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Dieser Beitrag stellt die Möglichkeiten des 3D-Druckes unter der Berücksichtigung von Mensch-Roboter-Kollaborations-Anforderungen dar. Dabei werden die Vorteile mit besonderem Fokus auf die zusätzliche Gestaltungsfreiheit erläutert. Anhand von Beispielen wird der Stand der Technik bereits eingesetzter Sensorik sowie deren Notwendigkeit in Greifsystemen erläutert. Im weiteren Verlauf dieses Beitrags werden allgemeine Verfahren für die additive Verarbeitung von leitfähigen Materialien vorgestellt. Daran angeknüpft sind Beispiele speziell zur 3D-gedruckten Sensorik. Abgerundet wird der Beitrag mit einem Ausblick bezüglich 3D-gedruckter Sensorik in MRK-Greifsystemen.
This paper describes the use of the single-linkage hierarchical clustering method in outlier detection for manufactured metal work pieces. The main goal of the study is to group defects that occur 5 mm into a work piece from the edge, i.e., the border of the metal work piece. The goal is to remove defects outside the area of interest as outliers. According to the assumptions made for the performance criteria, the single-linkage method has achieved better results compared to other agglomeration methods.
Uncontrollable manufacturing variations in electrical hardware circuits can be exploited as Physical Unclonable Functions (PUFs). Herein, we present a Printed Electronics (PE)-based PUF system architecture. Our proposed Differential Circuit PUF (DiffC-PUF) is a hybrid system, combining silicon-based and PE-based electronic circuits. The novel approach of the DiffC-PUF architecture is to provide a specially designed real hardware system architecture, that enables the automatic readout of interchangeable printed DiffC-PUF core circuits. The silicon-based addressing and evaluation circuit supplies and controls the printed PUF core and ensures seamless integration into silicon-based smart systems. Major objectives of our work are interconnected applications for the Internet of Things (IoT).
The increase in households with grid connected Photovoltaic (PV) battery system poses challenge for the grid due to high PV feed-in as a result of mismatch in energy production and load demand. The purpose of this paper is to show how a Model Predictive Control (MPC) strategy could be applied to an existing grid connected household with PV battery system such that the use of battery is maximized and at the same time peaks in PV energy and load demand are reduced. The benefits of this strategy are to allow increase in PV hosting capacity and load hosting capacity of the grid without the need for external signals from the grid operator. The paper includes the optimal control problem formulation to achieve the peak shaving goals along with the experiment set up and preliminary experiment results. The goals of the experiment were to verify the hardware and software interface to implement the MPC and as well to verify the ability of the MPC to deal with the weather forecast deviation. A prediction correction has also been introduced for a short time horizon of one hour within this MPC strategy to estimate the PV output power behavior.
In rural low voltage grid networks, the use of battery in the households with a grid connected Photovoltaic (PV) system is a popular solution to shave the peak PV feed-in to the grid. For a single electricity price scenario, the existing forecast based control approaches together with a decision based control layer uses weather and load forecast data for the on–off schedule of the battery operation. These approaches do bring cost benefit from the battery usage. In this paper, the focus is to develop a Model Predictive Control (MPC) to maximize the use of the battery and shave the peaks in the PV feed-in and the load demand. The solution of the MPC allows to keep the PV feed-in and the grid consumption profile as low and as smooth as possible. The paper presents the mathematical formulation of the optimal control problem along with the cost benefit analysis . The MPC implementation scheme in the laboratory and experiment results have also been presented. The results show that the MPC is able to track the deviation in the weather forecast and operate the battery by solving the optimal control problem to handle this deviation.
A Nonlinear FEM Model to Calculate Third-Order Harmonic and Intermodulation in TC-SAW Devices
(2018)
Nonlinearities in Temperature Compensated SAW (TC-SAW) devices in the 2 GHz range are investigated using a nonlinear finite element model by simultaneously considering both third-order intermodulation distortion (IMD3)and third harmonic (H3). In the employed perturbation approach, different contributions to the total H3, the direct and indirect contribution, are discussed. H3 and IMD3 measurements were fitted simultaneously using scaling factors for SiO 2 film and Cu electrode nonlinear material tensors in TC-SAW devices. We employ a P-Matrix simulation as intermediate step: Firstly, measurement and nonlinear P-Matrix calculations for finite devices are compared and coefficients of the P-Matrix simulation are determined. The nonlinear tensor data of the different materials involved in periodic nonlinear finite element method (FEM) computations are optimized to fit periodic P-Matrix calculations by introducing scaling factors. Thus, the contribution of different materials to the nonlinear behavior of TC-SAW devices is obtained and the role of materials is discussed.
