Machine learning based lattice generation method derived from topology optimisation
File(s)
Published version
Author(s)
Wang, Jier
Panesar, Ajit
Type
Journal Article
Abstract
Lattice structures are widely being employed in various lightweighting and multifunctional applications. With the developments in Additive Manufacturing (AM), lattice structures can now be fabricated with limited manufacturing constraints, which facilitates the design of lattice structures to become more flexible. In this paper, a novel lattice generation strategy is proposed to design graded lattice structures with the assistance of Machine Learning (ML). A Neural Network (NN)-based inverse lattice generator is trained to output lattice unit cells from the input of target mechanical properties. The proposed ML-based lattice generation method utilises the density distribution and stress field of low-resolution Topology Optimisation (TO) design to inform the inverse generator and produce lattice cells. The efficiency and efficacy of this method and the influence of cell types are demonstrated with the MBB-beam design case. Furthermore, the developed ML-based method is also applicable to multiple cell types.
Date Issued
2022-12-01
Date Acceptance
2022-10-15
Citation
Additive Manufacturing, 2022, 60 (Part B)
ISSN
2214-8604
Publisher
Elsevier
Journal / Book Title
Additive Manufacturing
Volume
60
Issue
Part B
Copyright Statement
© 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by
License URL
Publication Status
Published
Article Number
103238
Date Publish Online
2022-10-19