金属损失缺陷检测和深度估计使用冷却受腐蚀刺激的钢样的多光谱图像分析
Shamendra Egodawela1, Amirali K Gostar2, H A D Samith Buddika3
1School of Engineering, RMIT University, Melbourne, 3001, Australia. shamendra.egodawela@rmit.edu.au.
Scientific reports
|July 4, 2025
概括
这项研究引入了一种多光谱成像方法,以准确测量钢的腐蚀深度. 机器学习,特别是Feedforward神经网络,使用光谱数据有效量化金属损失的严重程度.
科学领域:
- 材料科学 材料科学 材料科学
- 腐蚀工程 腐蚀工程
- 计算成像技术的成像
背景情况:
- 精确评估钢中腐蚀引起的金属损失对于结构完整性至关重要.
- 从二维图像中估计缺陷深度仍然是当前成像技术的一个重大挑战.
研究的目的:
- 开发和验证一种多光谱成像方法,用于精确量化钢的腐蚀强度.
- 评估各种机器学习回归模型在估计缺陷深度方面的有效性.
主要方法:
- 使用了一种具有人工缺陷的钢铁试验样本,经过加速腐蚀.
- 采用多光谱成像 (MSI) 来捕捉冷却后刺激的光谱反应.
- 应用主要组件分析 (PCA) 和机器学习回归 (SVR,DTR,RF,GBR,FNN) 用于深度估计.
主要成果:
- 在特定波长的反射强度与缺陷的存在和严重程度相关.
- 输送神经网络 (FNN) 获得了最高的准确性,根平均平方误差 (RMSE) 为0.2829和R2为0.976.
- 对缺陷量化的最佳光谱范围被确定为700 nm900 nm.
结论:
- 多光谱成像与机器学习相结合,为量化钢中腐蚀引起的金属损失提供了强大的解决方案.
- FNN模型在估计缺陷深度方面表现出卓越的性能,提高了严重性评估能力.
- 这种技术有可能改善钢结构结构的非破坏性评估和结构健康监测.
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