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    Data-Driven Topology Optimization With Multiclass Microstructures Using Latent Variable Gaussian Process

    Source: Journal of Mechanical Design:;2020:;volume( 143 ):;issue: 003::page 031708-1
    Author:
    Wang, Liwei
    ,
    Tao, Siyu
    ,
    Zhu, Ping
    ,
    Chen, Wei
    DOI: 10.1115/1.4048628
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: The data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or “distance” measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures.
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      Data-Driven Topology Optimization With Multiclass Microstructures Using Latent Variable Gaussian Process

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    contributor authorWang, Liwei
    contributor authorTao, Siyu
    contributor authorZhu, Ping
    contributor authorChen, Wei
    date accessioned2022-02-05T21:45:39Z
    date available2022-02-05T21:45:39Z
    date copyright11/13/2020 12:00:00 AM
    date issued2020
    identifier issn1050-0472
    identifier othermd_143_3_031708.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4276285
    description abstractThe data-driven approach is emerging as a promising method for the topological design of multiscale structures with greater efficiency. However, existing data-driven methods mostly focus on a single class of microstructures without considering multiple classes to accommodate spatially varying desired properties. The key challenge is the lack of an inherent ordering or “distance” measure between different classes of microstructures in meeting a range of properties. To overcome this hurdle, we extend the newly developed latent-variable Gaussian process (LVGP) models to create multi-response LVGP (MR-LVGP) models for the microstructure libraries of metamaterials, taking both qualitative microstructure concepts and quantitative microstructure design variables as mixed-variable inputs. The MR-LVGP model embeds the mixed variables into a continuous design space based on their collective effects on the responses, providing substantial insights into the interplay between different geometrical classes and material parameters of microstructures. With this model, we can easily obtain a continuous and differentiable transition between different microstructure concepts that can render gradient information for multiscale topology optimization. We demonstrate its benefits through multiscale topology optimization with aperiodic microstructures. Design examples reveal that considering multiclass microstructures can lead to improved performance due to the consistent load-transfer paths for micro- and macro-structures.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleData-Driven Topology Optimization With Multiclass Microstructures Using Latent Variable Gaussian Process
    typeJournal Paper
    journal volume143
    journal issue3
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4048628
    journal fristpage031708-1
    journal lastpage031708-13
    page13
    treeJournal of Mechanical Design:;2020:;volume( 143 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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