Review of pulse phase thermography

Ibarra-Castanedo, Clemente; Maldague, Xavier

doi:10.1117/12.2181042

Cited by 18 publications

(14 citation statements)

References 9 publications

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“…The depth resolution of the phase is about 1.5-2μ, where μ is the thermal diffusion length. For PPT evaluations the term 'blind frequency' is often used [4,5,13], 'as a limiting frequency at which the defect presents enough phase contrast to be detected on the phase spectra' . It has been published [13] that the blind frequency and the defect depth are correlating by where C 1 has been called as a correlation constant, which is in the range of 1.5-2.…”

Section: Blind Frequencymentioning

confidence: 99%

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Time and frequency behaviour in TSR and PPT evaluation for flash thermography

Oswald-Tranta

2017

Quantitative InfraRed Thermography Journal

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Subsurface defects can be detected by flash thermography evaluating the temperature response at the surface. Many techniques have been developed in the past to localise defects and also to estimate their depth and size. Two of the most established methods are TSR and PPT, whereby TSR analyses the data in the time domain, and PPT evaluates the signal in the frequency domain. In order to get the data in the frequency domain, Fourier transformation, especially FFT is used to calculate the phase shift for the different frequencies. The usage of FFT assumes a periodic signal or a temporal signal limited in time. As this is not the case for the temperature signal after a short pulse heating, the transformation to the frequency domain generates some errors. Therefore parameters as e.g. sampling frequency or time duration of evaluation have to be selected carefully. Even if many publications have been already dealing with this topic, in this paper a new approach is attempted by comparing the temporal behaviour as it is handled by the TSR technique with the frequency behaviour calculated by PPT. The results are interpreted with the help of simulation and measurements for flat bottom hole samples are also presented.

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Section: Blind Frequencymentioning

confidence: 99%

“…Therefore parameters as e.g. sampling frequency or time duration have to be selected carefully [4][5][6]. In the next sections this paper deals with the consequences of using FFT instead of a Fourier transformation and how this affects the PPT evaluations.…”

Section: Introductionmentioning

confidence: 99%

Time and frequency behaviour in TSR and PPT evaluation for flash thermography

Oswald-Tranta

2017

Quantitative InfraRed Thermography Journal

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“…In order to identify the delamination defects in composite materials there exist many infrared thermographic techniques but a few of the techniques are frequently used. They are Pulse Thermography (PT), Lock in Thermography (LT) [2] and Pulse Phase Thermography [3,16] etc. But these methods do suffer from certain limitations.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Thermogram Based on Region Based Technique using Split and Merge Method

Bhushan,

Reddy,

Murali

2019

IJRTE

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The main aim of segmentation is to identify the Region of Interest for image analysis. The segregation of an image into meaningful structures is often an important phase in image analysis, object representation, visualization and also in various other image processing tasks. Image segmentation is mostly useful in applications like detection where it is difficult to process whole image at a time. In this paper Region based image segmentation is used to identify the delaminations in Thermographic image of Infrared Non-Destructive Testing. There are two basic techniques in Region based segmentation viz. Region growing method, splitting and merging method. New method based Split and Merge segmentation technique is employed to identify the defective regions in thermogram. Results obtained after segmentation as compared with state of art segmentation methods.

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“…In PPT, originally proposed by [ 21 ] and recently reviewed by [ 23 ], data are transformed from the time domain to the frequency spectra using the 1D DFT (Discrete Fourier transform):

where j is the imaginary number, n designates the frequency increment (

is the sampling increment, and

and

are the real and imaginary parts of the transform, respectively. In this case, real and imaginary parts of the complex transform are used to estimate the amplitude and the phase as described in [ 24 ]:

…”

Section: Methodsmentioning

confidence: 99%

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts

Fernandes

Zhang

Figueiredo

et al. 2018

Sensors

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The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

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Review of pulse phase thermography

Cited by 18 publications

References 9 publications

Time and frequency behaviour in TSR and PPT evaluation for flash thermography

Time and frequency behaviour in TSR and PPT evaluation for flash thermography

Segmentation of Thermogram Based on Region Based Technique using Split and Merge Method

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts

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