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首页> 外文期刊>Journal of Nondestructive Evaluation >Detection of Internal Defects in Carbon Fiber Reinforced Plastic Slabs Using Background Thermal Compensation by Filtering and Support Vector Machines
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Detection of Internal Defects in Carbon Fiber Reinforced Plastic Slabs Using Background Thermal Compensation by Filtering and Support Vector Machines

机译:通过滤波和支持向量机使用背景热补偿碳纤维增强塑料板内缺陷的检测

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摘要

Composite materials, such as Carbon-Fiber-Reinforced Plastic (CFRP), are used in many industries because they have advantages over more traditional materials. However, CFRPs may have structural flaws, because mechanical stress or manufacturing process, that represent an important risk for the safe operation of CFRP-made structures. This study analyzes the performance in detection of internal defects, by means of training and operating Support Vector Machines (SVM) with thermal contrast information obtained from Background Thermal Compensation by Filtering (BTCF) technique. IR images were obtained by using an Active Pulsed Thermography (PT) system, under two different conditions, for inspection of a 20x20cm CFRP slab with 25 squared Teflon insertions as emulated defects. Detection results show that the combination of BTCF contrast technique and SVM classifier leads to a greater sensibility (22 of 23 defects considered) than other combinations of thermal contrast, feature selection and classifiers proposed in previous works.
机译:复合材料,如碳纤维增强塑料(CFRP),在许多行业中使用,因为它们具有更具传统材料的优势。然而,CFRP可能具有结构缺陷,因为机械应力或制造过程,这代表了CFRP制造的结构安全操作的重要风险。本研究通过训练和操作支持向量机(SVM)分析了内部缺陷检测的性能,通过滤波(BTCF)技术从背景热补偿中获得的热对比信息。通过在两个不同条件下使用有源脉冲热成像(PT)系统获得IR图像,用于检查20x20cm CFRP板,其具有25个平方的Teflon插入作为模拟缺陷。检测结果表明,BTCF对比度技术和SVM分类器的组合导致更大的敏感性(考虑的22个缺陷22),而不是先前作品中提出的热对比度,特征选择和分类器的其他组合。

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