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

2020 ◽  
Vol 10 (19) ◽  
pp. 6819 ◽  
Author(s):  
Qiang Fang ◽  
Xavier Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Pulsed Thermography ◽  
And Performance ◽  
Gated Recurrent Units ◽  
Non Destructive

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.

scholarly journals Defect Depth Estimation in Infrared Thermography with Deep Learning

2021 ◽  
Author(s):  
Qiang Fang ◽  
farima abdollahi-mamoudan ◽  
Xavier Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Pulsed Thermography ◽  
And Performance ◽  
Gated Recurrent Units ◽  
Non Destructive

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 (GRU) 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.

scholarly journals Defect Depth Estimation in Infrared Thermography with Deep Learning

2020 ◽  
Author(s):  
Qiang Fang ◽  
and Xavier. Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Reinforced Polymer ◽  
And Performance ◽  
Gated Recurrent Units ◽  
Non Destructive

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 wider application of IRT is the quantification. In this work, we proposed a specific depth quantifying technique by employing the Gated Recurrent Units (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 quantified the depth of defects presented in the CFRP material. The proposed method evaluated the accuracy and performance of synthetic CFRP data from FEM for defect depth predictions.

scholarly journals Defect Depth Estimation in Infrared Thermography with Deep Learning

2020 ◽  
Author(s):  
Qiang Fang ◽  
Xavier Maldague
Keyword(s):  
Infrared Thermography ◽  
Low Cost ◽  
Depth Estimation ◽  
Fiber Reinforced Polymer ◽  
Defect Depth ◽  
Fem Modeling ◽  
Flat Bottom ◽  
Reinforced Polymer ◽  
And Performance ◽  
Non Destructive

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.

scholarly journals A Method of Defect Depth Recognition in Active Infrared Thermography Based on GRU Networks

2021 ◽  
Vol 11 (14) ◽  
pp. 6387
Author(s):  
Li Xu ◽  
Jianzhong Hu
Keyword(s):  
Infrared Thermography ◽  
Visual Information ◽  
Evaluation Method ◽  
Recognition Performance ◽  
Principal Component ◽  
Defect Depth ◽  
Model Learning ◽  
Flat Bottom ◽  
Bp Network ◽  
Active Infrared Thermography

Active infrared thermography (AIRT) is a significant defect detection and evaluation method in the field of non-destructive testing, on account of the fact that it promptly provides visual information and that the results could be used for quantitative research of defects. At present, the quantitative evaluation of defects is an urgent problem to be solved in this field. In this work, a defect depth recognition method based on gated recurrent unit (GRU) networks is proposed to solve the problem of insufficient accuracy in defect depth recognition. AIRT is applied to obtain the raw thermal sequences of the surface temperature field distribution of the defect specimen. Before training the GRU model, principal component analysis (PCA) is used to reduce the dimension and to eliminate the correlation of the raw datasets. Then, the GRU model is employed to automatically recognize the depth of the defect. The defect depth recognition performance of the proposed method is evaluated through an experiment on polymethyl methacrylate (PMMA) with flat bottom holes. The results indicate that the PCA-processed datasets outperform the raw temperature datasets in model learning when assessing defect depth characteristics. A comparison with the BP network shows that the proposed method has better performance in defect depth recognition.

Defect Depth Quantification Using Pulsed Thermography

2012 ◽  
Vol 585 ◽  
pp. 72-76 ◽  
Author(s):  
D. Sharath ◽  
M. Menaka ◽  
B. Venkatraman
Keyword(s):  
Depth Estimation ◽  
Defect Size ◽  
Defect Depth ◽  
Pulsed Thermography ◽  
Full Field ◽  
First Derivative ◽  
Derivative Method ◽  
Aisi 316 ◽  
Materials Used ◽  
Defect Quantification

Pulsed Thermography is an advanced NDE technique which is becoming popular due to fast inspection rate, non contact nature and it gives full field image. Pulsed Thermography is successfully applied for defect detection, defect depth estimation, coating thickness evaluation and delamination detection in coatings but it is limited for evaluation of subsurface defects (of the order of few mm). In this paper we discuss the application of Pulsed Thermography for defect quantification and effect of defect size on it in AISI 316 grade SS which are important structural materials used in nuclear and other industries. Log First Derivative method is considered for defect depth quantification and the results are compared with Finite Difference Modeling carried out using ThermoCalc 6L software.

scholarly journals An Analytical Model for Defect Depth Estimation Using Pulsed Thermography

2016 ◽  
Vol 56 (6) ◽  
pp. 1111-1122 ◽  
Author(s):  
S.L. Angioni ◽  
F. Ciampa ◽  
F. Pinto ◽  
G. Scarselli ◽  
D.P. Almond ◽  
...  
Keyword(s):  
Analytical Model ◽  
Depth Estimation ◽  
Defect Depth ◽  
Pulsed Thermography

scholarly journals Latest Advances in Common Signal Processing of Pulsed Thermography for Enhanced Detectability: A Review

2021 ◽  
Vol 11 (24) ◽  
pp. 12168
Author(s):  
Yoonjae Chung ◽  
Seungju Lee ◽  
Wontae Kim
Keyword(s):  
Signal Processing ◽  
Infrared Thermography ◽  
Heat Diffusion ◽  
Thermal Excitation ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Pulsed Thermography ◽  
Processing Techniques ◽  
Non Destructive ◽  
Signal Processing Techniques

Non-destructive testing (NDT) is a broad group of testing and analysis techniques used in science and industry to evaluate the properties of a material, structure, or system for characteristic defects and discontinuities without causing damage. Recently, infrared thermography is one of the most promising technologies as it can inspect a large area quickly using a non-contact and non-destructive method. Moreover, thermography testing has proved to be a valuable approach for non-destructive testing and evaluation of structural stability of materials. Pulsed thermography is one of the active thermography technologies that utilizes external energy heating. However, due to the non-uniform heating, lateral heat diffusion, environmental noise, and limited parameters of the thermal imaging system, there are some difficulties in detecting and characterizing defects. In order to improve this limitation, various signal processing techniques have been developed through many previous studies. This review presents the latest advances and exhaustive summary of representative signal processing techniques used in pulsed thermography according to physical principles and thermal excitation sources. First, the basic concept of infrared thermography non-destructive testing is introduced. Next, the principle of conventional pulsed thermography and signal processing technologies for non-destructive testing are reviewed. Then, we review advances and recent advances in each signal processing. Finally, the latest research trends are reviewed.

Analysis of Pulsed Thermography Methods for Defect Depth Prediction

2005 ◽  
Vol 128 (4) ◽  
pp. 329-338 ◽  
Author(s):  
J. G. Sun
Keyword(s):  
Test Specimen ◽  
Three Dimensional ◽  
Quantitative Prediction ◽  
Defect Depth ◽  
Effective Technique ◽  
Flat Bottom ◽  
Dimensional Solution ◽  
Pulsed Thermography ◽  
One Dimensional ◽  
Depth Prediction

Pulsed thermography is an effective technique for quantitative prediction of defect depth within a specimen. Several methods have been reported in the literature. In this paper, using an analysis based on a theoretical one-dimensional solution of pulsed thermography, we analyzed four representative methods. We show that all of the methods are accurate and converge to the theoretical solution under ideal conditions. Three methods can be directly used to predict defect depth. However, because defect features that appear on the surface during a pulsed thermography test are always affected by three-dimensional heat conduction within the test specimen, the performance and accuracy of these methods differs for defects of various sizes and depths. This difference is demonstrated and evaluated from a set of pulsed thermography data obtained from a specimen with several flat-bottom holes as simulated defects.

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