Automatic defect detection in a steel sample using frequency-modulated thermal wave imaging

IF 1 4区 工程技术 Q4 INSTRUMENTS & INSTRUMENTATION Insight Pub Date : 2023-09-01 DOI:10.1784/insi.2023.65.9.501
J Ahmad, R Mulaveesala
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引用次数: 0

Abstract

Non-stationary thermal wave imaging (NSTWI) techniques are primarily used to assess material properties and structural integrity without damaging a structure. Frequency-modulated thermal wave imaging (FMTWI) is a well-known NSTWI approach that uses a low-peak power heat source to examine structures in a reasonable experimentation time. Recently, various methods, such as pulse compression, Fourier transform, principal component analysis (PCA) and independent component analysis (ICA), have been introduced to handle the non-linearity of transient thermal signatures. However, handling non-linearity and developing a fully automatic defect detection system remains very challenging due to certain limitations of the aforementioned methods. To overcome these problems, this paper proposes an artificial neural network (ANN) for the identification of subsurface flaws in a mild steel sample investigated using the FMTWI approach. The accuracy and the performance of the proposed neural network (NN) are evaluated through a confusion matrix and region of convergence (ROC) analysis for the classification of defective and healthy pixels in an infrared image sequence. The developed NN model has achieved 99.7% accuracy in classifying the pixels correctly.
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利用调频热波成像技术对钢样进行自动缺陷检测
非稳态热波成像(NSTWI)技术主要用于在不损坏结构的情况下评估材料性能和结构完整性。调频热波成像(FMTWI)是一种众所周知的NSTWI方法,它使用低峰值功率热源在合理的实验时间内检查结构。近年来,脉冲压缩、傅里叶变换、主成分分析(PCA)和独立成分分析(ICA)等方法被用于处理瞬态热特征的非线性。然而,由于上述方法的某些局限性,处理非线性和开发全自动缺陷检测系统仍然是非常具有挑战性的。为了克服这些问题,本文提出了一种人工神经网络(ANN)来识别使用FMTWI方法研究的低碳钢样品的亚表面缺陷。通过混淆矩阵和收敛区域(ROC)分析对红外图像序列中的缺陷像素和健康像素进行分类,评估了所提出的神经网络(NN)的准确性和性能。所开发的神经网络模型对像素的正确分类准确率达到99.7%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Insight
Insight 工程技术-材料科学:表征与测试
CiteScore
1.50
自引率
9.10%
发文量
0
审稿时长
2.8 months
期刊介绍: Official Journal of The British Institute of Non-Destructive Testing - includes original research and devlopment papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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