Neural Network Modeling of an SLA Printed Mesostructure

Anne Schmitz
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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 SLA, 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 the how the geometry and material of the cellular mesostructure affects structural stiffness.
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SLA 印刷中间结构的神经网络建模
关于各种参数化晶格设计对介质结构功能的共同影响的综合研究十分匮乏,本文针对这一问题进行了研究。本研究以优化使用 SLA 制造的蛇形介观结构的能量吸收为重点,利用前馈神经网络探索线宽、圈数和材料特性之间对结构能量吸收的相互影响。使用有限元模型进行的压缩模拟涵盖了一系列配置,为神经网络训练提供了数据集。由此产生的网络用于探究几何变量、材料和能量吸收之间的相关性。此外,神经网络灵敏度分析探索了隐藏层和神经元数量对网络性能的影响,证明了网络的鲁棒性。神经网络确定的优化中间结构配置最大限度地吸收了能量。讨论利用材料力学的基本概念,解释了细胞介质结构的几何形状和材料如何影响结构刚度。
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