A printed electronics technology has the advantage of additive and extremely low-cost fabrication compared with the conventional silicon technology. Specifically, printed electrolyte-gated field-effect transistors (EGFETs) are attractive for low-cost applications in the Internet-of-Things domain as they can operate at low supply voltages. In this paper, we propose an empirical dc model for EGFETs, which can describe the behavior of the EGFETs smoothly and accurately over all regimes. The proposed model, built by extending the Enz-Krummenacher-Vittoz model, can also be used to model process variations, which was not possible previously due to fixed parameters for near threshold regime. It offers a single model for all the operating regions of the transistors with only one equation for the drain current. Additionally, it models the transistors with a less number of parameters but higher accuracy compared with existing techniques. Measurement results from several fabricated EGFETs confirm that the proposed model can predict the I-V more accurately compared with the state-of-the-art models in all operating regions. Additionally, the measurements on the frequency of a fabricated ring oscillator are only 4.7% different from the simulation results based on the proposed model using values for the switching capacitances extracted from measurement data, which shows more than 2× improvement compared with the state-of-the-art model.
In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.
Hot work tools are subjected to complex thermal and mechanical loads during hot forming processes. Locally, the stresses can exceed the material’s yield strength in highly loaded areas as e.g. in small radii in die cavities. To sustain the high loads, the hot forming tools are typically made of martensitic hot work steels. While temperatures for annealing of the tool steels usually lie in the range between 400 and 600 °C, the steels may experience even higher temperatures during hot forming, resulting in softening of the material due to coarsening of strengthening particles. In this paper, a temperature dependent cyclic plasticity model for the martensitic hot work tool steel 1.2367 (X38CrMoV5-3) is presented that includes softening due to particle coarsening and that can be applied in finite-element calculations to assess the effect of softening on the thermomechanical fatigue life of hot work tools. To this end, a kinetic model for the evolution of the mean size of secondary carbides based on Ostwald ripening is coupled with a cyclic plasticity model with kinematic hardening. Mechanism-based relations are developed to describe the dependency of the mechanical properties on carbide size and temperature. The material properties of the mechanical and kinetic model are determined on the basis of tempering hardness curves as well as monotonic and cyclic tests.
A Validated Quantification of Sudan Red Dyes in Spicery using TLC and a 16-bit Flatbed Scanner
(2018)
We present a video-densitometric quantification method for Sudan red dyes in spices and spice mixtures, separated by TLC. Application was done band-wise in small dots using a 5 μL glass pipette. For separation, the RP-18 plates (20 × 20 cm with fluorescent dye; Merck, Germany, 1.05559) were developed in a vertical developing chamber without vapor saturation from the starting point to a distance of 70 mm by using acetonitrile, methanol, and aqueous ammonia solution (25%; 8 + 1.8 + 0.2, v/v) as mobile phase. The quantification is based on direct measurements using an inexpensive 16-bit flatbed scanner for color measurements (in red, green, and blue). Evaluation of only the green channel makes the measurements very specific. For linearization, an extended Kubelka-Munk expression for data transformation was used. The range of linearity covers more than two magnitudes and lies between 20 and 500 ng. The extraction from a 2 g sample with acetonitrile, evaporation, and reconstitution to 200 μL with methanol and the band-wise application (7 mm) of a 10 μL sample allows a statistically defined LOD of less than 500 ppb of Sudan red dyes. To perform the analysis, a separation chamber, RP-18 plates, 5 μL glass pipettes, and a 16-bit flatbed scanner for 105 € are needed; therefore, the separation method is inexpensive, fast, and reliable.
