Volume 90, Issue 10

The present study investigates experimentally the effects of a time-dependent and spatially inhomogeneous magnetic field on liquid metal droplet flow down an inclined substrate. The flow is solely excited by the electromagnetic interactions between the electrically conducting melt and the applied magnetic field. The metal droplet consists of the eutectic alloy GaInSn which is liquid at room temperature. The magnetic field is generated in the gap between two metallic disks that are equipped with a special geometric arrangement of permanent magnets and put into a measured rotation. During the experiments, a droplet of a measured volume is positioned on an electrically non-conducting substrate that is slightly inclined against the horizontal direction. Droplet and substrate are placed in between the two rotating magnetic disks. In our experiments, we record the electromagnetically excited flow of the droplet downwards onto the substrate using a high-speed camera system. Applying standard techniques of digital image processing, we measure both the displacement position and velocity of the droplet as a function of time. We observe that, depending on the rotation rate of the disks and angle of inclination, the magnetic field eventually triggers this spreading process. In more detail, by evaluating the recorded data, we find that the magnetic field excites capillary waves at the free surface of the droplet. These surface waves contribute to a redistribution of volume towards the contact line formed at the downward-facing end tip of the droplet. This mode of transport steepens the contact angle, allowing the droplet to move. Besides the fundamental aspect of this work, the present study may contribute to the electromagnetic control of both the production of metallic microfibers and metallurgic coating processes as well as to the non-contact electromagnetic flow measurement technique of Lorentz force velocimetry applied to liquid metal free-surface flows.

Condition monitoring of machines and powertrain components is an essential part of ensuring reliability and product safety in many industries. The monitored machines and components are often divided into different condition classes as well as classified using machine learning methods. In order to enable classification with machine learning algorithms, the acquisition of a sufficient amount of data from each condition class is essential. In reality, the collection of data for faulty system states turns out to be much more difficult, therefore in many use cases balanced data sets are not available. However, when classifying faulty states, an identical number of data per class is of great importance. This problem can be counteracted with synthetic data generation. Generative Adversarial Networks (GAN) are a suitable approach to generate synthetic data based on real measured data. In most cases of synthetic data generation, different damage cases, e.g. from a transmission, are simulated, but a generation of synthetic data is not performed at different operating conditions. However, different speeds and torques are a reality when monitoring, as the drive systems operate under changing operating conditions. Therefore, in the context of this paper, synthetic data generation at different operating states is investigated in order to implement a condition monitoring system for good and bad system conditions which includes different operating states. So, vibration data is acquired at different operating conditions of a transmission on a drive test rig and relevant features are highlighted using a suitable signal pre-processing method. The features, caused by different operating conditions, can also be generated synthetically by GAN. Therefore, it is possible to achieve a similar classification accuracy by integrating synthetically generated data as with real data, which makes the synthetic data generation a viable solution for extending existing data sets.

Zusammenfassung In der modernen Stahlproduktion sind automatische Oberflächeninspektionssysteme (OIS) zur Detektion und Klassifikation von Oberflächenfehlern weit verbreitet und ihre Ergebnisse haben stark an Bedeutung gewonnen. Trotzdem fehlt es bis heute an anerkannten Methoden für eine objektive und umfassende Leistungsbewertung der Systeme, um mit vertretbarem Aufwand geeignete Kenngrößen für die OIS-Klassifikationsleistung im realen Produktionsbetrieb zu ermitteln. Dieser Beitrag widmet sich der Problematik der Abschätzung des Recalls bei unbekannter „Grundwahrheit“ (ground truth), als zentrales Maß für die Fähigkeitsbewertung klassifizierender Bildverarbeitungssysteme (BV-Systeme). Es werden eine Methodik für die Recall-Schätzung mittels Hilfsklassifikator vorgestellt und Forschungsbedarfe für deren praktische Umsetzung erörtert.

Corrosion assessment of reinforced concrete structures is the basis for subsequent corrosion repair and early warning of structural hazards. Aiming at the problem of accurately measuring the corrosion degree of reinforced concrete structures in coastal area, a new Magnetic Sensor (NMS) based on the theory of magnetic medium is designed in this paper. A functional model of the relationship between magnetic induction intensity change and reinforcement mass loss based on multi-point detection is constructed, and an improved adaptive filtering Least Mean Square (LMS) algorithm is proposed to realize damage identification and quantitative calculation of reinforcement corrosion comprehensively. Through numerical simulation experiments, the feasibility of designing the sensor is verified. The measured corrosion test of the sensor prototype shows that the calculated corrosion rates of different steel corrosion degrees are in good agreement with the actual values, with an average relative error of 1.64 %. The sensor provides a new method for corrosion monitoring of reinforced concrete structures.

Ultrasound is a mechanical wave propagating in tissue which is influenced in its propagation behavior by the locally prevailing acousto-mechanical conditions. By suitable processing of the back-scattered signals received by the ultrasound transducer, tissue parameters such as local bulk modulus, mass density, speed of sound, isotropic scattering coefficient, and also the locally acting tissue absorption can be inferred. A discipline that has received increasing attention in the medical ultrasonic imaging discipline and its scientific publications in recent years is quantitative ultrasound (QUS) which tries to estimate with great accuracy these local acting tissue parameters. In this paper we analyze different algorithms for estimation of high spatial resolution tissue absorption parameters. On the one hand, there is a simple absorption estimator based on the evaluation of the quotient of the power density spectra calculated for different depth regions (spectral-log-difference estimator), which, however, assumes a linearly with frequency increasing absorption, this is contrasted with an estimator which also allows to estimate a polynomial increase of the absorption with frequency (method-of-moments estimator). Since a closed-form solution cannot be given for this, a maximum-likelihood estimator for which there is always an estimate that can be computed numerically efficiently is developed. The results, tissue attenuation, are presented as a color-coded overlay on conventional B-mode ultrasound images showing only morphology.

This paper presents a novel approach for accurately testing triangular waveform generators by leveraging the Ramp Vernier Test methodology commonly used for Analog-to-Digital Converters (ADCs). The proposed procedure enables efficient measurement of the nonlinearity of a triangular waveform using a low-cost data acquisition board. The key idea is to utilize the waveform generator under test to produce the stimulus signal for the data acquisition system’s testing. By analyzing the results of this test, the nonlinearity of the acquisition system can be determined and subsequently corrected. This approach effectively eliminates the influence of the acquisition system’s nonlinearity on the estimation of the nonlinearity of the waveform generator, ensuring accurate and reliable measurements.