Fast Characterization of the Width of Vertical Cracks Using Pulsed Laser Spot Infrared Thermography

Pech-May, Nelson W.; Oleaga, A.; Mendioroz, A.; Salazar, A.

doi:10.1007/s10921-016-0344-x

Cited by 41 publications

(22 citation statements)

References 26 publications

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“…The relationship (2), which is rigorously true in absence of the crack, eventually comes out assuming the approximate correctness of the β model for the cracked 2D-region, on the x axis (x 0 being the crack abscissa):…”

Section: Resultsmentioning

confidence: 99%

“…The latter is an active technique employed for detecting the presence of cracks in metallic specimens [1,2], also called flying-spot camera or flying-spot laser thermography when the laser beam scans a surface in a more or less automatic way [3,4]. The technique consists in heating a very small area of a material by means of a laser source.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Laser-Induced Thermography for Quantitative Detection of Cracks in Building Materials

Palombi

Inglese²,

Raimondi

et al. 2019

The 15th International Workshop on Advanced Infrared Technology and Applications

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Laser-induced thermography is a an active technique using a laser source to heat a very small area on a side of a crack in a building material. The presence of a crack is easily detected as a sharp change in the temperature due to its insulating nature, but no information about its depth is directly available from the thermal image. The method described in this paper uses a heuristic form of the temperature on the surface of the heated specimen, which is transformed to a two-dimensional distribution. Then, a relation is used (called β -tool) between the thermal gap across the crack and the unknown depth of the damage. The purpose is that of making it possible to distinguish between shallow and deep fractures (more than 15mm deep).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Laser-Induced Thermography for Quantitative Detection of Cracks in Building Materials

Palombi

Inglese²,

Raimondi

et al. 2019

The 15th International Workshop on Advanced Infrared Technology and Applications

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show abstract

“…Finally, they focus on applying computer vision or traditional machine learning algorithms on crack detection. Pech-May et al [16] used pulsed laser infrared to measure the width of vertical cracks and fit the depth of the crack using the least square of the surface temperature. Pahlberg et al [17] achieved the detection of cracks in oak floor sheets by using the ultrasonic excitation thermal imaging technology.…”

Section: Crack Defects Are the Most Common And Dangerous Defectsmentioning

confidence: 99%

Infrared Thermal Imaging-Based Crack Detection Using Deep Learning

et al. 2019

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Vision-based approaches are widely used in steel crack detection. After processing the images taken by the camera, the superficial defects can be detected. Due to the common limitation of the nature of photographic images, internal features of objects cannot be fully discovered. In order to overcome the drawbacks of vision-based methods, this work presents an approach for detecting cracks in infrared thermal imaging steel sheets using Convolutional Neural Networks (CNN). Firstly, a horizontal heat conduction method is researched to thermally excite the surface of the steel sheet, and a rolling electric heating device is developed to be a thermal excitation source. Secondly, we analyze the temperature difference between normal area and different crack depths, and study the influence of temperature change. Thirdly, 3000 infrared thermograms labeled for penetrating cracks, non-penetrating cracks, and surface scratches are fabricated into a databank. Then, the CNN is trained and validated on the data bank. Finally, improving the original Faster Region-based Convolutional Neural Network (Faster R-CNN) network, the feature maps of multiple levels in the feature extraction network are aggregated, and the anchor selection scheme of the Region Proposal Network (RPN) is adjusted from 9 to 25. The robustness of the improved Faster R-CNN is demonstrated by evaluating the detection results on the 125 images outside of the data bank, the accuracy and Mean Average Precision (mAP) are 95.54% and 92.41%, respectively, which outperform the original algorithm by increasing the accuracy of 3.18% and mAP of 1.88%.INDEX TERMS Machine learning, steel plate crack, infrared thermal imaging, target detection, faster R-CNN.

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“…We have calculated the evolution of the surface temperature distribution when the sample moves ant constant velocity and is illuminated by a cw laser spot, neglecting heat losses by convection and radiation. Following the procedure described in section 2, we have started from the time domain solution [2] and we have calculated the convolution product, followed by a Galilean transformation of coordinates. According to the sensitivity analysis we have conducted, we propose to fit the surface temperature profile, perpendicular to the crack, through the center of the laser spot, when the distance between the center of the excitation and the crack is approximately equal to the radius of the spot.…”

Section: Sizing the Width Of Infinite Vertical Cracksmentioning

confidence: 99%

Thermography on moving samples: recent developments for quantitative evaluation

Mendioroz¹,

Salazar²

2019

Proceedings of QIRT Asia 2019

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We propose a methodology to evaluate the thermal diffusivity and to size the thermal resistance of vertical cracks in samples that move at constant velocity, as they are illuminated by a continuous wave (cw) laser spot. This configuration is aimed at implementing in-line testing of industrial products that move at constant velocity, without stopping the production chain. First, we calculate the evolution of the surface temperature distribution of sound samples, and we propose a method to measure the in-plane thermal diffusivity of the material in the directions parallel and perpendicular to the motion. Second, we present calculations of the surface temperature distribution when the material contains an infinite vertical crack and we propose a methodology to size the width of the crack. In both cases, we present experimental data obtained on reference and calibrated samples that confirm the validity of the methods.

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Fast Characterization of the Width of Vertical Cracks Using Pulsed Laser Spot Infrared Thermography

Cited by 41 publications

References 26 publications

Laser-Induced Thermography for Quantitative Detection of Cracks in Building Materials

Laser-Induced Thermography for Quantitative Detection of Cracks in Building Materials

Infrared Thermal Imaging-Based Crack Detection Using Deep Learning

Thermography on moving samples: recent developments for quantitative evaluation

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