@misc{13271,
abstract = {{Pasteurization is the prevalent method for stabilizing cloudy apple juice and prolonging its shelf life, but can also impair quality. Therefore, it is necessary to investigate and quantify the chemical, physical and sensory effects of this treatment. In this study, cloudy apple juice was treated at different time-temperature combinations with equivalent microbial lethality with 161.6 PU. These can be categorized as low temperature/long time (LTLT with 70°C and 80°C) or high temperature/short time (HTST with 90°C, 100°C and 105°C) treatments. The results were compared to those of untreated juice. HTST treatment had significantly less impact on the juice compared to LTLT treatment. LTLT-treated juices were characterized by different sensory attributes, such as raisin and caramel odor and bitter taste. In contrast, the untreated and HTST-treated juices exhibited odors like pear and lemon. There were also significant differences in turbidity, sugar composition, viscosity and a heightened 5-(hydroxymethyl)furfural (HMF) content with LTLT treatment. In summary, HTST-treated juices are more similar to the untreated juices and are rated higher in terms of quality characteristics. The lowest pasteurization temperature of 70°C results in the greatest deterioration of juice quality. It can be concluded that different pasteurization conditions showed different effects on juice quality, despite having the same microbiological lethality of 161.6 PU. Results can be considered when designing pasteurization processes.}},
author = {{Katsch, Linda and Sokolowsky, Martina and Gibson, Brian and Schneider, Jan}},
booktitle = {{Applied Food Research}},
issn = {{2772-5022}},
keywords = {{Cloudy apple juice, HTST, juice pasteurization, sensory analysis, process optimization, equivalent microbial lethality}},
number = {{2}},
publisher = {{Elsevier}},
title = {{{Influence of different pasteurization conditions with equivalent pasteurization units on chemical, physical, and sensory properties of cloudy apple juice}}},
doi = {{10.1016/j.afres.2025.101471}},
volume = {{5}},
year = {{2025}},
}
@misc{13433,
abstract = {{The use of lubricants on electrical connector contacts is essential for certain applications in order to reduce mating forces, minimize wear, and mitigate fretting corrosion. However, increasing performance requirements (e.g., operation at elevated temperatures) and regulatory restrictions are prompting a reassessment of lubricant selection. In this study, the influence of three lubricants (one oil and two greases), operating temperature (room temperature and 130°C), and applied volume (low, medium and high volume) on electrical contact resistance (ECR) and coefficient of friction (CoF) is investigated by means of fretting wear tests on silver-plated contacts. The results show that unlubricated silver contacts exhibit a longer stable phase at 130 °C than at room temperature. For lubricated contacts, service life is influenced by both temperature and lubricant volume. Certain lubricants demonstrate earlier fail at room temperature than at elevated temperatures. The findings highlight the importance of careful selection and optimization of lubricants for electrical connectors under varying environmental conditions.}},
author = {{Blauth, Michael and Tülling, Sören and Song, Jian}},
booktitle = {{Proceedings of the 70th IEEE Holm Conference on Electrical Contacts (HLM)}},
isbn = {{979-8-3315-5997-7}},
issn = {{2158-9992}},
keywords = {{Connectors, Resistance, Silver, Lubricants, Contacts, Oils, Friction, Corrosion, Optimization}},
location = {{San Antonio, TX, USA }},
publisher = {{IEEE}},
title = {{{Effect of Operating Temperature and Application Quantity of Lubricants on the Fretting Behavior of Silver Plated Electrical Contacts}}},
doi = {{10.1109/hlm51652.2025.11278329}},
year = {{2025}},
}
@misc{10985,
abstract = {{This thesis examines the impacts of scene and rendering optimizations techniques aimed for GPU rendering within the realm of animated scene for offline rendering. Focusing on the unique challenges posed by dynamic animations, the research explores how these rendering approaches impact rendering speed, memory efficiency, and the attainment of lifelike visual quality. Using Octane render as ground for analysis, this study aims to uncover how scene optimizations intersect with the demands of animated content. Through meticulous comparison, this thesis endeavours to provide insights that empower rendering practitioners and animators to go through optimization strategies, bridging the gap between efficient resource allocation and the pursuit of captivating visual.}},
author = {{De Melo Bernardino, Lucas}},
keywords = {{GPU Rendering, Scene Optimization, 3D Rendering, Cinema 4D, Octane Render}},
pages = {{55}},
publisher = {{Technischen Hochschule Ostwestfahlen-Lippe}},
title = {{{Enhancing GPU Rendering Efficiency through Scene Optimizations: A Case Study using Octane for Cinema 4D}}},
year = {{2024}},
}
@misc{12009,
