@article{UlmerBraunChengetal.2020, author = {Ulmer, Jessica and Braun, Sebastian and Cheng, Chi-Tsun and Dowey, Steve and Wollert, J{\"o}rg}, title = {Human-Centered Gamification Framework for Manufacturing Systems}, series = {Procedia CIRP}, volume = {93}, journal = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2020.04.076}, pages = {670 -- 675}, year = {2020}, abstract = {While bringing new opportunities, the Industry 4.0 movement also imposes new challenges to the manufacturing industry and all its stakeholders. In this competitive environment, a skilled and engaged workforce is a key to success. Gamification can generate valuable feedbacks for improving employees' engagement and performance. Currently, Gamification in workspaces focuses on computer-based assignments and training, while tasks that require manual labor are rarely considered. This research provides an overview of Enterprise Gamification approaches and evaluates the challenges. Based on that, a skill-based Gamification framework for manual tasks is proposed, and a case study in the Industry 4.0 model factory is shown.}, language = {en} } @article{WollbrinkMasloZimmeretal.2020, author = {Wollbrink, Moritz and Maslo, Semir and Zimmer, Daniel and Abbas, Karim and Arntz, Kristian and Bergs, Thomas}, title = {Clamping and substrate plate system for continuous additive build-up and post-processing of metal parts}, series = {Procedia CIRP}, volume = {93}, journal = {Procedia CIRP}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2212-8271}, doi = {10.1016/j.procir.2020.04.015}, pages = {108 -- 113}, year = {2020}, abstract = {The manufacturing share of laser powder bed fusion (L-PBF) increases in industrial application, but still many process steps are manually operated. Additionally, it is not possible to achieve tight dimensional tolerances or low surfaces roughness. Hence, a process chain has to be set up to combine additive manufacturing (AM) with further machining technologies. To achieve a continuous workpiece flow as basis for further industrialization of L-PBF, the paper presents a novel substrate system and its application on L-PBF machines and post-processing. The substrate system consists of a zero-point clamping system and a matrix-like interface of contact pins to be substantially connected to the workpiece within the L-PBF process.}, language = {en} } @inproceedings{SchmidtsKraftWinkensetal.2020, author = {Schmidts, Oliver and Kraft, Bodo and Winkens, Marvin and Z{\"u}ndorf, Albert}, title = {Catalog integration of low-quality product data by attribute label ranking}, series = {Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA}, booktitle = {Proceedings of the 9th International Conference on Data Science, Technology and Applications - Volume 1: DATA}, isbn = {978-989-758-440-4}, doi = {10.5220/0009831000900101}, pages = {90 -- 101}, year = {2020}, language = {en} } @article{FrankoDuKallweitetal.2020, author = {Franko, Josef and Du, Shengzhi and Kallweit, Stephan and Duelberg, Enno Sebastian and Engemann, Heiko}, title = {Design of a Multi-Robot System for Wind Turbine Maintenance}, series = {Energies}, volume = {13}, journal = {Energies}, number = {10}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {10.3390/en13102552}, pages = {Article 2552}, year = {2020}, abstract = {The maintenance of wind turbines is of growing importance considering the transition to renewable energy. This paper presents a multi-robot-approach for automated wind turbine maintenance including a novel climbing robot. Currently, wind turbine maintenance remains a manual task, which is monotonous, dangerous, and also physically demanding due to the large scale of wind turbines. Technical climbers are required to work at significant heights, even in bad weather conditions. Furthermore, a skilled labor force with sufficient knowledge in repairing fiber composite material is rare. Autonomous mobile systems enable the digitization of the maintenance process. They can be designed for weather-independent operations. This work contributes to the development and experimental validation of a maintenance system consisting of multiple robotic platforms for a variety of tasks, such as wind turbine tower and rotor blade service. In this work, multicopters with vision and LiDAR sensors for global inspection are used to guide slower climbing robots. Light-weight magnetic climbers with surface contact were used to analyze structure parts with non-destructive inspection methods and to locally repair smaller defects. Localization was enabled by adapting odometry for conical-shaped surfaces considering additional navigation sensors. Magnets were suitable for steel towers to clamp onto the surface. A friction-based climbing ring robot (SMART— Scanning, Monitoring, Analyzing, Repair and Transportation) completed the set-up for higher payload. The maintenance period could be extended by using weather-proofed maintenance robots. The multi-robot-system was running the Robot Operating System (ROS). Additionally, first steps towards machine learning would enable maintenance staff to use pattern classification for fault diagnosis in order to operate safely from the ground in the future.