Membrane distillation (MD) is a thermal separation process which possesses a hydrophobic, microporous
membrane as vapor space. A high potential application for MD is the concentration of hypersaline brines, such as
e.g. reverse osmosis retentate or other saline effluents to be concentrated to a near saturation level with a Zero
Liquid Discharge process chain. In order to further commercialize MD for these target applications, adapted MD
module designs are required along with strategies for the mitigation of membrane wetting phenomena. This
work presents the experimental results of pilot operation with an adapted Air Gap Membrane Distillation
(AGMD) module for hypersaline brine concentration within a range of 0–240 g NaCl /kg solution. Key performance
indicators such as flux, GOR and thermal efficiency are analyzed. A new strategy for wetting mitigation
by active draining of the air gap channel by low pressure air blowing is tested and analyzed. Only small reductions
in flux and GOR of 1.2% and 4.1% respectively, are caused by air sparging into the air gap channel.
Wetting phenomena are significantly reduced by avoiding stagnant distillate in the air gap making the air blower
a seemingly worth- while additional system component.
This paper deals with the detection and segmentation of clouds on high-dynamic-range (HDR) images of the sky as well as the calculation of the position of the sun at any time of the year. In order to predict the movement of clouds and the radiation of the sun for a short period of time, the clouds thickness and position have to be known as precisely as possible. Consequently, the segmentation algorithm has to provide satisfactory results regardless of different weather, illumination and climatic conditions. The principle of the segmentation is based on the classification of each pixel as a cloud or as a sky. This classification is usually based on threshold methods, since these are relatively fast to implement and show a low computational burden. In order to predict if and when the sun will be covered by clouds, the position of the sun on the images has to be determined. For this purpose, the zenith and azimuth angles of the sun are determined and converted into XY coordinates.
Aspekte der Motivation
(2018)
Autonome Systeme im Consumerbereich - Was bedeutet die Autonomie technischer Systeme für den Kunden
(2018)
Dieser Artikel gibt einen Überblick der Möglichkeiten kontextbewusster Systeme und erläutert, wie diese die Autonomie zugleich erweitern und begrenzen können. Anwendungsbeispiele wie autonomes Fahren, Rehabilitation, industrielle Arbeit und Robotik zeigen die technischen Möglichkeiten auf. Neben der Erkennung von räumlichen Details werden auch die Potenziale der Erkennung von Emotionen beschrieben. Dabei wird zugunsten der Allgemeinverständlichkeit auf eine tiefe technische Detaillierung verzichtet, zugleich aber auf die jeweils relevante Forschungsliteratur verweisen.
Benchmarking
(2018)
BGH "Mehrere Werbekanäle"
(2018)
BGH "Preisportal"
(2018)
Big Data Governance
(2018)
Bildung statt Profilbildung
(2018)
Im Rahmen der Konstruktionsausbildung an der Hochschule Offenburg wird die Lehre im Fach Technische Dokumentation fortlaufend optimiert. In der vorliegenden Laborstudie wurde das visuelle Wahrnehmen von 34 Maschinenbaustudierenden (2w + 32m) im Alter von 19 bis 29 Jahren mithilfe der Eye-Tracking-Technik und einer Videokamera bei der Analyse einer Baugruppenzeichnung beobachtet.
Brand identification has the potential of shaping individuals' attitudes, performance and commitment within learning and work contexts. We explore these effects, by incorporating elements of branded identification within gamified environments. We report a study with 44 employees, in which task performance and emotional outcomes are assessed in a real-world assembly scenario - namely, while performing a soldering task. Our results indicate that brand identification has a direct impact on individuals' attitude towards the task at hand: while instigating positive emotions, aversion and reactance also arise.
Business Reengineering
(2018)
Covert- and side-channels as well as techniques to establish them in cloud computing are in focus of research for quite some time. However, not many concrete mitigation methods have been developed and even less have been adapted and concretely implemented by cloud providers. Thus, we recently conceptually proposed C 3 -Sched a CPU scheduling based approach to mitigate L2 cache covert-channels. Instead of flushing the cache on every context switch, we schedule trusted virtual machines to create noise which prevents potential covert-channels. Additionally, our approach aims on preserving performance by utilizing existing instead of artificial workload while reducing covert-channel related cache flushes to cases where not enough noise has been achieved. In this work we evaluate cache covert-channel mitigation and performance impact of our integration of C 3 -Sched in the XEN credit scheduler. Moreover, we compare it to naive solutions and more competitive approaches.