abstract = {{AbstractTraditional work models often need more flexibility and time autonomy for employees, especially in manufacturing. Quantitative approaches and Artificial Intelligence (AI) applications offer the potential to improve work design. However, current research does not entirely focus on human-centric criteria that enable time autonomy. This paper addresses this gap by developing a set of criteria to evaluate intelligent personnel planning approaches based on their ability to enhance time autonomy for employees. Existing quantitative approaches are not sufficient to fully integrate the developed criteria.Consequently, a novel model approach is proposed in an attempt to bridge the gap between current practices and the newly developed criteria. This two-stage planning approach fosters democratization of time autonomy on the shopfloor, moving beyond traditional top-down scheduling. The paper concludes by outlining the implementation process and discusses future developments with respect to AI for this model approach.Practical Relevance: In order to make working conditions on the shopfloor in high-wage countries more attractive, an alternative organization of shift work is needed. Intelligent planning approaches that combine traditional operations research methods with artificial intelligence approaches can democratize shift organization regarding time autonomy. Planning that takes both employee and employer preferences into account in a balanced way will strengthen the long-term competitiveness of manufacturing companies in high-wage countries and counteract the shortage of skilled labor.}},
author = {{Latos, Benedikt and Buckhorst, Armin and Kalantar, Peyman and Bentler, Dominik and Gabriel, Stefan and Dumitrescu, Roman and Minge, Michael and Steinmann, Barbara and Guhr, Nadine}},
booktitle = {{Zeitschrift für Arbeitswissenschaft}},
issn = {{2366-4681}},
keywords = {{Personnel Planning, Time Autonomy, Human-Centric Optimization, Artificial Intelligence, Manufacturing}},
number = {{3}},
pages = {{277--298}},
publisher = {{Springer-Verlag GmbH}},
title = {{{Time autonomy in personnel planning: Requirements and solution approaches in the context of intelligent scheduling from a holistic organizational perspective }}},
doi = {{10.1007/s41449-024-00432-7}},
volume = {{78}},
year = {{2024}},
}
@phdthesis{13335,
abstract = {{The process of thermal preservation of liquid foods is a safety-relevant process step in the processing of products such as fruit juices and is associated with a high-energy expenditure and safety margin. There are already various approaches to improve this conventionally managed process step in terms of product and resource preservation. Compared to these novel technologies, the use of real-time process analytics offers great potential to improve already existing process plants by implementing inline capable process analytical tools. This allows direct control of the reactions taking place and changes during the running process. Instead of post process, random product control, quality control during the process can be made rendered. The chemical and pharmaceutical industry serves as a reference industry for the use of process analytical tools, although the reactions and product matrices are less complex. In the food industry, on the other hand, there is a greater variation in raw materials and intermediate products. In addition, a large number of reactions can take place in parallel within a process, and the physical states and properties of the individual components can vary. A uniform set of rules for the use of process analytical tools does not exist here. Each product, each process provides its own research potential, so that a large research gap opens up in the area of the food industry.
In order to contribute to closing this gap, this thesis presents a novel approach to improve the process of pasteurization of liquid food. For fruit juices as an application, near infrared spectroscopy in combination with chemometric methods was applied to make the process more product specific. Based on known weaknesses of the process, the relevant aspects for a product-specific treatment were identified. In the further course, the suitability of near infrared spectroscopy as a process analytical tool in the process of pasteurization was verified. Moreover, it was investigated whether a sufficiently accurate identification of the product type as well as the microbiologically relevant properties can be achieved by the application of chemometric methods. In the course of this, the suitability of the measurement methodology was confirmed and solutions were established for any process influences. The product classification and description of the microbiologically relevant parameters extract content and pH value were also implemented with sufficient accuracy. Knowing the destruction kinetics of relevant microorganisms, the product-specific determination of target values for the necessary lethal heat input could be realized. In addition, an analysis of the actual values was carried out on the basis of a chemometric regression method by inferring the microbiological pasteurization effect through the chemical reaction of acid hydrolytic sucrose degradation by means of the indirect approach. This required knowledge of the chemical reaction kinetics and mahematical modeling of the degradation behavior. The novel approach could be confirmed by calculations using results from off-line analysis, whereas the use of near infrared spectroscopy as an inline method still revealed potential for optimization with respect to measurement accuracies.