}, language = {en} } @article{KhayyamJamaliBabHadiasharetal.2020, author = {Khayyam, Hamid and Jamali, Ali and Bab-Hadiashar, Alireza and Esch, Thomas and Ramakrishna, Seeram and Jalili, Mahdi and Naebe, Minoo}, title = {A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modeling with Application in Industry 4.0}, series = {IEEE Access}, volume = {8}, journal = {IEEE Access}, number = {Art. 9108222}, publisher = {IEEE}, address = {New York, NY}, issn = {2169-3536}, doi = {10.1109/ACCESS.2020.2999898}, pages = {111381 -- 111393}, year = {2020}, abstract = {To meet the challenges of manufacturing smart products, the manufacturing plants have been radically changed to become smart factories underpinned by industry 4.0 technologies. The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data. This manuscript reviews these concepts and present a case study that demonstrates the use of a novel intelligent hybrid algorithms for Industry 4.0 applications with limited data. In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data. In our approach, a novel hybrid data-driven combining the Group-Method of Data-Handling and Singular-Value Decomposition is adapted to find an offline deterministic model combined with Pareto multi-objective optimization to overcome the overfitting issue. An Unscented-Kalman-Filter is also incorporated to update the coefficient of the deterministic model and increase its robustness against data uncertainties. The effectiveness of the proposed method is examined on a set of real industrial measurements.}, language = {en} } @article{KellerRathBruckmannetal.2020, author = {Keller, Johannes and Rath, Volker and Bruckmann, Johanna and Mottaghy, Darius and Clauser, Christoph and Wolf, Andreas and Seidler, Ralf and B{\"u}cker, H. Martin and Klitzsch, Norbert}, title = {SHEMAT-Suite: An open-source code for simulating flow, heat and species transport in porous media}, series = {SoftwareX}, volume = {12}, journal = {SoftwareX}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-7110}, doi = {10.1016/j.softx.2020.100533}, pages = {9}, year = {2020}, abstract = {SHEMAT-Suite is a finite-difference open-source code for simulating coupled flow, heat and species transport in porous media. The code, written in Fortran-95, originates from geoscientific research in the fields of geothermics and hydrogeology. It comprises: (1) a versatile handling of input and output, (2) a modular framework for subsurface parameter modeling, (3) a multi-level OpenMP parallelization, (4) parameter estimation and data assimilation by stochastic approaches (Monte Carlo, Ensemble Kalman filter) and by deterministic Bayesian approaches based on automatic differentiation for calculating exact (truncation error-free) derivatives of the forward code.}, language = {en} } @article{AkimbekovQiaoDigeletal.2020, author = {Akimbekov, Nuraly and Qiao, Xiaohui and Digel, Ilya and Abdieva, Gulzhamal and Ualieva, Perizat and Zhubanova, Azhar}, title = {The effect of leonardite-derived amendments on soil microbiome structure and potato yield}, series = {Agriculture}, volume = {10}, journal = {Agriculture}, number = {Art. 147}, publisher = {MDPI}, address = {Basel}, doi = {10.3390/agriculture10050147}, pages = {1 -- 17}, year = {2020}, abstract = {Humic substances originating from various organic matters can ameliorate soil properties, stimulate plant growth, and improve nutrient uptake. Due to the low calorific heating value, leonardite is rather unsuitable as fuel. However, it may serve as a potential source of humic substances. This study was aimed at characterizing the leonardite-based soil amendments and examining the effect of their application on the soil microbial community, as well as on potato growth and tuber yield. A high yield (71.1\%) of humic acid (LHA) from leonardite has been demonstrated. Parental leonardite (PL) and LHA were applied to soil prior to potato cultivation. The 16S rRNA sequencing of soil samples revealed distinct relationships between microbial community composition and the application of leonardite-based soil amendments. Potato tubers were planted in pots in greenhouse conditions. The tubers were harvested at the mature stage for the determination of growth and yield parameters. The results demonstrated that the LHA treatments had a significant effect on increasing potato growth (54.9\%) and tuber yield (66.4\%) when compared to the control. The findings highlight the importance of amending leonardite-based humic products for maintaining the biogeochemical stability of soils, for keeping their healthy microbial community structure, and for increasing the agronomic productivity of potato plants.