Abstracted/indexed

Deep learning has become a state-of-the-art method in machine learning, with a broad range of successful applications. Our goal is to explore the benefits of deep learning techniques for computational optical form measurements. The research is based on solving a nonlinear inverse problem aimed at the reconstruction of optical topographies from given processed interferograms. A U-Net network structure is chosen and tested on a simulated database. The obtained results are promising.

Symmetrical Pt | YSZ | Pt–NO gas sensors were produced with frit-containing and fritless Pt electrodes and fired between 950 and 1300 °C. The sensors were operated by pulsed polarization. With fritless pastes, the sensors responded significantly higher. The firing temperature affects the sensitivity only slightly. The low NO sensitivity of the frit-containing electrodes was attributed to a blocking effect at the triple-phase boundaries that inhibits the oxygen transport through the sensor.

We present a work on gas sensors that can for example be used for the assessment of indoor air quality. These sensors suffer from deterioration by siloxanes, so we investigated these effects by a distinct operation mode and exposition to this gas that allows us to interpret different reactions on the sensor surface. We found that all processes on the sensor surface are slowed down by this treatment and a self-compensation by the evaluation of oxygen adsorption processes is likely to be found.

The necessary reliability of wind turbine gearboxes increases the requirements for large gear measurements. However, standard measuring methods reach their limits for large gears with diameters > 1 m. Therefore a scalable optical gear measurement approach is presented. At first, simulation and experimental results prove the principle applicability of the measuring approach for small gear measurements. Geometric parameters of gears can be determined with a single-digit micrometer uncertainty.

In a very specific way, this research paper shows how established systems – in this case a commercial soot sensor for the automotive sector – can be optimized by diving deep into the basic research. The approach here is to link macroscopic observations or signal behavior with processes taking place on the atomic level. Taking these fundamental processes into account, the sensor's specific response time could be shortened effectively by a change in operating strategy – without any design changes.

In this paper we investigate an in-line concept for continuous monitoring of sodium, potassium, calcium and urea concentrations in blood serum using ion-selective electrodes. This concept is evaluated in a preclinical study with human packed red blood cells as a test medium. It has been shown that the electrolytes can be well monitored. In addition, we present the first measurements with ion-sensitive field-effect transistors in a miniaturized sensor assembly using a novel readout electronics.

This paper covers the design study of a multicomponent transducer (MCT) for wind turbine test benches. The MCT will cover the characteristics of wind turbines in the power range of up to 6 MW. The motivation to develop a MCT such as this is to provide satisfying measurement accuracy of loads and moments for all 6 degrees of freedom in order to reduce the uncertainty in the traceability of the drive train behavior due to the applied loads.

To improve the energy efficiency of industrial processes, such as continuous steel casting, lab-scale experiments with liquid metals are performed. We present an ultrasound Doppler signal process for multiplane, high-frame-rate flow imaging. We achieve real-time operations with an efficient implementation in a field-programmable gate array (FPGA). We analyze the contributions to the uncertainty of the velocity measurement and relate it to its fundamental limit.

This paper introduces a new method to drastically reduce the anisotropic strain sensitivity of granular thin film strain gauges. As a result, these improved strain gauges produce a much higher sensor signal when used for force transducers with biaxial strain fields. This gauge type is also more advantageous for uniaxial stress measurements. The method is based on the creation of a certain topographic structure of the strain gauges; in our case, this was realized by a picosecond laser system.

Photoelasticity is considered a useful measurement tool for non-destructive and contactless determination of mechanical stresses or strains in the production of silicon wafers. In this work, a detailed derivation for the anisotropic stress-optic law is presented, and the corresponding stress-optical parameters are measured.

A completely new evaluation method for optical sensors is investigated in this paper. It originates from research that integrates optical sensors into radio-over-fibre networks. The laboratory-based experimental set-up is explained, and the findings are validated by a comparison to the conventional evaluation scheme. Finally, the work provides a detailed mathematical model to understand the implications of this new read-out method for practical applications.

Distributed fiber optic strain measurement based on Rayleigh scattering enables the monitoring of strain along the entire fiber length and the possibility of integration into material matrices. In this article, optical fibers were integrated into the concrete matrix of small prisms to measure the deformation by drying. In comparison with a reference measuring method, the losses of strain transfer were measured. The analysis shows a high correlation between the reference method and the fiber.

European EMPIR project MicroProbes aims to use large-cale tactile microprobes for contact resonance applications, an established measurement mode of atomic force microscopes (AFMs). Because of the size of these probes, some of the simplifications made for AFM probes are not applicable. This study presents a guide on how to systematically create a model that replicates the dynamic behavior of microprobes. The finished model is then verified by analyzing a series of measurements.

This article presents a classification concept based on deep learning as an additional optical test method for real-time visualization and analysis of electrical assemblies in the production environment. For this purpose, a neural convolutional network is used to identify the quality of the solder joint of surface-mounted chip components in the inspection images. The concept can be used to increase the detection performance of the solder joint inspection systems.