使用不规则单元细胞设计具有可编程机械性能的3D NPR格子材料的AI辅助设计
Zewen Gu1, Yalong Liu1, Xiaoxuan Ding1
1Department of Engineering Mechanics, College of Pipeline and Civil Engineering, China University of Petroleum East China, Qingdao, 266580, Shandong, China. guzewen07@hotmail.com.
Materials horizons
|January 27, 2026
概括
本研究介绍了一种AI框架,用于设计具有可调节负波桑定位的不规则3D晶格元材料.
科学领域:
- 材料科学与工程 材料科学与工程
- 计算材料设计设计 计算材料设计
- 超材料是什么?超材料是什么?
背景情况:
- 具有负波桑比率 (NPR) 的格子材料具有独特的机械性能.
- 传统的周期性格子设计限制了异构的潜力;不规则的架构带来了设计挑战.
- 为特定的元材料特性优化不规则的晶格细胞几何是复杂的.
研究的目的:
- 开发一个人工智能驱动的框架,以优化异型负波桑比率 (NPR) 和不规则的3D网格细胞中的能量吸收.
- 为了使3D晶格元材料的反向设计能够具有量身定制的机械反应.
主要方法:
- 利用混合人工智能方法,结合深度神经网络和遗传算法进行参数优化.
- 使用微尺度和宏尺度3D打印制造优化不规则的3D格子细胞.
- 进行了现场和准静态压缩测试,以及微数字图像相关性 (DIC) 分析.
主要成果:
- 在各种材料和尺度上成功验证了可编程负波桑比率 (NPR) 效应.
- 识别了应变局部化模式和关键曲不稳定性,这些不稳定性控制了压缩架构中的变形.
- 证明了对不规则单元细胞基超材料的反向设计能力.
结论:
- 人工智能框架有效地优化了异构性NPR和不规则的3D晶格元材料中的能量吸收.
- 实验验证证证实了可编程的机械性能,并提供了对变形机制的见解.
- 这种方法为设计用于轻质结构和能量吸收应用的先进材料开辟了新的途径.
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