Mr LEIJIAN YU LEIJIAN.YU@NOTTINGHAM.AC.UK
RESEARCH FELLOW
Generating new cellular structures for additive manufacturing through an unconditional 3D latent diffusion model
Yu, Leijian; Kok, Yong En; Parry, Luke; Özcan, Ender; Maskery, Ian
Authors
Yong En Kok
Dr LUKE PARRY LUKE.PARRY@NOTTINGHAM.AC.UK
ASSISTANT PROFESSOR IN ADDITIVE MANUFACTURING OF FUNCTIONAL MATERIAL
Professor Ender Ozcan ender.ozcan@nottingham.ac.uk
PROFESSOR OF COMPUTER SCIENCE AND OPERATIONAL RESEARCH
Dr IAN MASKERY IAN.MASKERY@NOTTINGHAM.AC.UK
ASSOCIATE PROFESSOR
Abstract
Advances in additive manufacturing (AM) have facilitated the fabrication of cellular structures inspired by those in the natural world. But the design of complex, tessellating cellular structures remains a challenge for human designers, and only a small number of geometries, defined either by connected walls or struts, or by surface equations, have been investigated. This study introduces generative deep learning to the problem, with the aim of synthesising novel cellular geometries producible by AM. Our unconditional 3D latent diffusion model (U3LDM) explores the design space from a new class of training data comprising 10,650 unit cells. A critical task involved developing a varied set of cell geometries based on random permutations of trigonometric -surface equations. This was coupled with a stringent set of pass/fail tests to ensure the generated structures possessed structural connectivity and could tessellate in 3D. The new cellular structures were analysed numerically using finite element analysis, fabricated by polymer AM, and subjected to compression tests to verify their manufacturability and mechanical properties. Results indicate that the U3LDM is capable of generating new ‘unseen’ cellular structures with geometries and mechanical properties consistent with those of the training specimens. This method also demonstrates the potential universal technique for creating nature-inspired and AMmanufacturable structures beyond the currently limited set of human-derived geometries.
Citation
Yu, L., Kok, Y. E., Parry, L., Özcan, E., & Maskery, I. (2025). Generating new cellular structures for additive manufacturing through an unconditional 3D latent diffusion model. Additive Manufacturing, Article 104712. https://doi.org/10.1016/j.addma.2025.104712
Journal Article Type | Article |
---|---|
Acceptance Date | Feb 16, 2025 |
Online Publication Date | Feb 27, 2025 |
Publication Date | 2025-02 |
Deposit Date | Feb 28, 2025 |
Publicly Available Date | Feb 28, 2026 |
Journal | Additive Manufacturing |
Print ISSN | 2214-7810 |
Electronic ISSN | 2214-8604 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Article Number | 104712 |
DOI | https://doi.org/10.1016/j.addma.2025.104712 |
Keywords | Additive manufacturing, unconditional 3D latent diffusion model, new cellular structure design, triply periodic continuous surfaces, mechanical testing |
Public URL | https://nottingham-repository.worktribe.com/output/45861114 |
Publisher URL | https://www.sciencedirect.com/science/article/pii/S2214860425000764#d1e5464 |
Additional Information | This article is maintained by: Elsevier; Article Title: Generating new cellular structures for additive manufacturing through an unconditional 3D latent diffusion model; Journal Title: Additive Manufacturing; CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.addma.2025.104712; Content Type: article; Copyright: © 2025 The Authors. Published by Elsevier B.V. |
Files
This file is under embargo until Feb 28, 2026 due to copyright restrictions.
You might also like
Drop-on-demand 3D printing of programable magnetic composites for soft robotics
(2024)
Journal Article
PySLM - Python Library for Selective Laser Melting and Additive Manufacturing
(2023)
Digital Artefact
Magnetorheological brushes – Scarcely explored class of magnetic material
(2023)
Journal Article
FLatt Pack: A research-focussed lattice design program
(2021)
Journal Article
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search