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    Multi-Lattice Topology Optimization Via Generative Lattice Modeling

    Source: Journal of Mechanical Design:;2025:;volume( 147 ):;issue: 005::page 51707-1
    Author:
    Chen, Jiangce
    ,
    Baldwin, Martha
    ,
    Prabha Narra, Sneha
    ,
    McComb, Christopher
    DOI: 10.1115/1.4067528
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Additive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity that must be managed in order to achieve a multi-lattice structure solution. However, there is a lack of design automation approaches that can tractably create multi-lattice structures. This article introduces an innovative multi-scale topology optimization (TO) framework, called multi-lattice topology optimization with variational autoencoder (MulaTOVA), that is capable of concurrently addressing macro- and mesoscale design requirements. Neural networks (NNs) are employed in this framework to jointly represent the structural topology at the macroscale and the lattice heterogeneity at the mesoscale, enabling simultaneous optimization through the updating of the NNs’ weights. The connectivity between lattices is implicitly constrained by constraining the NNs, while the diversity of the lattices is guaranteed through a generative lattice model which is trained over a large lattice dataset. The performances of various NN types are compared, and Fourier neural operators (FNOs) demonstrated the best flexibility in balancing lattice diversity and local connectivity. Furthermore, our results show that the multi-lattice TO structures achieve a higher stiffness-to-weight ratio than solid TO structures.
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      Multi-Lattice Topology Optimization Via Generative Lattice Modeling

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    contributor authorChen, Jiangce
    contributor authorBaldwin, Martha
    contributor authorPrabha Narra, Sneha
    contributor authorMcComb, Christopher
    date accessioned2025-04-21T10:03:41Z
    date available2025-04-21T10:03:41Z
    date copyright1/30/2025 12:00:00 AM
    date issued2025
    identifier issn1050-0472
    identifier othermd_147_5_051707.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305407
    description abstractAdditive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity that must be managed in order to achieve a multi-lattice structure solution. However, there is a lack of design automation approaches that can tractably create multi-lattice structures. This article introduces an innovative multi-scale topology optimization (TO) framework, called multi-lattice topology optimization with variational autoencoder (MulaTOVA), that is capable of concurrently addressing macro- and mesoscale design requirements. Neural networks (NNs) are employed in this framework to jointly represent the structural topology at the macroscale and the lattice heterogeneity at the mesoscale, enabling simultaneous optimization through the updating of the NNs’ weights. The connectivity between lattices is implicitly constrained by constraining the NNs, while the diversity of the lattices is guaranteed through a generative lattice model which is trained over a large lattice dataset. The performances of various NN types are compared, and Fourier neural operators (FNOs) demonstrated the best flexibility in balancing lattice diversity and local connectivity. Furthermore, our results show that the multi-lattice TO structures achieve a higher stiffness-to-weight ratio than solid TO structures.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleMulti-Lattice Topology Optimization Via Generative Lattice Modeling
    typeJournal Paper
    journal volume147
    journal issue5
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4067528
    journal fristpage51707-1
    journal lastpage51707-14
    page14
    treeJournal of Mechanical Design:;2025:;volume( 147 ):;issue: 005
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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