In summary, the results of this work provide a promising opportunity to make conventional processes for the preservation of liquid foods more product-specific by using near-infrared spectroscopy as an inline-capable and multimodal sensor technique, leading to an increase in process efficiency and product quality.}},
author = {{Weishaupt, Imke}},
keywords = {{fruit juice pasteurization, near infrared spectroscopy, process optimization, multivariate statistics, inline process analytics, Fruchtsaftpasteurisation, Prozessoptimierung, Nahinfrarotspektroskopie, multivariate Statistik, Inline-Prozessanalytik}},
pages = {{144}},
publisher = {{Technische Universität Berlin}},
title = {{{Near infrared spectroscopy as inline analytical tool to optimize the pasteurization process of liquid foods}}},
doi = {{https://doi.org/10.14279/depositonce-17804}},
year = {{2023}},
}
@misc{10787,
abstract = {{Cyber-physical production systems have emerged with the rise of Industry 4.0 in different industrial fields. Especially the food sector, where inhomogeneous input products like beer/yeast suspensions with different qualities and properties have yet slowed down automation, has potential for this evolution. This contribution presents optimization methods for a dynamical cross-flow filtration plant which is driven by an advanced control concept in combination with data driven product monitoring via inline near infrared spectroscopy (NIR) in order to improve energy savings and filtration performance. Using a hierarchical control and optimization structure, the non stationary batch process is steered towards a high production rate with low energy consumption for a variety of different input products.}},
author = {{Tebbe, Jörn and Pawlik, Thomas and Trilling-Haasler, Marc and Löbner, Jannis and Lange-Hegermann, Markus and Schneider, Jan}},
booktitle = {{2023 IEEE 21st International Conference on Industrial Informatics (INDIN)}},
editor = {{Jasperneite, Jürgen and Wisniewski, Lukasz and Fung Man, Kim}},
isbn = {{978-1-6654-9314-7 }},
issn = {{1935-4576}},
keywords = {{Spectroscopy, Production systems, Filtration, Velocity control, Optimization methods, Cyber-physical systems, Nonhomogeneous media}},
location = {{Lemgo}},
pages = {{1--7}},
publisher = {{IEEE}},
title = {{{Holistic optimization of a dynamic cross-flow filtration process towards a cyber-physical system}}},
doi = {{10.1109/INDIN51400.2023.10217913}},
year = {{2023}},
}
@misc{11428,
abstract = {{Systems that place high demands on availability are typically modular in design. However, a modular design also offers potential for optimized operation under norma requirements. In this paper we present an approach to find optimal operating points from the characteristic fields of individual modules. Our approach consists of a two-step procedure. In the first stage, Pareto sets are calculated using the NSGA-II genetic algorithm. The second stage contains a heuristic that finds situationally optimal operating points using a defined operating strategy.}},
author = {{Lammersen, Maximilian and Rasche, Rainer}},
booktitle = {{Conference proceedings of Mecatronics & AISM 2023}},
keywords = {{modularity, optimization, PEBB, operating strategy, genetic algorithm, Pareto}},
location = {{Yokohama}},
publisher = {{*}},
title = {{{Optimized Operating Points for Power Electronic Building Blocks}}},
year = {{2023}},
}
@misc{12806,
abstract = {{Cyber-Physical Systems (CPS) play an essential role in today’s production processes, leveraging Artificial Intelligence (AI) to enhance operations such as optimization, anomaly detection, and predictive maintenance. This article reviews a cognitive architecture for Artificial Intelligence, which has been developed to establish a standard framework for integrating AI solutions into existing production processes. Given that machines in these processes continuously generate large streams of data, Online Machine Learning (OML) is identified as a crucial extension to the existing architecture. To substantiate this claim, real-world experiments using a slitting machine are conducted, to compare the performance of OML to traditional Batch Machine Learning. The assessment of contemporary OML algorithms using a real production system is a fundamental innovation in this research. The evaluations clearly indicate that OML adds significant value to CPS, and it is strongly recommended as an extension of related architectures, such as the cognitive architecture for AI discussed in this article. Additionally, surrogate-model-based optimization is employed, to determine the optimal hyperparameter settings for the corresponding OML algorithms, aiming to achieve peak performance in their respective tasks.}},
