source mesh-gen — Python package for generating self-supporting stochastic porous structures. https://github.com/panallax/mesh-gen
Spyffness — Python library for structural analysis of lattice structures. https://github.com/panallax/Spyffness
Tesis doctoral / PhD Thesis Generation of Self-Supported Organic Structures for Tissue Engineering and Space Exploration. Bernadí-Forteza, A. Doctorado en Tecnologías Industriales e Informática, Universidad Nebrija / ESA, Madrid & Noordwijk, 2025. Directora: Dra. Nieves Cubo Mateo. Codirector: Dr. Michael Mallon. Cum Laude.
+ info:
Página oficial del proyecto, en ESA: