YaBeSH Engineering and Technology Library

    • Journals
    • PaperQuest
    • YSE Standards
    • YaBeSH
    • Login
    View Item 
    •   YE&T Library
    • ASME
    • Journal of Engineering and Science in Medical Diagnostics and Therapy
    • View Item
    •   YE&T Library
    • ASME
    • Journal of Engineering and Science in Medical Diagnostics and Therapy
    • View Item
    • All Fields
    • Source Title
    • Year
    • Publisher
    • Title
    • Subject
    • Author
    • DOI
    • ISBN
    Advanced Search
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Archive

    Neural Network Modeling of a Stereolithography Printed Mesostructure

    Source: Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004::page 44502-1
    Author:
    Schmitz, Anne
    DOI: 10.1115/1.4065291
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: This paper addresses the scarcity of comprehensive studies on the collective impact of various parametric lattice designs on mesostructure functionality. Focusing on optimizing the energy absorption of a serpentine mesostructure made using stereolithography, this research leverages a feedforward neural network to explore the interplay between line width, number of turns, and material properties on the energy absorbed by the structure. Compression simulations using a finite element model, covering a range of configurations, provided the dataset for neural network training. The resulting network was used to probe correlations between geometric variables, material, and energy absorption. Additionally, a neural network sensitivity analysis explored the impact of hidden layers and number of neurons on the network's performance, demonstrating the network's robustness. The optimized mesostructure configuration, identified by the neural network, maximized energy absorption. Using foundational mechanics of materials concepts, the discussion explains how the geometry and material of the cellular mesostructure affect structural stiffness.
    • Download: (2.423Mb)
    • Show Full MetaData Hide Full MetaData
    • Get RIS
    • Item Order
    • Go To Publisher
    • Price: 5000 Rial
    • Statistics

      Neural Network Modeling of a Stereolithography Printed Mesostructure

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4303341
    Collections
    • Journal of Engineering and Science in Medical Diagnostics and Therapy

    Show full item record

    contributor authorSchmitz, Anne
    date accessioned2024-12-24T19:07:59Z
    date available2024-12-24T19:07:59Z
    date copyright4/26/2024 12:00:00 AM
    date issued2024
    identifier issn2572-7958
    identifier otherjesmdt_007_04_044502.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4303341
    description abstractThis paper addresses the scarcity of comprehensive studies on the collective impact of various parametric lattice designs on mesostructure functionality. Focusing on optimizing the energy absorption of a serpentine mesostructure made using stereolithography, this research leverages a feedforward neural network to explore the interplay between line width, number of turns, and material properties on the energy absorbed by the structure. Compression simulations using a finite element model, covering a range of configurations, provided the dataset for neural network training. The resulting network was used to probe correlations between geometric variables, material, and energy absorption. Additionally, a neural network sensitivity analysis explored the impact of hidden layers and number of neurons on the network's performance, demonstrating the network's robustness. The optimized mesostructure configuration, identified by the neural network, maximized energy absorption. Using foundational mechanics of materials concepts, the discussion explains how the geometry and material of the cellular mesostructure affect structural stiffness.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleNeural Network Modeling of a Stereolithography Printed Mesostructure
    typeJournal Paper
    journal volume7
    journal issue4
    journal titleJournal of Engineering and Science in Medical Diagnostics and Therapy
    identifier doi10.1115/1.4065291
    journal fristpage44502-1
    journal lastpage44502-6
    page6
    treeJournal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004
    contenttypeFulltext
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian
     
    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
    yabeshDSpacePersian