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基于脉冲多频流测试和k-最近邻近方法的材料缺陷的快速识别

Jacek M Grochowalski¹, Tomasz Chady¹

¹Faculty of Electrical Engineering, West Pomeranian University of Technology in Szczecin, 70-313 Szczecin, Poland.

Materials (Basel, Switzerland)

|October 28, 2023

概括

这项研究使用脉冲多频激发和光谱流测试 (PMFES-ECT) 与k-近邻 (k-NN) 准确估计导电材料的缺陷参数.

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
机器学习机器学习

背景情况:

厄迪电流测试 (ECT) 是一种常见的非破坏性方法.
准确的缺陷参数估计对于材料完整性至关重要.
监督学习为增强ECT分析提供了潜力.

研究的目的:

开发和评估一种用于估计导电材料缺陷参数的方法.
将脉冲式多频激发和光谱电流测试 (PMFES-ECT) 与机器学习相结合.
评估拟议方法的分类准确性.

主要方法:

为传感器样本模拟创建了一个三维有限元素方法 (FEM) 模型.
模拟结果被用作k-Nearest Neighbors (k-NN) 算法的训练数据.
应用k-NN算法对测量数据进行缺陷参数估计.

主要成果:

该研究成功地使用PMFES-ECT和k-NN进行缺陷参数估计.
FEM模拟为机器学习模型提供了有效的训练数据.
对各种预测组合的分类准确性进行了评估.

结论:

组合的PMFES-ECT和k-NN方法对于缺陷表征是有效的.
这种方法为导电材料的自动缺陷分析提供了一个有希望的途径.
进一步的研究可以探索不同的机器学习算法和功能集.

关键词:

流流流是一种流.有限元素分析的研究.k-最近邻居 (k-NN) 算法多频激发和光谱图流测试.非破坏性测试是指非破坏性测试.数字模拟的数字模拟.

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