Experimentally validated defect depth estimation using artificial neural network in pulsed thermography

Saeed, Numan; Abdulrahman, Yusra; Amer, Saed; Omar, Mohammed

doi:10.1016/j.infrared.2019.03.014

Cited by 36 publications

(17 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Temporal evolutions can be observed from the defective regions and subsurface sound regions. A thermal contrast is acquired as a feature vector which is obtained distinctly via the thermal value from the defective region subtracted from the corresponding value from the surrounding sound region [11] as indicated in Equation 1, where Td(t) is the temperature value on the pixel point of the defect area. The temperature value on the reference point of the sound area is T s (t) Then the ∆T(t) is the absolute thermal contrast extracted from the defect and sound region.…”

Section: Pulsed Thermographymentioning

confidence: 99%

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Fang

Maldague

2020

Applied Sciences

View full text Add to dashboard Cite

Infrared thermography has already been proven to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity, and low cost. However, the thorniest issue for the wider application of IRT is quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (GRUs) in composite material samples via pulsed thermography (PT). Finite Element Method (FEM) modeling provides the economic examination of the response pulsed thermography. In this work, Carbon Fiber Reinforced Polymer (CFRP) specimens embedded with flat bottom holes are stimulated by a FEM modeling (COMSOL) with precisely controlled depth and geometrics of the defects. The GRU model automatically quantified the depth of defects presented in the stimulated CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

show abstract

Section: Pulsed Thermographymentioning

confidence: 99%

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Fang

Maldague

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Temporal evolutions can be observed from the defective regions and subsurface sound regions. A thermal contrast is acquired as a feature vector which is obtained distinctly via the thermal value from the defective region subtracted from the corresponding value from the surrounding sound region [9] as indicated in Eq (1), where Td(t) is the temperature value on the pixel point of the defect area. The temperature value on the reference point of the sound area is ( ) Then the (t) is the absolute thermal contrast extracted from the defect and sound region.…”

Section: Pulsed Thermographymentioning

confidence: 99%

“…The GRU neural network in Figure 6 are structured as follows [13]. In equations (6)- (9), , represent the update, reset gate units respectively. ( ) , ( ) , are the weight factors for each gate unit.…”

Section: Gated Recurrent Unit Model With Depth Estimatormentioning

confidence: 99%

Defect Depth Estimation in Infrared Thermography with Deep Learning

Fang¹,

Maldague²

2020

Preprint

View full text Add to dashboard Cite

Infrared thermography has already been proved to be a significant method in non-destructive evaluation since it gives information with immediacy, rapidity and low cost. However, the thorniest issue for wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Unites (GRU) in composite material samples via pulsed thermography (PT). Carbon Fiber Reinforced Polymer (CFRP) embedded with flat bottom holes were designed via Finite Element Method (FEM) modeling in order to precisely control the depth and geometrics of the defects. The GRU model automatically quantify the depth of defects presented in the Plexiglasses materials. The proposed evaluated the accuracy and performance of synthetic plexiglasses data from FEM for defect depth predictions.

show abstract

“…To achieve more accurate results of filtration it is necessary to employ more precise analytical models for evolution of the temperature contrast. The analytical model (12) contains the depth of the defect as one of the governing parameter defining the behavior of the temperature contrast evolution. However, in real applications of pulsed thermography for defect detection this value is often unknown.…”

Section: Kalman-based Filtration Techniquementioning

confidence: 99%

“…More recent approaches for identification of subsurface defects are based on adaptive algorithms and artificial neuron networks. In [12] an artificial neuron network is applied to the estimation of the defect's depth. Results of the numerical simulation obtained according to the mathematical model of the pulsed thermography procedure were used for generation of ideal (without noise) sets of training data.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation of subsurface defect identification by pulsed thermography and improvement of this technique for noisy data

Plekhov

Kostina

Aizikovich

2019

Frattura Ed Integrità Strutturale

View full text Add to dashboard Cite

Pulsed thermography is an active non-destructive technique which uses optical excitation source to stimulate heating of the object under investigation. This work is devoted to the simulation of the pulsed thermography method in a steel plate with a ceramic coating containing artificial defects of various depths and sizes. The simulation has been carried out on the base of a model which takes into account complex heat exchange of the sample with the surrounding by convection, conduction and radiation. Comparison of the temperature contrast with the experimental data has shown that the results are in a good qualitative and quantitative agreement in all stages of the cooling process. Due to the fact that the temperature contrast is often susceptible to surface noise of various nature the Kalman-based signal processing technique was developed. The comparative analysis has shown that the proposed filtration technique provides a better signal-to-noise ratio in comparison to the considered well-known techniques of signal reconstruction when proper calibration of the filtration parameters is carried out.

show abstract

Experimentally validated defect depth estimation using artificial neural network in pulsed thermography

Cited by 36 publications

References 24 publications

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

A Method of Defect Depth Estimation for Simulated Infrared Thermography Data with Deep Learning

Defect Depth Estimation in Infrared Thermography with Deep Learning

Numerical simulation of subsurface defect identification by pulsed thermography and improvement of this technique for noisy data

Contact Info

Product

Resources

About