621 Angewandte Physik
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Der technische Fortschritt hat dazu geführt, dass die Kosten für das automatisierte Drucken von Emblemen, Anhängern sowie allgemein gedruckten Prototypen stark gefallen sind. Dies geschah insbesondere auch vor dem Hintergrund eines Wandels der Industrie hin zu einer modernen Industrie 4.0. In dieser Arbeit wird ein neuer Ansatz für das automatische 3D-Drucken eines Logos präsentiert, welches als Pixelgrafik vorliegt. Die vorgeschlagene Prozesskette erzeugt hierbei final den G-code zum Drucken. Dabei sind seitens des Users wenige Eingaben erforderlich.
Fabry-Pèrot (FP) interferometer sensors enable highly sensitive backward mode acoustic detection in Photoacoustic (PA) imaging. They are transparent to the excitation wavelength, can be placed directly next to the PA source, and offer a broadband frequency response and high acoustic sensitivity. PA tomography using parallelized detection requires high spatial uniformity of the optical and acoustic properties, which can be hampered by contaminations during fabrication that lead to the formation of inhomogeneities and artefacts. The quality and homogeneity of the dielectric and polymer layers have a direct effect on the maximum optical phase sensitivity, and hence acoustic sensitivity. In this study, cross-sectional images of FP sensors were obtained using focused ion beam milling and ultramicrotomy followed by Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) to evaluate different fabrication methods.
Fabry-Perot (FP) sensors are typically read out using a raster scan to acquire tomographic Photoacoustic (PA) images. To speed up the recording time, wide-field illumination of the sensor in combination with a camera as detector can be used. In this study, an sCMOS camera and wavelengths around 517 nm are used to interrogate a FP sensor with a homogeneous optical thickness over a 4 cm2 aperture. The recorded time series show PA signals are acquired over the entire area of the interrogation beam. The performance of the system, such as the noise equivalent pressure, is evaluated.
Biomedical photoacoustics is usually used to image absorption-based contrast in soft tissues up to depths of several centimeters and with sub-millimeter resolution. By contrast, measuring Photoacoustic (PA) signals through hard bone tissue shows severe signal degradation due to aberration and high attenuation of high frequency acoustic signal components. This is particularly noticeable when measuring through thicker, human, skull bone. Which is the main reason why transcranial PA imaging in humans has so far proved challenging to implement. To tackle this challenge, we developed an optical resonator sensor based on a previous planar-concave design. This sensor was found to be highly suitable for measuring the low-pressure amplitude and low acoustic frequency signals that are transmitted through human cranial bone. A plano-concave optical resonator sensor was fabricated to provide high sensitivity in the acoustic frequency range of DC to around 2 MHz, a low noise equivalent pressure and a small active element size enabling it to significantly outperform conventional piezoelectric transducers when measuring PA waves transmitted through ex vivo human cranial bones.
Small and medium-sized companies increasingly turning their attention towards the fourth industrial revolution. In order to increase their own long-term competitiveness, there is a growing desire to make production smarter, more efficient, safer and more sustainable through new technologies. Often, however, existing plants cannot be easily replaced by modern equipment. The reasons for this can be high investment costs, excessive downtimes or the unavailability of an equivalent machine. An alternative solution to the purchase of new equipment is the modernisation or expansion of existing systems, also called retrofitting. Thus, this paper deals with the retrofit process of a machine tool, whereby the software architecture of the control unit is the primary concern of this work.
During the last decade production innovation was mainly focused on connectivity aspects. The vision of smart factories running on software, that uses collected machine data, has become true but foremost for leading industrial companies in highly developed countries. Apart from these, production can also be found in non-industrialized craft professions as well as in less developed countries. As digitalization does not necessarily require an industrial or developed setting the latter could possibly benefit from it as well. Socio-cyber-physical production systems have been used to describe the interdependencies of linked production systems but usually focus on highly developed regions as well as for industrial applications. This paper lines out similarities and differences for each case, introduces the concept of cyber-physical production systems (CPPS) and its extension to socio-CPPS (SCPPS), which emphasizes the role of human workers in the production environment. The relation between industrial, non-industrial production and innovations is examined. Furthermore, the widening of SCCPS concepts for non-industrial production is discussed.
Dermatological in vivo measurements are used for various purposes, e.g. health care, development and testing of skin care products or claim support in marketing. Especially for the last two purposes, in vivo measurements are extensive due to the quantity and repeatability of the measurement series. Furthermore, they are performed manually and therefore represent a nonnegligible time and cost factor. A solution to this is the implementation of collaborative robotics for the measurement execution. Due to various body shapes and surface conditions, common static control procedures are not applicable. To solve this problem, spatial information obtained from a stereoscopic camera can be integrated into the robot control process. However, the designated measurement area has to be detected and the spatial information processed. Therefore the authors propose a concept towards segmenting arm areas through a CNN-based object detector and their further processing to perform robot-based in vivo measurements. The paper gives an overview of the utilization of RGB-D images in 2D object detectors and describes the selection of a suitable model for the application. Furthermore the creation, annotation and augmentation of a custom dataset is presented.
Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.
Chiller systems are used in many different applications in both the industrial and the commercial sector. They are considered major energy consumers and thus contribute a non-negligible factor to environmental pollution as well as to the overall operating cost. In addition, chillers, especially in industrial applications, are often associated with high reliability requirements, as unplanned system downtimes are usually costly. As many studies over the past decades have shown, the presence of faults can lead to significant performance degradation and thus higher energy consumption of these systems. Thus, data-driven fault detection plays an ever-increasing role in terms of energy efficient control strategies. However, labelled data to train associated algorithms are often only available to a limited extent, which consequently inhibits the broad application of such technologies. Therefore, this paper presents an approach that exploits only a small amount of labelled and large amounts of unlabelled data in the training phase in order to detect fault related anomalies. For this, the model utilizes the residual space of the data transformed through principal component analyses in conjunction with a biased support vector machine, which can be ascribed to the concept of semi-supervised learning, or more specifically, positive-unlabelled learning.
To achieve climate goals and contain further global warming, it is inevitable to reduce CO2 emissions especially in energy consumption. A way to do so is by integrating renewable energy sources (RES) into an energy system’s power generation. However, there is no standard procedure to decarbonise a locally restricted system. Therefore, the various local conditions have to be analysed and taken into consideration.
The authors propose a model-based approach to decarbonise the energy system of the island Föhr, Germany. This includes various collected data sets on local conditions such as climate data and heat and power demand. The data is used to represent the island’s energy system and design a model-based solution in a simulation software.
The authors identify potentials by comparing costs and revenues by addressing the deployment of different RES technologies. One finding is that heat generation causes 91 % of CO2 emissions making it the major producer. However, with the designed solution, emissions could be reduced to a third.