利用代理建模和全局优化,高效设计具有蜂窝桁架芯和大声波带隙的夹芯板

IF 2 Q2 ENGINEERING, MECHANICAL Frontiers in Mechanical Engineering Pub Date : 2024-04-16 DOI:10.3389/fmech.2024.1329345
Viviana Meruane, Ignacio Puiggros, Ruben Fernandez, Rafael O. Ruiz
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引用次数: 0

摘要

增材制造技术和拓扑优化技术的最新进展催化了结构材料设计的变革,使其配置日益复杂和个性化。本研究深入探讨了工程蜂窝材料领域,重点关注其通过战略性地创建声波带隙来调节机械波传播的能力。我们将重点放在带有蜂窝桁架核心的夹层板设计上,旨在利用这些带隙在特定频率范围内实现明显的波抑制。我们的方法将代用建模与全面的全局优化策略相结合,采用三种机器学习算法--最近邻算法(kNN)、随机森林回归算法(RFR)和人工神经网络算法(ANN)--从参数化的有限元分析中构建预测模型。这些模型经过训练后,与粒子群优化(PSO)相结合,完善面板设计。这种方法不仅有助于发现针对目标声波带隙的最佳桁架核心配置,还展示了与传统优化方法相比计算效率的显著提高,尤其是在针对不同目标频率进行设计的情况下。
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Efficient design of sandwich panels with cellular truss cores and large phononic band gaps using surrogate modeling and global optimization
Recent advancements in additive manufacturing technologies and topology optimization techniques have catalyzed a transformative shift in the design of architected materials, enabling increasingly complex and customized configurations. This study delves into the realm of engineered cellular materials, spotlighting their capacity to modulate the propagation of mechanical waves through the strategic creation of phononic band gaps. Focusing on the design of sandwich panels with cellular truss cores, we aim to harness these band gaps to achieve pronounced wave suppression within specific frequency ranges. Our methodology combines surrogate modeling with a comprehensive global optimization strategy, employing three machine learning algorithms—k-Nearest Neighbors (kNN), Random Forest Regression (RFR), and Artificial Neural Networks (ANN)—to construct predictive models from parameterized finite element (FE) analyses. These models, once trained, are integrated with Particle Swarm Optimization (PSO) to refine the panel designs. This approach not only facilitates the discovery of optimal truss core configurations for targeted phononic band gaps but also showcases a marked increase in computational efficiency over traditional optimization methods, particularly in the context of designing for diverse target frequencies.
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来源期刊
Frontiers in Mechanical Engineering
Frontiers in Mechanical Engineering Engineering-Industrial and Manufacturing Engineering
CiteScore
4.40
自引率
0.00%
发文量
115
审稿时长
14 weeks
期刊最新文献
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