The spatial resolution limit in photoacoustic/thermal imaging is derived from the irreversibility of attenuation of the pressure wave and of heat diffusion during propagation of the signals from the imaged subsurface structures to the sample surface, respectively. The acoustic or temperature signals are converted into so-called virtual waves, which are their reversible counterparts, and which can be used for image reconstruction by well-known ultrasound reconstruction methods, which is an ill-posed inverse problem. The resolution from entropy production is equal to the diffraction limit -which is noise limited. Incorporating sparsity and non-negativity in iterative regularization methods gives a significant resolution enhancement.
In the presented work the principle of super-resolution imaging using structured illumination is applied to thermography. Scattered light which can penetrate under the surface of a sample heats subsurface light absorbing structures. The diffused heat is measured on the sample surface e.g. with an infrared camera, which allows to image the subsurface structures. The deeper these structures are below the surface, the more blurred their images become.
Blind structured illumination inside the sample is used to calculate a super-resolution image which less blurring. The structured illumination inside the sample can be generated by interference of scattered light producing laser speckles.
Photothermal measurements with an infrared camera enable a fast and contactless part inspection. The main drawback of existing reconstruction methods is the degradation of the spatial resolution with increasing imaging depth, which results in blurred images for deeper lying structures. In this work, we propose an efficient image reconstruction strategy that allows prior information to be included to overcome the diffusion-based information loss. Following the virtual wave concept, in a first step we reconstruct from the measured photothermal signal an acoustic wave field that satisfies the standard wave equation. This wave is called a virtual one, because it is not the measured acoustic wave but mathematically calculated from the temperature signal measured on the sample surface. In the second step, stable and efficient reconstruction methods developed for photoacoustic tomography are used. We compensate for the loss of information in thermal measurements by incorporating the prior information positivity and sparsity. For that purpose we combine circular projections with an iterative regularization scheme. Using experimental data, this work demonstrates that the quality of the reconstruction based on photothermal measurements can be significantly enhanced. The main goal of this work was to illustrate that prior information significantly improves the regularized solution and, hence, the reconstructed field. Using an iterative non-linear regularization method, the prior information positivity and sparsity could be incorporated. The regularization and reconstruction results show that respecting information available about the data significantly increases the quality of the regularized solution.
KEYWORDS: Diffusion, Thermography, Signal to noise ratio, Aerospace engineering, Infrared cameras, Ultrasonics, Manufacturing, Testing and analysis, Time metrology, Wave propagation
High strength and light weight, justify the frequent use of carbon fibre reinforced plastics for aeronautical applications. The manufacturing process of such material systems is a multi-stage process and susceptible to the formation of air-filled voids. This porosity weakens the epoxy matrix and causes noticeable degradation of mechanical properties. Active thermography with optical-excitation is an advantageous photothermal method because due to the infrared camera it is a non-contacting, fast testing method for the estimation of material properties or for defect detection. We use the Virtual Wave Concept, which allows ultrasonic testing methods for photothermal measurement data. Based on this ability, we apply the through-transmission method to determine the Time-of-Flight of virtual waves, which is directly related to the porosity dependent diffusion time. A signalto-noise ratio dependent approach is used for the temporal truncation of measurement data to get the optimum evaluation time. This ensures to evaluate only time-ranges which contain information of the heat diffusion inside the sample. In addition, undesired effects of heat losses due to convection and radiation are reduced. After the evaluation procedure is shown for simulated data, we demonstrate the experimental pixel-wise estimation of the porosity affected thermal diffusion times on a real aerospace part in transmission configuration. The results are validated by X-ray computed tomography reference measurements, where a good match can be achieved with active thermography results.
In active thermography, the structure below the surface can be reconstructed from measured surface temperature signals. The main drawback in active thermographic is the degradation of the spatial resolution with imaging depth. Recently, we used a mathematical compensation method to transform each measured surface temperature signal into a virtual acoustic wave, which is the solution of the wave equation and therefore ultrasound image reconstruction methods can be used. This allows a 3D thermo-tomography, which combines the advantages of thermographic and ultrasonic imaging, but the degradation of spatial resolution for deeper lying structures is still significant. A possibility to overcome this degradation is to incorporate prior information such as positivity and sparsity in the reconstruction process. Based on pulsed thermography data we show that the thermographic detection limit is extendible by a factor of four.
