钢板涂层材料的厚度特征化与太赫兹时域反射光谱基于BP神经网络
Xuelei Jiang1,2, Ying Xu1,2, Hang Hu1,2
1School of Civil and Environmental Engineering, Harbin Institute of Technology, Shenzhen 518055, China.
Sensors (Basel, Switzerland)
|August 10, 2024
概括
本研究使用太赫兹时域反射光谱和BP神经网络来准确测量钢板涂层厚度,从而能够以超过96%的精度检测缺陷并进行质量评估.
科学领域:
- 材料科学 材料科学 材料科学
- 非破坏性测试 不破坏性测试
- 频谱学是一种光谱学.
背景情况:
- 精确监测钢板涂层厚度对于建筑质量控制和耐用性至关重要.
- 传统方法在精度和可视化方面可能存在局限性.
研究的目的:
- 使用太赫兹时域反射光谱和BP神经网络,开发钢板涂层厚度的定量可视化方法.
- 为了克服传统太赫兹反射光谱的折射率依赖,用于厚度计算.
主要方法:
- 太赫兹时域反射光谱与反向传播 (BP) 神经网络模型相结合.
- 建立信号特征和涂层厚度之间的功能关系.
- 测量各种涂料的折射率 (黑色涂料,白色涂料,环氧树脂,).
主要成果:
- 在预测涂层厚度方面,BP神经网络模型实现了超过96%的准确性.
- 成功可视化了涂层厚度分布和提取的特征值.
- 证明了识别厚度异常和定位像划痕和异物等缺陷的能力.
结论:
- 拟议的太赫兹光谱和BP神经网络方法为钢板涂层厚度评估提供了高度准确和可视化的方法.
- 这种技术增强了涂层层的监测和质量评估,提高了缺陷检测能力.
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