小规模球体内涵的力学使用非局部弹性与光梯度增强机器集成
Ali Farajpour1,2, Wendy V Ingman1,2
1Adelaide Medical School, University of Adelaide, The Queen Elizabeth Hospital, Woodville South, SA 5011, Australia.
Micromachines
|February 24, 2024
概括
这项研究引入了一个非局部的多弹性模型,使用机器学习来检测材料中的超微小包含. 这种方法提高了微/纳米尺度缺陷的检测,提高了材料质量和性能.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算力学 计算力学 计算力学
背景情况:
- 检测小规模的入对于微电子机械系统的完整性和性能至关重要.
- 超声波和连续模型是材料缺陷检测的非破坏性方法.
- 由于包含而导致的机械性质变化的定量分析需要先进的建模.
研究的目的:
- 开发一个与机器学习相结合的非局部大小依赖的可波弹性模型.
- 为了描述在辐射压缩下球体内涵的机械行为.
- 为了增强检测能力,以超微小的含有在 poroelastic 介质.
主要方法:
- 利用埃林根的非局部性理论来捕捉流体压力和辐射位移的尺度效应.
- 应用质量守恒定律对固体矩阵和流体含量来推导储存方程.
- 采用了加勒金离散技术,精确集成和渐变增强机器学习模型.
主要成果:
- 开发了一种非局部的弹性模型,并通过数值解决.
- 机器学习有效地从材料的机械反应中提取模式.
- 综合方法显著提高了检测微/纳米尺度内含物的估计能力.
结论:
- 集成的非局部和机器学习模型在检测超微小的入方面提供了更高的准确性.
- 这种方法推进了微/纳米材料的非破坏性测试.
- 这些发现有助于确保微电子机械产品的质量和可靠性.
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