Image enhancement method for laser infrared thermography defect detection in aviation composites

Wang, Qiang; Hu, Qiuping; Qiu, Jinxing; Pei, Cuixiang; Zhou, Hongbin; Xia, Ruicong; Lu, Jia

doi:10.1117/1.oe.58.10.103104

Cited by 8 publications

(4 citation statements)

References 24 publications

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“…Some methods are based on preprocessing the data from defect-free areas in the infrared range [26]. The study results have shown that this method effectively reduces the effect of uneven laser energy distribution on the defect detection efficiency and improves the quality of information about internal defects.…”

Section: Literature Reviewmentioning

confidence: 99%

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Divin,

Karpov,

Zakharov

et al. 2024

Engineering Technologies and Systems

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Introduction. Control of the presence of subsurface defects in products from composite materials is necessary for verification of products after release from production and in the process of operation. Aim of the Study. The purpose of the presented work is to estimate the parameters of subsurface defects using local laser thermography, suitable for quality control of both small objects and suspicious areas of large objects with curved surfaces. Materials and Methods. The laboratory setup on which this work was carried out includes a robotic arm, a COX CG640 thermal imager and a 3 W laser. The method was tested on a fiberglass sample with introduced delamination defect simulations located at different depths below the surface. By means of computer modeling rational parameters of thermographic control were selected, providing reliable detection of the defect at a depth of up to 3 mm under the surface of the composite sample. Results. Numerical modeling of surface temperature field induced by moving focused laser beam was carried out using COMSOL software package. It showed that laser beam with 3 W power moving at 5 mm/s provided the thermal contrast sufficient to detect the defects at the depth up to 3 mm. The obtained experimental data are in satisfactory agreement with numerical modeling both qualitatively and quantitatively. Experimental data were used to construct a regression model for determining defect depth based on the maximal thermal contrast and the time interval between heating and the contrast maximum. Discussion and Conclusion. The results obtained in this work allow us to propose a technique for detecting defects in fiberglass plastics and estimating their depth. The coefficient of determination for the obtained regression model was found to be equal to 0.95, and the mean square error of the metric was no more than 0.016 mm2. The use of a robotic arm to scan objects will make it possible to investigate objects with complex curved surfaces.

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Section: Literature Reviewmentioning

confidence: 99%

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Divin,

Karpov,

Zakharov

et al. 2024

Engineering Technologies and Systems

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“…In addition, IRT is real-time, noninvasive, and does not expose living organisms to strong, harmful radiation like x-rays [ 20 ]. Over the past few years, IRT has been widely used in the fields of medicine [ 21 , 22 , 23 ], agriculture [ 24 , 25 , 26 , 27 , 28 ], architecture [ 29 , 30 ], energy [ 31 ], and aviation [ 32 , 33 ]. IRT imaging technology has also been applied to animal production [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Progress on Infrared Imaging Technology in Animal Production: A Review

Zheng

Zhou

Jiang

et al. 2022

Sensors

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Infrared thermography (IRT) imaging technology, as a convenient, efficient, and contactless temperature measurement technology, has been widely applied to animal production. In this review, we systematically summarized the principles and influencing parameters of IRT imaging technology. In addition, we also summed up recent advances of IRT imaging technology in monitoring the temperature of animal surfaces and core anatomical areas, diagnosing early disease and inflammation, monitoring animal stress levels, identifying estrus and ovulation, and diagnosing pregnancy and animal welfare. Finally, we made prospective forecast for future research directions, offering more theoretical references for related research in this field.

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“…They stated that principal component thermography (PCT) [6] after the noise reduction could enhance the results. Wang et al [7] used sequence differential pre-processing, which was combined with cold image subtraction (CIS) [8], to provide better thermal data for post-processing approaches in laser infrared thermography. They evaluated the quality of the image after the combination of pre-processing with pulsed phase thermography (PPT) [9] or PCT and found that pre-processing improved some results.…”

Section: Introductionmentioning

confidence: 99%

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers

Ebrahimi

Fleuret

Klein

et al. 2021

Sensors

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Pulsed thermography is a commonly used non-destructive testing method and is increasingly studied for the assessment of advanced materials such as carbon fibre-reinforced polymer (CFRP). Different processing approaches are proposed to detect and characterize anomalies that may be generated in structures during the manufacturing cycle or service period. In this study, matrix decomposition using Robust PCA via Inexact-ALM is investigated as a pre- and post-processing approach in combination with state-of-the-art approaches (i.e., PCT, PPT and PLST) on pulsed thermography thermal data. An academic sample with several artificial defects of different types, i.e., flat-bottom-holes (FBH), pull-outs (PO) and Teflon inserts (TEF), was employed to assess and compare defect detection and segmentation capabilities of different processing approaches. For this purpose, the contrast-to-noise ratio (CNR) and similarity coefficient were used as quantitative metrics. The results show a clear improvement in CNR when Robust PCA is applied as a pre-processing technique, CNR values for FBH, PO and TEF improve up to 164%, 237% and 80%, respectively, when compared to principal component thermography (PCT), whilst the CNR improvement with respect to pulsed phase thermography (PPT) was 77%, 101% and 289%, respectively. In the case of partial least squares thermography, Robust PCA results improved not only only when used as a pre-processing technique but also when used as a post-processing technique; however, this improvement is higher for FBHs and POs after pre-processing. Pre-processing increases CNR scores for FBHs and POs with a ratio from 0.43% to 115.88% and from 13.48% to 216.63%, respectively. Similarly, post-processing enhances the FBHs and POs results with a ratio between 9.62% and 296.9% and 16.98% to 92.6%, respectively. A low-rank matrix computed from Robust PCA as a pre-processing technique on raw data before using PCT and PPT can enhance the results of 67% of the defects. Using low-rank matrix decomposition from Robust PCA as a pre- and post-processing technique outperforms PLST results of 69% and 67% of the defects. These results clearly indicate that pre-processing pulsed thermography data by Robust PCA can elevate the defect detectability of advanced processing techniques, such as PCT, PPT and PLST, while post-processing using the same methods, in some cases, can deteriorate the results.

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Image enhancement method for laser infrared thermography defect detection in aviation composites

Cited by 8 publications

References 24 publications

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Using Laser Point Scanning Thermography for Quality Monitoring of Products Made of Composite Materials

Progress on Infrared Imaging Technology in Animal Production: A Review

Data Enhancement via Low-Rank Matrix Reconstruction in Pulsed Thermography for Carbon-Fibre-Reinforced Polymers

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