多目标贝叶斯优化用于设计纳克尔灵感的复合材料:通过AI优化和理解仿生学
Kundo Park1, Chihyeon Song2, Jinkyoo Park2
1Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. ryush@kaist.ac.kr.
Materials horizons
|July 12, 2023
概括
本研究介绍了一种基于数据的框架,用于设计生物灵感复合材料,优化强度,性和特定体积. 该方法产生了最佳设计的帕雷托表面,允许针对特定应用进行量身定制的材料选择.
科学领域:
- 材料科学 材料科学 材料科学
- 复合材料 复合材料 复合材料
- 生物启发工程 生物启发工程
背景情况:
- 生物材料表现出层次结构,提供卓越,平衡的特性.
- 设计以自然为灵感的工程材料 (生物灵感复合材料) 具有挑战性,原因是"黑子"优化问题和属性之间的权衡.
- 现有的方法在寻找多目标复合材料优化的独特最佳设计方面扎.
研究的目的:
- 开发一个数据驱动的材料设计框架,用于生成具有最佳物质特性平衡的生物灵感复合材料.
- 将这个框架应用于一种以花为灵感的复合材料,优化强度,性和特定体积.
- 为了克服复杂,多目标材料设计的传统优化方法的局限性.
主要方法:
- 利用高斯过程回归来建模复杂的输入-输出关系.
- 使用从裂相场模拟中生成的数据来训练模型.
- 采用多目标贝叶斯优化来识别帕雷托最佳复合设计.
- 创建了一个3D帕雷托表面,代表了一系列最佳设计解决方案.
主要成果:
- 成功生成了一个3D帕雷托表面的最佳复合材料设计,以纳克尔为灵感的材料.
- 证明数据驱动的框架可以实现强度,性和特定体积的最佳平衡.
- 通过使用PolyJet 3D打印机进行物理制造并随后进行拉伸测试来验证帕雷托最佳设计.
结论:
- 拟议的数据驱动框架在优化具有多种,权衡性质的生物灵感复合材料方面取得了突破.
- 用户可以根据其特定需求从生成的帕雷托表面中选择设计.
- 实验验证证了数据驱动方法在实现优化材料设计方面的有效性.
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