开发基于数字图像处理和机器学习的方法,以预测聚氨泡的形态和热性能
1Department of Metallurgical and Materials Engineering, Zonguldak Bulent Ecevit University, 67100 Zonguldak, Türkiye.
Polymers
|April 12, 2025
概括
本研究介绍了一种使用数字图像处理和机器学习分析聚氨泡结构的新,可访问的方法. 开发的算法通过检查孔邻近效应来预测导热率,为传统成像技术提供更快,非破坏性的替代方案.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 聚合物科学 聚合物科学
背景情况:
- 由于其多孔结构,聚氨泡对于隔热至关重要.
- 这些泡的合成涉及到制造隔热毛孔的吹剂.
- 了解孔隙稳定性和相邻效应对于优化热性能至关重要.
研究的目的:
- 通过计算分析与水和环素合成的聚氨泡中的孔稳定性和相邻效应.
- 开发一个数字图像处理和机器学习算法来预测泡的特性.
- 为了确定孔隙结构分析和导热率之间的相关性.
主要方法:
- 合成五个不同的聚氨泡样本,使用水和环素吹剂.
- 使用MP2/aug-cc-pVDZ进行孔稳定性和邻近效应的计算分析.
- 基于Voronoi图形的机器学习算法的开发,用于分析数字图像中的孔邻近效应距离.
主要成果:
- 在计算的Voronoi邻近效应距离和聚氨泡的导热系数之间发现了强烈的相关性.
- 开发的算法成功预测了平均邻近效应距离.
- 与扫描电子显微镜 (SEM) 相比,基于手机的成像和Python算法提供了更快,非破坏性的分析.
结论:
- 沃罗诺伊邻近效应距离是预测聚氨泡的导热率的可靠指标.
- 这种新且易于使用的方法为泡特性提供了一种快速而非破坏性的方法.
- 这些发现使得使用标准手机成像来预测类似的聚氨泡的导热率.
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