Calculating probability of detection of short surface cracks using inductive thermography

Oswald-Tranta, Beate; Hackl, Alexander; Olavera, Pablo Lopez de Uralde; Gorostegui-Colinas, E.; Rosell, Anders

doi:10.1080/17686733.2022.2152259

Cited by 6 publications

(8 citation statements)

References 14 publications

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“…The length of a crack is determined by the distance of the maxima of the hot spots at the crack ends, see Figure 4b. The phase contrast, which is the difference of the maximum phase of the hot spots and the minimum phase of the crack region, is characteristic for the depth of the defect 4,14,15 . This phase contrast value is determined in the post-processing step.…”

Section: Classification Results For Experimental Datamentioning

confidence: 99%

“…Thereby, the inductive thermography provides a very good possibility to estimate the crack depth. Furthermore it is to note that for short cracks not only the crack depth, but also the crack length affects the phase contrast 11 .…”

Section: Validation With Ct Resultsmentioning

confidence: 99%

“…100 ms induction heating pulses are applied to the sample. A more detailed description of the experimental setup has been described in an earlier publication 11 .…”

Section: Experimental Setup and Samplesmentioning

confidence: 99%

“…Afterwards, the sample was straightened again, Figure 1a shows one of the samples. A more detailed description of the samples and the so-called Varestraint test for creating samples with nearly natural cracks, has been presented in an earlier paper 11,14 .…”

Section: Experimental Setup and Samplesmentioning

confidence: 99%

See 3 more Smart Citations

Convolutional neural network for automated surface crack detection in inductive thermography

Oswald-Tranta¹,

Olavera²,

Gorostegui-Colinas³

et al. 2023

Thermosense: Thermal Infrared Applications XLV

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This work presents a convolutional neural network (CNN), trained on simulated data and used for the detection of cracks resulted by inductive thermography measurements. In inductive thermography the sample under study is heated with a short heating pulse and an infrared (IR) camera records the emitted surface radiation during both heating and cooling. The recorded IR sequence is then evaluated to a phase image using Fourier transform. In phase images, short surface cracks become visible due to the hot spots around the defect tips and due to the low phase value along the crack line. For the training of a deep neural network many images are necessary, which should be different from the images to be evaluated. This is why FEM simulations have been carried out varying crack length, depth and inclination angle. Additional Gaussian noise and augmentation have been added to these simulated images before using them to train a CNN. Samples with real cracks along a weld have been created in Inconel 718, and the CNN, trained on the simulation results, has been used for semantic segmentation of these real samples’ phase images, in order to identify the defects. Additionally, the samples were investigated by computer tomography, and this 3D information of the cracks is compared to the phase image results.

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Section: Classification Results For Experimental Datamentioning

confidence: 99%

Section: Validation With Ct Resultsmentioning

confidence: 99%

“…100 ms induction heating pulses are applied to the sample. A more detailed description of the experimental setup has been described in an earlier publication 11 .…”

Section: Experimental Setup and Samplesmentioning

confidence: 99%

Section: Experimental Setup and Samplesmentioning

confidence: 99%

See 2 more Smart Citations

Convolutional neural network for automated surface crack detection in inductive thermography

Oswald-Tranta¹,

Olavera²,

Gorostegui-Colinas³

et al. 2023

Thermosense: Thermal Infrared Applications XLV

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show abstract

“…It is well visible that the phase contrast depends on the crack length and the crack depth [6]. Additional simulations were calculated to investigate how other parameters, such as the shape of the crack, its inclination angle, and the angle between the crack line and the eddy current flow direction, affect the phase contrast [6,7].…”

Section: Finite Element Simulationsmentioning

confidence: 99%

Comparison of Inductive Thermography and Computer Tomography Results for Short Surface Cracks

Oswald-Tranta

2023

The 17th International Workshop on Advanced Infrared Technology and Applications

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EHSGNet: A novel edge and high-level semantic guided network for CFRP subsurface defects detection

Liu,

Song,

Yang

et al. 2024

Measurement

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Calculating probability of detection of short surface cracks using inductive thermography

Cited by 6 publications

References 14 publications

Convolutional neural network for automated surface crack detection in inductive thermography

Convolutional neural network for automated surface crack detection in inductive thermography

Comparison of Inductive Thermography and Computer Tomography Results for Short Surface Cracks

EHSGNet: A novel edge and high-level semantic guided network for CFRP subsurface defects detection

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