KEYWORDS: Acoustics, Signal attenuation, Temperature metrology, Wave propagation, Signal to noise ratio, Photoacoustic spectroscopy, Diffusion, Sensors, Photoacoustic imaging, Absorption
The resolution in photoacoustic imaging is limited by the acoustic bandwidth and therefore by acoustic attenuation,
which can be substantial for high frequencies. This effect is usually ignored for photoacoustic reconstruction but has a
strong influence on the resolution of small structures. The amount of information about the interior of samples, which
can be gained in general by the detection of optical, thermal, or acoustical waves on the sample surface, is essentially
influenced by the propagation from its excitation to the surface. Scattering, attenuation, and thermal diffusion cause an
entropy production which results in a loss of information of propagating waves.
Using a model based time reversal method, it was possible to partly compensate acoustic attenuation in photoacoustic
imaging. To examine this loss of information in more detail, we have restricted us to "thermal waves" in one dimension,
which can be realized experimentally by planar layers. Simulations using various boundary conditions and experimental
results are compared. Reconstruction of the initial temperature profile from measurement data is performed by various
regularization methods, the influence of the measurement noise (fluctuations) on the information loss during
reconstruction is shown to be equal to the entropy production during wave propagation.
Active Thermography has become a powerful tool in the field of non-destructive testing (NDT) in recent years. This
infrared thermal imaging technique is used for non-contact inspection of materials and components by visualizing
thermal surface contrasts after a thermal excitation. The imaging modality combined with the possibility of detecting and
characterizing flaws as well as determining material properties makes Active Thermography a fast and robust testing
method even in industrial-/production environments. Nevertheless, depending on the kind of defect (thermal properties,
size, depth) and sample material (CFRP carbon fiber reinforced plastics, metal, glass fiber) or sample structure
(honeycomb, composite layers, foam), active thermography can sometimes produce equivocal results or completely fails
in certain test situations. The aim of this paper is to present examples of results of Active Thermography methods
conducted on aircraft components compared to various other (imaging) NDT techniques, namely digital shearography,
industrial x-ray imaging and 3D-computed tomography. In particular we focus on detection limits of thermal methods
compared to the above-mentioned NDT methods with regard to: porosity characterization in CFRP, detection of
delamination, detection of inclusions and characterization of glass fiber distributions.
Active infrared thermography is a method for non-destructive testing (NDT) of materials and components. In pulsed
thermography (PT), a brief and high intensity flash is used to heat the sample. The decay of the sample surface
temperature is detected and recorded by an infrared camera. Any subsurface anomaly (e.g. inclusion, delamination, etc.)
gives rise to a local temperature increase (thermal contrast) on the sample surface. Conventionally, in Pulsed Phase
Thermography (PPT) the analysis of PT time series is done by means of Discrete Fourier Transform producing phase
images which can suppress unwanted physical effects (due to surface emissivity variations or non-uniform heating). The
drawback of the Fourier-based approach is the loss of temporal information, making quantitative inversion procedures
tricky (e.g. defect depth measurements). In this paper the complex Morlet-Wavelet transform is used to preserve the time
information of the signal and thus provides information about the depth of a subsurface defect. Additionally, we propose
to use the according phase contrast value to derive supplementary information about the thermal reflection properties at
the defect interface. This provides additional information (e.g. about the thermal mismatch factor between the specimen
and the defect) making interpretation of PPT results easier and perhaps unequivocal.
Active infrared thermography is a non-destructive testing (NDT) technique used for non-contact inspection of
components and materials by temporal mapping of thermal patterns by means of infrared imaging. Through the
application of a short heat pulse, thermal waves of various amplitudes and frequencies are launched into the specimen
allowing a signal analysis based on amplitude and phase information (pulsed phase thermography PPT). The wavelet
transform (with complex wavelets) can be used with PPT data in a similar way as the classical Fourier transform
however with the advantage of preserving time information of the signal which can then be correlated to defect depth,
and in this way allowing a quantitative evaluation. In this paper we review the methodology of PPT and the associated
signal analysis (Fourier analysis, wavelet analysis) to obtain quantitative defect depth information. We compare and
discuss the results of thermal FEM simulations with experimental data and show the advantages of wavelet based signal
analysis for defect depth measurements and material characterization.
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