In this paper, we report on the application of the ultrasonic Total Focusing Method for quantitative imaging of cracks in austenitic welds. Imaging inspection procedures provide more information than punctual measurements. In addition, an image provides more easy access for most human viewers. In ultrasonic testing, array transducers have played an important role in imaging for many years. The Full Matrix Capture technique in combination with Total Focusing is one of the newer methods with decisive advantages. In combination with fast electronics, computer technology and image processing, it now enables tomographic imaging in real time. The functionality of the method and the technical requirements are explained here. Austenitic weld seams are considered as examples for anisotropic materials, which can be examined with this method. An important requirement for quantitative measurement of defect size in anisotropic materials is the knowledge of the ultrasonic phase and group velocities of the inspected volume. The Gradient Elastic Constant Descent Method can deliver unknown parameters, which are needed to calculate these velocities.
An analytical solution for the determination of either angle of incidence (AOI) and the refractive index from combined ellipsometric and reflectometric measurements at dielectric substrates is presented. The solution is of special importance for retroreflex ellipsometry (but not limited to this application). Overcoming the geometric restrictions of conventional ellipsometers, the patented retroreflex ellipsometry can detect changes of intensity and the state of polarization in or at test objects even with curved surfaces. In contrast to conventional ellipsometers where the AOI is set by the adjustment procedure, the AOI is usually unknown in retroreflex ellipsometry. For quantitative analysis, the knowledge of the AOI is nevertheless essential. The proposed combination of retroreflex-reflectometry and retroreflex-ellipsometry opens the path to precise measurements of either surface geometry and index of refraction of nonplanar dielectric substrates (e. g. surfaces of freeform optics).
Heat flow thermography is a non-destructive testing method that offers a number of advantages. These include the relatively quick inspection of larger areas, the ease of interpretation of the results and the absence of potential hazards such as ionizing radiation. The disadvantage is, depending on the application, its limited penetration depth. The present article explains the physical principles of the process and provides examples of concrete realizations, mainly in the field of composites and natural materials. One focus is on the evaluation of the acquired thermal images and image series, since they often contain more information than is visible at first glance, and a suitable post-processing of the data is the key to a successful application.
Zahlreiche Fragestellungen bei Design, Errichtung und Betrieb von Windenergieanlagen erfordern Vibrationsmessungen, insbesondere auch an Rotorblättern unter Betriebsbedingungen. Verbaute Sensoren können diese Aufgabe nur punktuell, extern angebrachte nur mit großem Aufwand und eventueller Beeinflussung des Messobjektes erfüllen. Als anlagenunabhängiges, distantes Messverfahren soll hier eine Kombination aus Laser-Doppler-Vibrometer mit einem Trackingverfahren für die Verfolgung der Rotordrehung vorgestellt werden. Es erfordert eine Trennung der Effekte, die durch die makroskopische Bewegung des Rotors auftreten, von den zu messenden Vibrationsbewegungen. Neben dem Doppler-Effekt der schnellen Rotorbewegung werden dazu das Signalrauschen der zurückgestreuten kohärenten Laserstrahlung, sowie Bewegungsartefakte aufgrund des schrägen Blickwinkels erörtert. Erste Testmessungen konnten trotz dieser Herausforderungen die Anwendbarkeit der Methode demonstrieren.
Micromagnetic materials characterization is a nondestructive means of predicting mechanical properties and stress of steel and iron products. The method is based on the circumstance that both mechanical and magnetic behaviour relate to microstructure over similar interaction mechanisms, which leads to characteristic correlations between mechanical and magnetic properties of ferromagnetic materials. The prediction of mechanical properties or stress from micromagnetic parameters represents an inverse problem commonly addressed by regression and classification approaches. Challenges for the industrial application of micromagnetic methods lie in the development of robust sensors, definition of significant features, and implementation of powerful machine learning algorithms for a reliable quantitative target value prediction by processing of the micromagnetic features. This contribution briefly explains the background of micromagnetics, describes the typical challenges experienced in practice and provides insight into latest progress in the application of machine learning to micromagnetic data.