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The allocation of tasks to Autonomous Mobile Robots in a production setting in combination with the most efficient parking and charging processes are the focus of this paper. This study presents a simulative evaluation of the theoretical allocation methods developed in Selmair and Maurer (2020) combined with either hard or dynamic availability rules to ascertain the most efficient parameters of an Autonomous Mobile Robot System. In order to quantify this efficiency, the following Key Performance Indicator (KPI) were considered: number of delayed orders, driven fleet metres and the percentage of available Autonomous Mobile Robot as determined by their state of charge. Additionally, as an alternative energy source, a fast-charging battery developed by Battery Streak Inc. was included in this study. The results show that, in comparison to a conventional and commonly used trivial strategy, our developed strategies provide superior results in terms of the relevant KPI.
The sample preparation for shadowing microscopy, to examinate biological soil properties, is time consuming, manual work. The outcome depends on subjective skills of the operator, furthermore the results are mostly not quantitative. The database on biological soil properties is mostly not sufficient for an integrated modelling on an multidisciplinary scale. This project combines three progressive approaches to develop a tool that is easy to use and gives in situ results that can be used for many purposes.
The primary consumers of plant exudates – in exact fungi and bacteria, are representative for the soil succession level from bare soil, which is bacterial dominant to old growth forest constitute by fungal dominance. In a specified level of soil succession, a special kind of plant family benefits on the Fungal to Bacteria Ratio. The ability to determine this ratio in situ without complex chemical applications is part of the project Electronical Laboratory for Intelligent Soil Examination (ELISE). Several mechanical and optical tests on soil samples are covered within this Project. To analyze the fungal to bacteria ratio, samples are prepared automatically – in a defined and reproductive procedure – to generate slides for shadowing microscopy. The samples are observed by a camera, which is attached to a transmitted light microscope. The automatic analysis, done with computer vision algorithms, aims to quantify bacterial and fungal biomass in the actual sample view. Moreover, the algorithm can classify organisms according to their color and shape.
To get a processable picture, several images from different focal levels must be taken through the sample thickness. Parts of each image, are in focus at the actual layer, are merged to a whole depth of field picture, by focus stacking.
This produced picture is used to classify, locate and quantify – in first step filamentous organisms e.g. fungal by image sematic segmentation. The result represents an image sized mask, which indicates the class of fungi with class equivalate values at the pixel positions – covered by the organism. This information is used to calculate the fungal mass per gram soil.
To quantify the bacterial biomass two approaches are implemented. For low density of bacterial existence, the individual bacteria is counted for a part of the field of view by an image detection algorithm to be extrapolate afterwards to the mass per gram soil. For high density of bacterial occurrence, specified regions of interest with only bacteria present are chosen. An image classification which has been pretrained by pictures of bacterial density patterns – previously determent by making the sample countable due to preforming sample dilutions, is done. The second option for high density bacterial count is, to automatically preform dilutions until the image detection is confidently countable.
To ensure a usable confidence score a statistical approach of many fields of view is taken.
Manually defined control limits remain a common strategy for quality control in manufacturing due to their ease of deployment on the shop floor compared to more advanced data analysis approaches. Despite their continued importance, there is no systematic method of defining these control limits. However, sub-optimal control limits can lead to undetected faults or cause unnecessary interruption to production. This manuscript presents an algorithm that systematizes this manual process into an efficient search task. We conceptualized the search task as a sequence of sub-problems that are based on the conventional steps taken by process experts when defining control limits. This algorithm can be integrated into an expert tool for shop floor personnel to automate the definition of control limits in annotated time series data. We demonstrate the efficacy of the control limits found by our algorithm by comparing them to those manually defined by process experts in real-world process data from the automotive industry. Furthermore, we show that our algorithm generalizes to traditional time series classification problems and achieves state-of-the-art performance on selected benchmark datasets. Our work is the first effort in automating the otherwise manual definition of control limits for fault detection.
Accurate and reliable information about three-dimensional (3D) knee joint laxity can prevent misdiagnosis and avoid incorrect treatments. Nevertheless, knee laxity assessments presented in the literature suffer from significant drawbacks such as soft tissue artifacts, restricting the knee within the measurement, and the absence of quantitative knee ligament property information. In this study, we demonstrated the applicability of a novel methodology for measuring 3D knee laxity, combining robotics- and image-based technology. As such technology has never been applied to healthy living subjects, the aims of this study were to develop novel technology to measure 3D knee laxity in vivo and to provide proof-of-concept 3D knee laxity measurements. To measure tibiofemoral movements, four healthy subjects were placed on a custom-built arthrometer located inside a low dose biplanar X-ray system with an approximately 60 deg knee flexion angle. Anteroposterior and mediolateral translation as well as internal and external rotation loads were subsequently applied to the unconstrained leg, which was placed inside a pneumatic cast boot. Bone contours were segmented in the obtained X-rays, to which subject-specific bone geometries from magnetic resonance imaging (MRI) scans were registered. Afterward, tibiofemoral poses were computed. Measurements of primary and secondary laxity revealed considerable interpersonal differences. The method differs from those available by the ability to accurately track secondary laxity of the unrestricted knee and to apply coupled forces in multiple planes. Our methodology can provide reliable information for academic knee ligament research as well as for clinical diagnostics in the future.