Cell lifetime diagnostics and system be-havior of stationary LFP/graphite lithium-ion batteries
(2018)
Real-Time Ethernet has become the major communication technology for modern automation and industrial control systems. On the one hand, this trend increases the need for an automation-friendly security solution, as such networks can no longer be considered sufficiently isolated. On the other hand, it shows that, despite diverging requirements, the domain of Operational Technology (OT) can derive advantage from high-volume technology of the Information Technology (IT) domain. Based on these two sides of the same coin, we study the challenges and prospects of approaches to communication security in real-time Ethernet automation systems. In order to capitalize the expertise aggregated in decades of research and development, we put a special focus on the reuse of well-established security technology from the IT domain. We argue that enhancing such technology to become automation-friendly is likely to result in more robust and secure designs than greenfield designs. Because of its widespread deployment and the (to this date) nonexistence of a consistent security architecture, we use PROFINET as a showcase of our considerations. Security requirements for this technology are defined and different well-known solutions are examined according their suitability for PROFINET. Based on these findings, we elaborate the necessary adaptions for the deployment on PROFINET.
Solar irradiance prediction is vital for the power management and the cost reduction when integrating solar energy. The study is towards a ground image based solar irradiance prediction which is highly dependent on the cloud coverage. The sky images are collected by using ground based sky imager (fisheye lens). In this work, different algorithms for cloud detection being a preparation step for their segmentation are compared.
The comprehensive assessment method includes 80 innovation performance parameters and 10 key indicators of innovation capability, such as innovation process performance, innovating system performance, market and customer orientation, technology orientation, creativity, leadership, communication and knowledge management, risk and cost management, innovative climate, and innovation competences. The cross-industry study identifies parameters critical for innovation success and reveals different innovation performance patterns in companies.
In this paper the yield surface of a recently presented microstructure-based volume element of the gray cast iron material GJL-250 is assessed after different plastic loading histories. The evolution of the yield surface is investigated for different volumetric, deviatoric and uniaxial loadings. The micromechanical material properties of the metallic matrix and the graphite inclusions are validated by means experimental stress-strain hysteresis loops. The metallic matrix is modeled as elastic-plastic with a non-linear kinematic hardening law. The graphite inclusions are described by means of a volumetric strain state dependent Young’s modulus. The results show that the shape of the yield surface does not change significantly in comparison to the initial yield surface after pure deviatoric loadings. After volumetric loadings, the dependence of the material on the Lode angle is significantly reduced. Uniaxial tensile preloadings result in a deformed yield surface, whereby the magnitude of the deformation depends on the applied load. Uniaxial preloadings to compression do not change the shape of the initial yield surface.
Nowadays, robotic systems are an integral part of many orthopedic interventions. Stationary robots improve the accuracy but also require adapted surgical workflows. Handheld robotic devices (HHRDs), however, are easily integrated into existing workflows and represent a more economical solution. Their limited range of motion is compensated by the dexterity of the surgeon. This work presents control algorithms for HHRDs with multiple degrees of freedom (DOF). These algorithms protect pre- or intraoperatively defined regions from being penetrated by the end effector (e.g., a burr) by controlling the joints as well as the device’s power. Accuracy tests on a stationary prototype with three DOF show that the presented control algorithms produce results similar to those of stationary robots and much better results than conventional techniques. This work presents novel and innovative algorithms, which work robustly, accurately, and open up new opportunities for orthopedic interventions.
Conversion-Killer in Onlineshops - Identifikation von Kundenorientierung anhand von Mimikindikatoren
(2018)
Mimik als Ausdrucksform von Emotionen ist, seitdem es die Menschheit gibt, ein zentrales Verständigungsmittel in der Kommunikation. Da das Gegenüber in der Interaktion heute in vielen Situationen des täglichen Lebens eine Maschine ist, wäre es vorstellbar, dass die Mimik als Emotionsträger und Kommunikationsmittel seine Bedeutung verliert. Dies ist ein Irrtum: In der vorliegenden Untersuchung wird im Rahmen einer umfassenden Studie festgestellt, dass Mimik von der menschlichen Kommunikation schwer trennbar ist und auch in der Interaktion mit Maschinen unvermindert auftritt. Diese Erkenntnis kann Unternehmen helfen, den Mensch-Maschine-Dialog genau zu analysieren, um erfolgsmindernde Kundenirritationen zu eliminieren und die Abläufe optimal auf die Bedürfnisse der Nutzer anpassen zu können.
Corporate Governance
(2018)