author = {{Hinterleitner, Alexander and Schulz, Richard and Hans, Lukas and Subbotin, Aleksandr and Barthel, Nils and Pütz, Noah and Rosellen, Martin and Bartz-Beielstein, Thomas and Geng, Christoph and Priss, Phillip}},
booktitle = {{ Applied Sciences : open access journal}},
issn = {{2076-3417}},
keywords = {{machine learning, online algorithms, cyber-physical production systems, surrogate-based optimization}},
number = {{20}},
publisher = {{MDPI AG}},
title = {{{Online Machine Learning and Surrogate-Model-Based Optimization for Improved Production Processes Using a Cognitive Architecture}}},
doi = {{10.3390/app132011506}},
volume = {{13}},
year = {{2023}},
}
@misc{11377,
abstract = {{consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.}},
author = {{Hernández Rodriguez, Tanja and Sekulic, Anton and Lange-Hegermann, Markus and Frahm, Björn}},
booktitle = {{Processes}},
issn = {{2227-9717}},
keywords = {{Gaussian processes, Bayes optimization, Pareto optimization, multi-objective, cell culture, seed train}},
number = {{5}},
publisher = {{MDPI AG}},
title = {{{Designing Robust Biotechnological Processes Regarding Variabilities Using Multi-Objective Optimization Applied to a Biopharmaceutical Seed Train Design}}},
doi = {{10.3390/pr10050883}},
volume = {{10}},
year = {{2022}},
}
@misc{8024,
abstract = {{For the optimization of the impinging round jet, the pressure force coefficient and drying energy consumption on the moving curved surface are set as the objective functions to be minimized simultaneously. SHERPA search algorithm is used to search for the optimal point from multiple objective tradeoff study (Pareto Front) method. It is found that the pressure force coefficient on the impingement surface is highly dependent on the jet to surface distance and jet angle, while the drying energy consumption is highly dependent on the jet to jet spacing. Generally, the best design study during the multi-objective optimization is found at the maximum jet to surface distance, jet to jet spacing and surface velocity, and also minimum inlet velocity and jet angle. }},
author = {{Chitsazan, Ali and Klepp, Georg Heinrich and Chitsazan, Mohammad Esmaeil and Glasmacher, Birgit}},
booktitle = {{Frontiers in heat and mass transfer : FHMT ; an international journal }},
issn = {{2151-8629}},
keywords = {{Multiple jets, Heat transfer, Pressure force, Energy consumption, Optimization}},
publisher = {{Global Digital Centra}},
title = {{{MULTI-OBJECTIVE OPTIMIZATION OF DRYING ENERGY CONSUMPTION AND JET IMPINGEMENT FORCE ON A MOVING CURVED SURFACE}}},
doi = {{10.5098/hmt.18.17}},
volume = {{18}},
year = {{2022}},
}
@inbook{10193,
abstract = {{Development and optimization of biopharmaceutical production processes with cell cultures is cost- and time-consuming and often performed rather empirically. Efficient optimization of multiple objectives such as process time, viable cell density, number of operating steps & cultivation scales, required medium, amount of product as well as product quality depicts a promising approach. This contribution presents a workflow which couples uncertainty-based upstream simulation and Bayes optimization using Gaussian processes. Its application is demonstrated in a simulation case study for a relevant industrial task in process development, the design of a robust cell culture expansion process (seed train), meaning that despite uncertainties and variabilities concerning cell growth, low variations of viable cell density during the seed train are obtained. Compared to a non-optimized reference seed train, the optimized process showed much lower deviation rates regarding viable cell densities (<10% instead of 41.7%) using five or four shake flask scales and seed train duration could be reduced by 56 h from 576 h to 520 h. Overall, it is shown that applying Bayes optimization allows for optimization of a multi-objective optimization function with several optimizable input variables and under a considerable amount of constraints with a low computational effort. This approach provides the potential to be used in the form of a decision tool, e.g., for the choice of an optimal and robust seed train design or for further optimization tasks within process development.}},
author = {{Hernández Rodriguez, Tanja and Sekulic, Anton and Lange-Hegermann, Markus and Frahm, Björn}},
booktitle = {{Bioprocess Systems Engineering Applications in Pharmaceutical Manufacturing}},
editor = {{Pörtner, Ralf and Möller, Johannes}},
isbn = {{978-3-0365-5210-1}},
issn = {{2227-9717}},
keywords = {{Gaussian processes, Bayes optimization, Pareto optimization, multi-objective, cell culture, seed train}},
