AccScience Publishing / MSAM / Volume 3 / Issue 2 / DOI: 10.36922/msam.3430
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ORIGINAL RESEARCH ARTICLE

Accelerating hybrid lattice structures design with machine learning

Chenxi Peng1,2 ,  Phuong Tran3* ,  Erich Rutz1,2,4,5,6,7*
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1 Department of Paediatrics, The University of Melbourne, Parkville, Victoria, Australia
2 Murdoch Children’s Research Institute, Parkville, Victoria, Australia
3 RMIT Centre for Additive Manufacturing, School of Engineering, RMIT University, Melbourne, Victoria, Australia
4 Bob Dickens Chair Paediatric Orthopaedic Surgery, The University of Melbourne, Parkville, Victoria, Australia
5 Department of Orthopaedics, The Royal Children’s Hospital Melbourne, Parkville, Victoria, Australia
6 The Hugh Williamson Gait Analysis Laboratory, The Royal Children’s Hospital Melbourne, Parkville, Victoria, Australia
7 Medical Faculty, The University of Basel, Basel, Switzerland
MSAM 2024, 3(2), 3430 https://doi.org/10.36922/msam.3430
Submitted: 16 April 2024 | Accepted: 3 June 2024 | Published: 25 June 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Lattice structures inspired by triply periodic minimal surfaces (TPMS) have attracted increasing attention due to their lightweight properties and high mechanical performance. Recent research showed that hybrid structures based on the topology of two or more types of TPMS can present interesting multifunctional properties. However, the complexity of TPMS-based lattice designs presents challenges in both design and evaluation. To address these challenges, this study was designed to explore the integration of the machine learning method to predict the mechanical properties of hybrid lattice structures inspired by TPMS based on their patterns. A back propagation neural network (BPNN) was designed and trained on a dataset generated through finite element (FE) simulations and homogenization methods. The BPNN demonstrated robustness in predicting elastic modulus and Poisson’s ratio of TPMS hybrid lattice structures, offering rapid and efficient predictions. Validation against FE simulations confirmed the accuracy and reliability of the BPNN predictions, proving its potential as a valuable tool for accelerating the design and evaluation of complex hybrid lattice structures.

Keywords
Lattice structures
Triply periodic minimal surfaces
Elastic modulus
Poisson’s ratio
Machine learning
Funding
Not applicable.
Conflict of interest
The authors declare that they have no competing interests.
Editorial Disclosure

Phuong Tran serves as the Editorial Board Member of the journal, but did not in any way involve in the editorial and peer-review process conducted for this paper, directly or indirectly.

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Materials Science in Additive Manufacturing, Electronic ISSN: 2810-9635 Published by AccScience Publishing
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