Spatial Structure Analysis for Subsurface Defect Detection in Materials Using Active Infrared Thermography and Adaptive Fixed-Rank Kriging

Chun-Han Chang, S. Sfarra, Nan-Jung Hsu, Yuan Yao
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Abstract

: The study focuses on reducing noise and nonstationary backgrounds in data collected through active infrared thermography (AIRT) for defect detection in materials. The authors employ adaptive fixed-rank kriging to analyze a sequence of thermograms obtained in the AIRT experiment. Using basis functions derived from thin-plate splines, the data features are represented at various resolution levels, resulting in a concise spatial covariance function representation. Eigenfunctions are then derived from the estimated covariance function to capture spatial structures at different scales. Visualizing these eigenfunctions highlights defect information. The authors validate their approach through a pulsed thermography experiment on a carbon-fiber-reinforced plastic (CFRP) sample, demonstrating its effectiveness in detecting defects.
利用主动红外热成像技术和自适应固定秩克里金法进行空间结构分析,以检测材料的次表层缺陷
:这项研究的重点是减少通过主动红外热成像技术(AIRT)收集的数据中的噪声和非稳态背景,以检测材料中的缺陷。作者采用自适应固定秩克里金法对 AIRT 实验中获得的一系列热图进行分析。利用从薄板样条曲线中提取的基函数,在不同的分辨率水平上表示数据特征,从而得出简洁的空间协方差函数表示法。然后从估计的协方差函数推导出特征函数,以捕捉不同尺度的空间结构。对这些特征函数进行可视化处理,可突出显示缺陷信息。作者通过对碳纤维增强塑料 (CFRP) 样品进行脉冲热成像实验,验证了他们的方法,证明了其在检测缺陷方面的有效性。
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