通过数据驱动的方法定制曲线光束格子的能量吸收
Pengting Xiang1, Xian Liu1, Xiang Chen1
1College of Civil Engineering, Nanjing Tech University, Nanjing 211816, China.
Materials (Basel, Switzerland)
|December 11, 2025
概括
这项研究引入了一种新的曲线光束格子结构,用于优越的能量吸收. 使用人工智能进行了优化,与传统设计相比,它显著提高了特定能量吸收.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 计算设计的计算设计.
背景情况:
- 机械超材料具有先进的能量吸收能力.
- 格子结构是设计高性能材料的关键.
- 优化几何参数对于最大化能量吸收至关重要.
研究的目的:
- 提出和优化一种新的曲线光束厚度梯度格子结构 (CBTGLS).
- 使用智能反向设计框架最大限度地提高总能吸收 (EA) 和特定能吸收 (SEA).
- 分析几何参数对能量吸收性能的影响,以改进设计的合理性.
主要方法:
- 使用了一个智能反向设计框架,集成了深度学习和遗传算法.
- 优化了光束厚度和曲线光束控制点.
- 使用可解释性方法 (SHAP,PDP) 来分析参数的影响.
主要成果:
- 优化的CBTGLS在EA和SEA中显示出显著的改进.
- 与基线直光结构相比,特定能量吸收 (SEA) 提高了49.12%.
- 梁厚度和外侧曲率被确定为性能关键参数.
结论:
- 拟议的CBTGLS设计提供了卓越的能量吸收性能.
- 人工智能驱动的设计框架为超材料优化提供了一种高效和可解释的方法.
- 了解几何参数的影响,可以提高先进的能量吸收结构的设计.
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