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In this work, the limits of using spatially structured heating combined with subsequent photothermal super resolution reconstruction for the thermographic detection and resolution of internal defects are investigated. The approach is based on the application of modern high-power laser projector technology, which is used to repeatedly project varying spatially structured 2D pixel patterns to photothermally heat the object under test. After processing the generated thermographic data using nonlinear convex optimisation in conjunction with exploiting the joint-sparse nature of the defect signals within the individual measurements, a high-resolution 2D-sparse defect/inhomogeneity map is obtained. The main focus of the investigation is set on the influence of the number of individual measurements on the achievable reconstruction quality. Using numerical simulations based on an analytical representation of the forward solution to the underlying inverse problem, the convergence rate over performed measurements of the achievable reconstruction quality is determined. Finally, all findings are experimentally validated by reconstructing a set of internal defects in an additively manufactured sample. In this work, it is shown that for a variety of different defect separation distances, the projection of 50 different pixel patterns allows for a good trade-off between experimental complexity and reconstruction quality.
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 Anwendungsgemeinde. Für eine weitere Verbreitung insbesondere im industriellen Kontext spielen Normen, Standards und technische Regeln eine wichtige Rolle. In diesem Beitrag wird der aktuelle Stand der Normung in Deutschland vorgestellt. Wir zeigen, welche Normen und technischen Regeln 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.
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 Anwendungsgemeinde. Für eine weitere Verbreitung insbesondere im industriellen Kontext spielen Normen, Standards und technische Regeln eine wichtige Rolle. In diesem Beitrag wird der aktuelle Stand der Normung in Deutschland vorgestellt. Wir zeigen, welche Normen und technischen Regeln 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.
In this work, we present a novel approach to photothermal super resolution based thermographic resolution of internal defects using two-dimensional pixel pattern-based active photothermal laser heating in conjunction with subsequent numerical reconstruction to achieve a high-resolution reconstruction of internal defect structures. With the proposed adoption of pixelated patterns generated using laser coupled high-power DLP projector technology the complexity for achieving true two-dimensional super resolution can be dramatically reduced taking a crucial step forward towards widespread practical viability. Furthermore, based on the latest developments in high-power DLP projectors, we present their first application for structured pulsed thermographic inspection of macroscopic metal samples. In addition, a forward solution to the underlying inverse problem is proposed along with an appropriate heuristic to find the regularization parameters necessary for the numerical inversion in a laboratory setting. This allows the generation of synthetic measurement data, opening the door for the application of machine learning based methods for future improvements towards full automation of the method. Finally, the proposed method is experimentally validated and shown to outperform several established conventional thermographic testing techniques while conservatively improving the required measurement times by a factor of 8 compared to currently available photothermal super resolution techniques.
Infrared thermography using a focused (spot or line) beam has proved to be effective for detection of surface breaking cracks on planar samples. In this work, we use the same principle, but applied to complex shaped components, like a rail section, a gear, and a gas turbine blade. We use a six-axis robot arm to move the sample in front of our thermographic setup. Several scanning paths and thermographic parameters are explored: scanning speed, density of points in each scanning slice, laser power and camera frame-rate. Additionally, we explore semi-automatic evaluation algorithms for crack detection, as well as 2D-to-3D registration of the found indications.
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.
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.
Thermographic photothermal super resolution reconstruction enables the resolution of internal defects/inhomogeneities below the classical limit, which is governed by the diffusion properties of thermal wave propagation. Based on a combination of the application of special sampling strategies and a subsequent numerical optimization step in post-processing, thermographic super resolution has already proven to be superior to standard thermographic methods in the detection of one-dimensional defect/inhomogeneity structures. In our work, we report an extension of the capabilities of the method for efficient detection and resolution of defect cross sections with fully two-dimensional structured laser-based heating. The reconstruction is carried out using one of two different algorithms that are proposed within this work. Both algorithms utilize the combination of several coherent measurements using convex optimization and exploit the sparse nature of defects/inhomogeneities as is typical for most nondestructive testing scenarios. Finally, the performance of each algorithm is rated on reconstruction quality and algorithmic complexity. The presented experimental approach is based on repeated spatially structured heating by a high power laser. As a result, a two-dimensional sparse defect/inhomogeneity map can be obtained. In addition, the obtained results are compared with those of conventional thermographic inspection methods that make use of homogeneous illumination. Due to the sparse nature of the reconstructed defect/inhomogeneity map, this comparison is performed qualitatively.
In this work, we report on our progress for investigating a new experimental approach for thermographic detection of internal defects by performing 2D photothermal super resolution reconstruction. We use modern high-power laser projector technology to repeatedly excite the sample surface photothermally with varying spatially structured 2D pixel patterns. In the subsequent (blind) numerical reconstruction, multiple measurements are combined by exploiting the joint-sparse nature of the defects within the specimen using nonlinear convex optimization methods. As a result, a 2D-sparse defect/inhomogeneity map can be obtained. Using such spatially structured heating combined with compressed sensing and computational imaging methods allows to significantly reduce the experimental complexity and to study larger test surfaces as compared to the one-dimensional approach reported earlier.