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Für die optische Erwärmung von Prüfteiloberflächen sind Blitz- und Halogenlampen Stand der Technik. Der Vorteil dieser Wärmequellen liegt in ihren relativ geringen Anschaffungskosten bei gleichzeitig hohen Leistungsdichten am Prüfobjekt. Nachteilig ist bei den Halogenlampen die Limitierung der möglichen Modulationsffequenz auf ca. 1 Hz für die Anwendung dieser Quelle in der Lockin-Thermografie. Blitzlampen bieten für gewöhnlich nur eine feste Blitzdauer bei einer geringen Taktrate. Beide Anregungsquellen müssen in Reflexionsanordnung mit Filtern betrieben werden um die entstehende thermische Eigenstrahlung als Störquelle zu eliminieren. Wir stellen Untersuchungen an Hochleistungs-LEDs für den Einsatz in der aktiven Thermografie vor. Diese Lichtquellen werden durch die rasante Entwicklung der möglichen Leistungsdichten nun auch für die thermografische ZfP interessant. Sie zeigen ähnlich dem Laser eine schmale und auf den Werkstoff abstimmbare spektrale Verteilung der Strahlungsleistung bei moderaten Kosten. Die zugrunde liegende Elektrolumineszenz generiert kaum Wärmestrahlung und ermöglicht dadurch die Prüfung während des Erwärmungsvorganges ohne größeren Aufwand. Modulationsfrequenzen über 100 Hz mit thermischen Eindringtiefen < 1 mm sind möglich. Dadurch qualifizieren sich die LEDs zur Anwendung in der Lockin-Thermografie von Metallen und zur oberflächennahen Prüfung von Schichtsystemen. Durch die fast verzögerungsffeie Reaktion der LED auf das steuernde Signal sind nahezu beliebige zeitliche Anregungsformen möglich. Mit solch flexiblen Anregungsformen ließen sich bereits bei den photothermischen Verfahren erfolgreich höhere Tiefenreichweiten und -auflösungen gegenüber den klassischen Methoden der monofrequenten Lockin - und energiereichen Kurzimpuls-Anregung erzielen. Untersuchungen dieser Möglichkeiten im praktischen Einsatz der HochleistungsLEDs werden vorgestellt und mit herkömmlichen Lichtquellen verglichen.
Die Thermografie ist trotz ihrer ausgereiften wissenschaftlichen und technologischen Grundlagen ein noch relativ junges Mitglied in der Familie der zerstörungsfreien Prüfverfahren. Sie erschließt sich aufgrund einer Reihe von Vorzügen eine wachsende Anwenderschaft. Für eine weitere Verbreitung insbesondere im industriellen Kontext spielen Normen, Standards und Richtlinien eine wichtige Rolle. In diesem Beitrag wird der aktuelle Stand der Normierung vorgestellt. Wir werden zeigen, welche Grundlagennormen und Anwendungsnormen es für die Thermografie in Deutschland und international gibt und wir wagen einen Blick in die Zukunft. Darüber hinaus lebt auch die Normierungsarbeit von der Beteiligung durch interessierte Kreise. Dies können industrielle und akademische Anwender*innen, Hersteller*innen von Geräten, Forschungseinrichtungen oder Dienstleistungsunternehmen sein. Sie können gern Ihre Bedarfe bezüglich Normierungsprojekten mitbringen und/oder direkt an die Autoren senden.
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.
Detection of surface breaking cracks using flying line laser thermography: A Canny-based algorithm
(2021)
In this work, we introduce a new algorithm for effectual crack detection using flying line laser thermography, based on the well-known Canny approach. The algorithm transforms the input thermographic sequence into an edge map. Experimental measurements are performed on a metallic component that contains surface breaking cracks due to industrial use. The specimen is tested using flying line thermography at different scanning speeds and laser input powers. Results obtained with the proposed algorithm are additionally compared with a previously established algorithm for flying spot thermography. The proposed Canny-based algorithm 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.
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
(2020)
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. To avoid time-consuming manually selected regularization parameters, we propose a learned block-sparse optimization approach using an iterative algorithm unfolded into a deep neural network. More precisely, we show the benefits of using a learned block iterative shrinkage thresholding algorithm that is able to learn the choice of regularization parameters. In addition, this algorithm enables the determination of a suitable weight matrix to solve the underlying inverse problem. Therefore, in this paper we present the algorithm and compare it with state of the art block iterative shrinkage thresholding using synthetically generated test data 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 than without learning. Thus, this new approach allows to improve the convergence speed and only needs a few iterations to generate accurate defect reconstruction in photothermal super resolution imaging.
This paper presents deep unfolding neural networks to handle inverse problems in photothermal radiometry enabling super resolution (SR) imaging. Photothermal imaging is a well-known technique in active thermography for nondestructive inspection of defects in materials such as metals or composites. A grand challenge of active thermography is to overcome the spatial resolution limitation imposed by heat diffusion in order to accurately resolve each defect. The photothermal SR approach enables to extract high-frequency spatial components based on the deconvolution with the thermal point spread function. However, stable deconvolution can only be achieved by using the sparse structure of defect patterns, which often requires tedious, hand-crafted tuning of hyperparameters and results in computationally intensive algorithms. On this account, Photothermal-SR-Net is proposed in this paper, which performs deconvolution by deep unfolding considering the underlying physics. This enables to super resolve 2D thermal images for nondestructive testing with a substantially improved convergence rate. Since defects appear sparsely in materials, Photothermal-SR-Net applies trained blocksparsity thresholding to the acquired thermal images in each convolutional layer. The performance of the proposed approach is evaluated and discussed using various deep unfolding and thresholding approaches applied to 2D thermal images. Subsequently, studies are conducted on how to increase the reconstruction quality and the computational performance of Photothermal-SR-Net is evaluated.
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.