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Impulse and lock-in thermography have been applied to detect delaminations of prototype solder joints, similar to those to be produced between Cu shunts and Cu busbar stabilisers at the Large Hadron Collider (LHC) at CERN. Two infrared cameras with different detector materials and with different spectral ranges and two excitation techniques have been tested and compared for their ability to detect delaminations behind 2 and 3 mm thick Cu shunts. We have analyzed the signal to noise ratio (SNR) for each detected defect and are able to detect defects down to a nominal edge length of 4 mm behind 2 mm thick Cu shunts by using fast impulse thermography and a camera with a microbolometer array. For the 3 mm thick Cu shunt, on the other hand, the nominal 4 mm defect is only visible in the lock-in thermography phase images and the highest SNR has been achieved with a cooled InSb-based camera. In addition, numerical simulations show the influence of the minimum detectable defect size on the shunt thickness and that the developed on-site testing technique is sufficient to find all defects that are detectable theoretically.
Catastrophic optical damage (COD) is analyzed during single current pulse excitation of 975 nm emitting diode lasers. Power transients and thermal images are monitored during each pulse. The COD process is unambiguously related to the occurrence of a thermal flash of Plancks radiation. We observe COD to ignite multiple times in subsequent pulses. Thermography allows for tracing a spatial motion of the COD site on the front facet of the devices. The time constant of power decay after the onset of COD has values from 400 to 2000 ns, i.e., an order of magnitude longer than observed for shorter-wavelength devices.
Single-pulse tests of the catastrophic optical damage (COD) are performed for three batches of diode lasers with different gain-regions. The tests involve in situ inspection of front, rear, and side of the devices by a thermocamera. Devices with an Al-containing gain-region show COD at the front facet, as expected for strong facet heating via surface recombination and reabsorption of laser light. In contrast, Al-free devices with low surface recombination rates tend to fail at the rear facet, pointing to a different heating scenario. The high carrier density at the rear facet favors heating and COD via Auger recombination processes.
Double-side cooled high-power diode laser bars packaged by different techniques on different types of passive heat sinks are analyzed in terms of packaging-induced strain. Reference data from standard devices being single-side cooled only and packaged by conventional soft and hard soldering are also presented. Thermal profiling across the devices complements the results. The most suitable packaging architecture and technique for double-side cooled bars is identified. Measurements of the laser emission near field and electroluminescence pattern provide direct reference to the functionality of the devices. Furthermore, a type of cross calibration of the methods used for strain analysis is made, since all techniques are applied to the same set of bars. This involves micro photoluminescence, micro Raman, and degree-of-polarization electroluminescence spectroscopy.
This article presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super-resolution (SR) imaging. The photothermal SR approach is a well-known technique to overcome the spatial resolution limitation in photothermal imaging by extracting high-frequency spatial components based on the deconvolution with the thermal point spread function (PSF). However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, handcrafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, this article proposes Photothermal-SR-Net, which performs deconvolution by deep unfolding considering the underlying physics. Since defects appear sparsely in materials, our approach includes trained block-sparsity thresholding in each convolutional layer. This enables to super-resolve 2-D thermal images for nondestructive testing (NDT) with a substantially improved convergence rate compared to classic approaches. The performance of the proposed approach is evaluated on various deep unfolding and thresholding approaches. Furthermore, we explored how to increase the reconstruction quality and the computational performance. Thereby, it was found that the computing time for creating high-resolution images could be significantly reduced without decreasing the reconstruction quality by using pixel binning as a preprocessing step.
In this work we introduce an algorithm based on the well-known Canny approach for effectual crack detection in thermographic films obtained using flying spot thermography (FST) or flying line thermography (FLT). The proposed algorithm performs faster than another algorithm, for crack detection, based on the application of two Sobel filters (one in x and another one in y directions). For FLT it is shown that processing 10-25 % of the thermograms of a thermographic film required to scan a whole sample is enough to obtain good results. In contrast, using the Sobel filter approach requires the processing of twice the thermographic film length. Experimental measurements are performed on a metallic component of complex shape which contains real defects, that is, surface breaking cracks due to industrial use. The specimen is tested using flying line thermography. Three different scanning speeds are tested: 10, 30 and 60 mms-1 with laser powers of 50, 60 and 120 W respectively. The sample and an infrared camera are aligned and fixed on a motorized linear stage. The diode laser LDM500 (500 W max power) is fixed on an optical bench separately from the linear stage. The results obtained
with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography based on the Sobel filter. It is shown that the proposed algorithm based on the Canny approach, can be used in automated systems for thermographic non-destructive testing.
Pulse-compression thermography is an emerging technique that has shown versatility by combination of pulsed and lock-in thermography. Accordingly, several aspects of this technique are still unexplored, and some others not fully developed yet. Barker codes were widely used in radar applications due to their simplicity and their optimum autocorrelation function. Nevertheless, applications were limited by the amplitude of the sidelobes present in the autocorrelation function and therefore, several filters have been developed which aim to reduce the sidelobes. However, the filters usually depend on empirical parameters which must be determined for each application. A better alternative would improve the applicability of the Barker codes. In this work, we further develop the pulse-compression thermography technique by introducing a 13-bit modified Barker code (mBC): This allows to drastically reduce the sidelobes characteristic of the 13-bit Barker code (BC). Consequently, the thermographic impulse response, obtained by cross-correlation, is almost free of such sidelobes. Deeper defects become easier to detect in comparison with using a 13-bit Barker code. Numerical simulations using the finite element method are used for comparison and experimental measurements are performed in a sample of steel grade St 37 with machined notches of three different depths: 2 mm, 4 mm and 6 mm.
We combine three different approaches to greatly enhance the defect reconstruction ability of active thermographic testing. As experimental approach, laser-based structured illumination is performed in a step-wise manner. As an intermediate signal processing step, the virtual wave concept is used in order to effectively convert the notoriously difficult to solve diffusion-based inverse problem into a somewhat milder wavebased inverse problem. As a final step, a compressed-sensing based optimization procedure is applied which efficiently solves the inverse problem by making advantage of the joint sparsity of multiple blind measurements. To evaluate our proposed processing technique, we investigate an additively manufactured stainless steel sample with eight internal defects. The concerted super resolution approach is compared to conventional thermographic reconstruction techniques and shows an at least four times better spatial resolution.
Learned block iterative shrinkage thresholding algorithm for photothermal super resolution imaging
(2022)
Block-sparse regularization is already well known in active thermal imaging and is used for multiple-measurement-based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. We show the benefits of using a learned block iterative shrinkage thresholding algorithm (LBISTA) that is able to learn the choice of regularization parameters, without the need to manually select them. In addition, LBISTA enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present LBISTA and compare it with state-of-the-art block iterative shrinkage thresholding using synthetically generated and experimental test data from active thermography for defect reconstruction. Our results show that the use of the learned block-sparse optimization approach provides smaller normalized mean square errors for a small fixed number of iterations. Thus, this allows us to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super-resolution imaging.
The coherent superposition of two anti-phased thermal wave fields creates a zone of destructive interference which is extremely sensitive to the presence of defects without any reference measurements.
Combining a high power laser with a spatial light modulator allows modulating phase and amplitude of an illuminated surface that induces spatially and temporally controlled thermal wave fields. The position and depth of defects are reconstructed from analysis of the Amplitude and Phase of the resulting photothermal signal. The proposed concept is experimentally validated and supported by numerical modeling.