Neural Network Modeling of a Stereolithography Printed MesostructureSource: Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004::page 44502-1Author:Schmitz, Anne
DOI: 10.1115/1.4065291Publisher: 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.
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contributor author | Schmitz, Anne | |
date accessioned | 2024-12-24T19:07:59Z | |
date available | 2024-12-24T19:07:59Z | |
date copyright | 4/26/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 2572-7958 | |
identifier other | jesmdt_007_04_044502.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4303341 | |
description 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. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Neural Network Modeling of a Stereolithography Printed Mesostructure | |
type | Journal Paper | |
journal volume | 7 | |
journal issue | 4 | |
journal title | Journal of Engineering and Science in Medical Diagnostics and Therapy | |
identifier doi | 10.1115/1.4065291 | |
journal fristpage | 44502-1 | |
journal lastpage | 44502-6 | |
page | 6 | |
tree | Journal of Engineering and Science in Medical Diagnostics and Therapy:;2024:;volume( 007 ):;issue: 004 | |
contenttype | Fulltext |