In recent years, particulate filters have become mandatory in almost all gasoline-powered vehicles to comply with emission standards regarding particulate number. In contrast to diesel applications, monitoring gasoline particulate filters (GPFs) by differential pressure sensors is challenging due to lower soot masses to be deposited in the GPFs. A different approach to determine the soot loading of GPFs is a radio frequency-based sensor (RF sensor). To facilitate sensor development, in previous work, a simulation model was created to determine the RF signal at arbitrary engine operating points. To ensure accuracy, the exact dielectric properties of the soot need to be known. This work has shown how small samples of soot-loaded filter are sufficient to determine the dielectric properties of soot itself using the microwave cavity perturbation method. For this purpose, mixing rules were determined through simulation and measurement, allowing the air and substrate fraction of the sample to be considered. Due to the different geometry of filter substrates compared to crushed soot samples, a different mixing rule had to be derived to calculate the effective filter properties required for the simulation model. The accuracy of the determined mixing rules and the underlying simulation model could be verified by comparative measurements on an engine test bench.
In recent years, the Tuned Mass Damper with inerter (TMDI) has received significant attention. The inerter is defined to exert a force that is in proportion to the relative acceleration of the two inerter terminals. Here, two TMDI topologies are investigated. The conventional topology is given by the inerter being in parallel to the spring and viscous damper of the TMDI. The other topology is the serial arrangement of spring, inerter and viscous damper being in parallel to the stiffness of the mass spring oscillator of the TMDI. While the first topology intends to increase the inertial force of the TMDI, the second topology aims at producing an additional degree of freedom. The considered TMDI concepts are simulated for harmonic and random excitations, with parameters set according to those described in the literature and with numerically optimized parameters which minimize the primary structure displacement response. The classical TMD is used as a benchmark. The findings are twofold. The conventional TMDI with typical inertance ratio of 1% and the very small value of 0.02% performs significantly worse than the classical TMD with the same mass ratio. In contrast, the TMDI with an additional degree of freedom can improve the mitigation of the primary structure if the inertance ratio is set very small and if the TMDI parameters are numerically optimized.
This paper introduces the results of an experimental study on the adsorption and desorption kinetics of a commercially available, open-structured asymmetric plate heat exchanger adapted to act as an adsorber/desorber for the application in adsorption heat transformation processes. In addition, a volumetric large temperature jump (V-LTJ) kinetic setup was applied to measure the adsorption and desorption kinetics of a small-scale adsorbent sample prepared dedicatedly to be representative for the adsorbent domain inside the investigated adsorber plate heat exchanger (APHE). All kinetic results of the small-scale adsorbent sample and the APHE were fitted into exponential forms with a single characteristic time constant (τ) with a coefficient of determination (R2) better than 0.9531. A very good matching between the small-scale and full-scale adsorption kinetic measurements was obtained, with an average relative deviation of 12.3% in the obtained τ-values. In addition, the kinetic data of the small-scale adsorbent sample were utilized for estimating the expected specific instantaneous and moving average powers of the evaporator/condenser heat exchanger. The average relative deviation (ARD) between the moving average specific evaporator powers obtained from the small-scale and the full-scale measurements amounts between 5.4 and 15.1%.
Non-road sectors, such as agriculture and construction machinery, require high energy densities and flexibility in use, which is why diesel engines are mainly used. The use of climate-neutral fuels, produced from renewable energies, such as Oxymethylene Ether (OME) as a diesel substitute, can significantly reduce CO2 and pollutant emissions in these sectors. In addition to CO2 neutrality, OME also offers improved combustion characteristics compared to diesel fuel, eliminating the soot–NOx trade-off and thus enabling new opportunities in engine design and calibration. In this paper, the combustion of pure OME on a close-to-production, single-cylinder non-road diesel engine with a pump–line–nozzle injection system is analyzed. A variation of the center of combustion at constant power output was performed for diesel and OME at different operating points. Two injectors were investigated with OME. A study on ignition delay and a detailed thermodynamic analysis was carried out. In addition, the exhaust emissions CO, NOx, VOC, as well as particulate-matter, -number and -size distributions were measured. With OME, a significantly shorter ignition delay as well as a shortened combustion duration could be observed, despite a longer injection duration. In addition, the maximum injection pressure increases. VOC and CO emissions are reduced. Particulate matter was reduced by more than 99% and particle number (>10 nm) was reduced by multiple orders of magnitude. The median of the particle size distribution shifts from 60 to 85 nm (diesel) into a diameter range of sub 23 nm (OME). A significant reduction of NOx emissions with OME enables new degrees of freedom in engine calibration and an efficiency advantage without hardware adaption.