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Detection of Internal Defects in Carbon Fiber Reinforced Plastic Slabs Using Background Thermal Compensation by Filtering and Support Vector Machines

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Abstract

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 20 × 20 cm 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.

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Acknowledgements

The authors would like to express their gratitude to the multipolar infrared vision (MIVIM) research group at the University of Laval in Quebec, Canada, for providing their facilities to produce the thermal images used in this study.

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  1. Escuela de Ingeniería Eléctrica y Electrónica, Universidad del Valle, Calle 13 #100-00, Ciudad Universitaria Meléndez, Cali, Colombia

    Juan-Camilo Forero-Ramírez, Andrés-David Restrepo-Girón & Sandra-Esperanza Nope-Rodríguez

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  1. Juan-Camilo Forero-Ramírez
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  2. Andrés-David Restrepo-Girón
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  3. Sandra-Esperanza Nope-Rodríguez
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Correspondence to Sandra-Esperanza Nope-Rodríguez.

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Forero-Ramírez, JC., Restrepo-Girón, AD. & Nope-Rodríguez, SE. Detection of Internal Defects in Carbon Fiber Reinforced Plastic Slabs Using Background Thermal Compensation by Filtering and Support Vector Machines. J Nondestruct Eval 38, 33 (2019). https://doi.org/10.1007/s10921-019-0569-6

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