通过机器学习进行多材料元材料反向设计,以实现可定制和可重复使用的能量吸收
Xuyang Li1, Yong Qin1, Lianfa Sun1
1Institute of Advanced Structure Technology, Beijing Institute of Technology, Beijing 100081, China.
ACS applied materials & interfaces
|June 16, 2025
概括
本研究介绍了一种机器学习框架,用于设计可重复使用的能吸收结构,具有可调节的机械性能. 该方法优化了复杂的格子设计,使能精确控制各种应用的能量吸收和平台应力.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 对于具有可定制机械性能的能吸收结构的需求日益增长.
- 现有的设计方法在多目标优化和材料集成方面扎.
研究的目的:
- 开发一种基于机器学习的逆向设计框架,用于可定制的能吸收元材料.
- 为了共同优化材料选择,格子拓和特定性能目标的几何参数.
主要方法:
- 使用了181维的参数空间进行优化.
- 集成的多材料兼容性 (TPU/树脂/NiTi/Al合金) 与身体中心立方 (BCC) 格子.
- 进行了超过20,000次有限元素分析模拟.
- 采用人工神经网络和基因算法用于反向设计.
主要成果:
- 实现了对特定平台应力 (0.0154.05 MPa) 和特定能量吸收 (0.04923.377 J/g) 的精确控制.
- 对于NiTi合金金属材料,已证明具有很高的可重复使用性 (经过五个循环后回收超过50%).
- 通过增材制造和实验性表征验证了框架.
结论:
- 拟议的框架有效地弥合了可定制的能量吸收和结构的可重复使用性之间的差距.
- 机器学习使先进的吸收能量的元材料的有效反向设计成为可能.
- 开发的方法为创建量身定制的高性能能量吸收解决方案提供了一条途径.
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