Modeling and optimization of open crack detection by flying spot thermography

Thiam, Abdoulahad; Kneip, Jean-Christophe; Cicala, Eugèn; Caulier, Y.; Jouvard, Jean-Marie; Matteı, Simone

doi:10.1016/j.ndteint.2017.03.010

Cited by 18 publications

(9 citation statements)

References 10 publications

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“…Thanks to their powerful capabilities in feature extraction, machine learning algorithms have been widely utilized in thermal image analysis [4,5,[9][10][11][12][13]. A variety of image processing methods have been successfully applied for feature extraction of the thermal images such as the discrete wavelet transform method [14], the K-means clustering algorithm [15] and the thermal resistance effect model-based method [16]. The aforementioned image processing algorithms are normally applied to handle one specific thermal image through the ECPT-based NDT process [17].…”

Section: Introductionmentioning

confidence: 99%

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

et al. 2021

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As a popular nondestructive testing (NDT) technique, thermal imaging test demonstrates competitive performance in crack detection, especially for detecting subsurface cracks. In thermal imaging test, the temperature of the crack area is higher than that of the non-crack area during the NDT process. By extracting the features of the thermal image sequences, the temperature curve of each spatial point is employed for crack detection. Nevertheless, the quality of thermal images is influenced by the noises due to the complex thermal environment in NDT. In this paper, a modified generative adversarial network (GAN) is employed to improve the image segmentation performance. To improve the feature extraction ability and alleviate the influence of noises, a penalty term is put forward in the loss function of the conventional GAN. A data preprocessing method is developed where the principle component analysis algorithm is adopted for feature extraction. The data argumentation technique is utilized to guarantee the quantity of the training samples. To validate its effectiveness in thermal imaging NDT, the modified GAN is applied to detect the cracks on the eddy current pulsed thermography NDT dataset.

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Section: Introductionmentioning

confidence: 99%

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

et al. 2021

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“…By scanning the laser spot along the sample surface at constant velocity (the so-called flying spot thermography), large parts can be analyzed in a fast way [ 1 , 2 ]. This method is well suited to image cracks by showing the crack length along the surface [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. A more challenging task is to evaluate the width and depth of the detected crack.…”

Section: Introductionmentioning

confidence: 99%

Sizing the Depth and Width of Narrow Cracks in Real Parts by Laser-Spot Lock-In Thermography

Colom

Rodríguez‐Aseguinolaza

Mendioroz

et al. 2021

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We present a complete characterization of the width and depth of a very narrow fatigue crack developed in an Al-alloy dog bone plate using laser-spot lock-in thermography. Unlike visible micrographs, which show many surface scratches, the thermographic image clearly identifies the presence of a single crack about 1.5 mm long. Once detected, we focus a modulated laser beam close to the crack and we record the temperature amplitude. By fitting the numerical model to the temperature profile across the crack, we obtain both the width and depth simultaneously, at the location of the laser spot. Repeating the process for different positions of the laser spot along the crack length, we obtain the distribution of the crack width and depth. We show that the crack has an almost constant depth (0.7 mm) and width (1.5 μm) along 0.7 mm and features a fast reduction in both quantities until the crack vanishes. The results prove the ability of laser-spot lock-in thermography to fully characterize quantitatively narrow cracks, even below 1 μm.

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“…This technique allows detecting cracks a few micrometers wide [17]. Recently, several approaches to characterize the geometrical parameters of the crack (depth, length, width, orientation…) have been introduced by several research groups [21][22][23][24][25][26][27]. They take advantage of the asymmetry of the temperature field at both sides of the crack arising from the thermal resistance produced by the crack, which partially blocks heat flux when the laser spot approaches the crack.…”

Section: Introductionmentioning

confidence: 99%

Fast sizing of the width of infinite vertical cracks using constant velocity Flying-Spot thermography

González

Mendioroz

Sommier

et al. 2019

NDT & E International

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Modeling and optimization of open crack detection by flying spot thermography

Cited by 18 publications

References 10 publications

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

A New GAN-Based Approach to Data Augmentation and Image Segmentation for Crack Detection in Thermal Imaging Tests

Sizing the Depth and Width of Narrow Cracks in Real Parts by Laser-Spot Lock-In Thermography

Fast sizing of the width of infinite vertical cracks using constant velocity Flying-Spot thermography

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