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使用人工神经网络预测碳泡的压缩性能
Debela N Gurmu1,2, Krzysztof Wacławiak1, Hirpa G Lemu2
1Faculty of Materials Engineering, Silesian University of Technology, 40-019 Katowice, Poland.
Materials (Basel, Switzerland)
|June 13, 2025
概括
这项研究使用人工神经网络 (ANN) 预测聚氨衍生碳泡的压缩性能. 该ANN模型实现了高精度 (R2=0.9797),证明了其对材料性质预测的有效性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 机器学习应用 机器学习应用
背景情况:
- 聚氨衍生碳泡是具有可调节性质的先进材料.
- 预测压力应力等机械性能对于材料设计和应用至关重要.
- 人工神经网络 (ANN) 为材料中的复杂性质预测提供了一个强大的工具.
研究的目的:
- 开发和评估一个人工神经网络 (ANN) 模型,用于预测聚氨衍生碳泡的压缩性能.
- 为了研究应变,孔密度和溶剂类型对压缩应力的影响.
- 建立一种可靠的计算方法来进行材料表征.
主要方法:
- 设计了一个前ANN,具有四个隐藏层 (每一个100个神经元).
- 输入变量 (菌株,孔密度,溶剂) 通过一次性编码和规范化进行预处理.
- 使用平均平方误差 (MSE),平均绝对误差 (MAE) 和确定系数 (R2) 评估模型性能,并使用Adam优化器和ReLU激活函数.
主要成果:
- 该ANN模型实现了0.9797.7.2的高确定系数 (R2).
- 整体平均平均平方误差 (MSE) 为36.34,平均绝对误差 (MAE) 为4.42.
- 该模型基于所选输入参数,对压力应激具有出色的预测能力.
结论:
- 开发的ANN模型准确地预测了聚氨衍生碳泡的压缩性能.
- 这种方法为材料属性预测提供了一种高效可靠的方法,减少了对广泛实验测试的需求.
- 这些发现突显了机器学习在加速材料发现和开发方面的潜力.
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