在压缩下使用神经网络模型预测闭细胞泡的机械性能和应力-应变关系
Anna M Stręk1, Marek Dudzik2, Tomasz Machniewicz3
1Cracow University of Technology, Faculty of Civil Engineering, ul. Warszawska 24, 31-155 Kraków, Poland.
Materials (Basel, Switzerland)
|October 16, 2025
概括
研究人员开发了一种数据驱动的泡配方.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算建模 计算建模
背景情况:
- 泡是具有可调节的机械性能的先进材料.
- 准确预测它们的压缩行为对于工程应用至关重要.
- 之前的工作建立了人工神经网络 (ANN) 模型,用于压力-应变行为.
研究的目的:
- 开发一个数据驱动的公式,用于封闭细胞泡的压缩应力-应变行为.
- 通过结合机械特性来完善ANN模型选择标准.
- 建立压力,应变和表面密度之间的预测关系.
主要方法:
- 使用实验压缩测试数据对500个人工神经网络 (ANN) 模型进行培训和评估.
- 修改了评估标准,包括平原应力和准弹性梯度.
- 开发用于评估ANN模型的加权条件.
主要成果:
- 一个选定的ANN模型实现了约5%的加权平均绝对相对误差.
- 一个数学方程 (σ=f(ε,ρ)) 为应力-应变密度关系推导出来.
- 展示了压力强度和平原应力之间的实验关系.
结论:
- 精细的ANN方法为泡行为提供了一个准确的预测模型.
- 由此得出的公式使得基于密度的应力-应变特征能够精确估计.
- 这项研究推动了金属泡的数据驱动设计和应用.
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