}, language = {en} } @article{NobisSchmittSchemmetal.2020, author = {Nobis, Moritz and Schmitt, Carlo and Schemm, Ralf and Schnettler, Armin}, title = {Pan-European CVAR-constrained stochastic unit commitment in day-ahead and intraday electricity markets}, series = {Energies}, volume = {13}, journal = {Energies}, number = {Art. 2339}, publisher = {MDPI}, address = {Basel}, issn = {1996-1073}, doi = {10.3390/en13092339}, pages = {1 -- 35}, year = {2020}, abstract = {The fundamental modeling of energy systems through individual unit commitment decisions is crucial for energy system planning. However, current large-scale models are not capable of including uncertainties or even risk-averse behavior arising from forecasting errors of variable renewable energies. However, risks associated with uncertain forecasting errors have become increasingly relevant within the process of decarbonization. The intraday market serves to compensate for these forecasting errors. Thus, the uncertainty of forecasting errors results in uncertain intraday prices and quantities. Therefore, this paper proposes a two-stage risk-constrained stochastic optimization approach to fundamentally model unit commitment decisions facing an uncertain intraday market. By the nesting of Lagrangian relaxation and an extended Benders decomposition, this model can be applied to large-scale, e.g., pan-European, power systems. The approach is applied to scenarios for 2023—considering a full nuclear phase-out in Germany—and 2035—considering a full coal phase-out in Germany. First, the influence of the risk factors is evaluated. Furthermore, an evaluation of the market prices shows an increase in price levels as well as an increasing day-ahead-intraday spread in 2023 and in 2035. Finally, it is shown that intraday cross-border trading has a significant influence on trading volumes and prices and ensures a more efficient allocation of resources.}, language = {en} } @article{RaffeisAdjeiKyeremehVroomenetal.2020, author = {Raffeis, Iris and Adjei-Kyeremeh, Frank and Vroomen, Uwe and Westhoff, Elmar and Bremen, Sebastian and Hohoi, Alexandru and B{\"u}hrig-Polaczek, Andreas}, title = {Qualification of a Ni-Cu alloy for the laser powder bed fusion process (LPBF): Its microstructure and mechanical properties}, series = {Applied Sciences}, volume = {10}, journal = {Applied Sciences}, number = {Art. 3401}, publisher = {MDPI}, address = {Basel}, issn = {2076-3417}, doi = {10.3390/app10103401}, pages = {1 -- 15}, year = {2020}, abstract = {As researchers continue to seek the expansion of the material base for additive manufacturing, there is a need to focus attention on the Ni-Cu group of alloys which conventionally has wide industrial applications. In this work, the G-NiCu30Nb casting alloy, a variant of the Monel family of alloys with Nb and high Si content is, for the first time, processed via the laser powder bed fusion process (LPBF). Being novel to the LPBF processes, optimum LPBF parameters were determined, and hardness and tensile tests were performed in as-built conditions and after heat treatment at 1000 °C. Microstructures of the as-cast and the as-built condition were compared. Highly dense samples (99.8\% density) were achieved after varying hatch distance (80 µm and 140 µm) with scanning speed (550 mm/s-1500 mm/s). There was no significant difference in microhardness between varied hatch distance print sets. Microhardness of the as-built condition (247 HV0.2) exceeded the as-cast microhardness (179 HV0.2.). Tensile specimens built in vertical (V) and horizontal (H) orientations revealed degrees of anisotropy and were superior to conventionally reported figures. Post heat treatment increased ductility from 20\% to 31\% (V), as well as from 16\% to 25\% (H), while ultimate tensile strength (UTS) and yield strength (YS) were considerably reduced.}, language = {en} } @article{OezsoyluKizildagSchoeningetal.2020, author = {{\"O}zsoylu, Dua and Kizildag, Sefa and Sch{\"o}ning, Michael Josef and Wagner, Torsten}, title = {Differential chemical imaging of extracellular acidification within microfluidic channels using a plasma-functionalized light-addressable potentiometric sensor (LAPS)}, series = {Physics in Medicine}, volume = {10}, journal = {Physics in Medicine}, number = {100030}, publisher = {Elsevier}, address = {Amsterdam}, issn = {2352-4510}, doi = {10.1016/j.phmed.2020.100030}, pages = {8}, year = {2020}, abstract = {Extracellular acidification is a basic indicator for alterations in two vital metabolic pathways: glycolysis and cellular respiration. Measuring these alterations by monitoring extracellular acidification using cell-based biosensors such as LAPS plays an important role in studying these pathways whose disorders are associated with numerous diseases including cancer. However, the surface of the biosensors must be specially tailored to ensure high cell compatibility so that cells can represent more in vivo-like behavior, which is critical to gain more realistic in vitro results from the analyses, e.g., drug discovery experiments. In this work, O2 plasma patterning on the LAPS surface is studied to enhance surface features of the sensor chip, e.g., wettability and biofunctionality. The surface treated with O2 plasma for 30 s exhibits enhanced cytocompatibility for adherent CHO-K1 cells, which promotes cell spreading and proliferation. The plasma-modified LAPS chip is then integrated into a microfluidic system, which provides two identical channels to facilitate differential measurements of the extracellular acidification of CHO-K1 cells. To the best of our knowledge, it is the first time that extracellular acidification within microfluidic channels is quantitatively visualized as differential (bio-)chemical images.}, language = {en} }