pages = {{21--48}},
publisher = {{MDPI}},
title = {{{Designing robust biotechnological processes regarding variabilities using multi-objective optimization applied to a biopharmaceutical seed train design}}},
doi = {{https://doi.org/10.3390/pr10050883}},
volume = {{special issue}},
year = {{2022}},
}
@misc{12801,
abstract = {{The present contribution derives a theoretical framework for constructing novel geometrical constraints in the context of density-based topology optimization. Principally, the predefined geometrical dimensionality is enforced locally on the components of the optimized structures. These constraints are defined using the principal values (singular values) from a singular value decomposition of points clouds represented by elemental centroids and the corresponding relative density design variables. The proposed approach is numerically implemented for demonstrating the designing of lattice or membrane-like structures. Several numerical examples confirm the validity of the derived theoretical framework for geometric dimensionality control.}},
author = {{Gerzen, Nikolai and Mertins, Thorsten and Pedersen, Claus B. W.}},
booktitle = {{Structural and Multidisciplinary Optimization}},
issn = {{1615-147X}},
keywords = {{Manufacturing constraints, Topology optimization, Geometric constraints, Gradient based structural optimization, Lattice designing, Additive manufacturing}},
number = {{5}},
publisher = {{Springer Science and Business Media LLC}},
title = {{{Geometric dimensionality control of structural components in topology optimization}}},
doi = {{10.1007/s00158-022-03252-7}},
volume = {{65}},
year = {{2022}},
}
@misc{12814,
abstract = {{Plug-in hybrid electric vehicles (PHEVs) are developed to reduce fuel consumption and the emission of carbon dioxide. Common powertrain configurations of PHEVs (i.e., the configuration of the combustion engine, electric motor, and transmission) can be operated either in series, parallel, or power split hybrid mode, whereas powertrain configurations with multimode transmissions enable switching between those modes during vehicle operation. Hence, depending on the current operation state of the vehicle, the most appropriate mode in terms efficiency can be selected. This, however, requires an operating strategy, which controls the mode selection as well as the torque distribution between the combustion engine and electric motor with the aim of optimal battery depletion and minimal fuel consumption. A well-known approach is the equivalent consumption minimization strategy (ECMS). It can be applied by using optimizations based on a prediction of the future driving behavior. Since the outcome of the ECMS depends on the quality of this prediction, it is crucial to know how accurate the predictions must be in order to obtain acceptable results. In this contribution, various prediction methods and real-time capable ECMS implementations are analyzed and compared in terms of the achievable fuel economy. The basis for the analysis is a holistic model of a state-of-the-art PHEV powertrain configuration, comprising the multimode transmission, corresponding powertrain components, and representative real-world driving data.}},
author = {{Geng, Stefan and Schulte, Thomas and Maas, Jürgen}},
booktitle = {{Applied Sciences}},
issn = {{2076-3417}},
keywords = {{PHEV, ECMS, multimode transmission, optimization, powertrain modeling}},
number = {{6}},
publisher = {{MDPI AG}},
title = {{{Model-Based Analysis of Different Equivalent Consumption Minimization Strategies for a Plug-In Hybrid Electric Vehicle}}},
doi = {{10.3390/app12062905}},
volume = {{12}},
year = {{2022}},
}
@misc{12800,
abstract = {{his paper presents the cognitive module of the Cognitive Architecture for Artificial Intelligence (CAAI) in cyber-physical production systems (CPPS). The goal of this architecture is to reduce the implementation effort of artificial intelligence (AI) algorithms in CPPS. Declarative user goals and the provided algorithm-knowledge base allow the dynamic pipeline orchestration and configuration. A big data platform (BDP) instantiates the pipelines and monitors the CPPS performance for further evaluation through the cognitive module. Thus, the cognitive module is able to select feasible and robust configurations for process pipelines in varying use cases. Furthermore, it automatically adapts the models and algorithms based on model quality and resource consumption. The cognitive module also instantiates additional pipelines to evaluate algorithms from different classes on test functions. CAAI relies on well-defined interfaces to enable the integration of additional modules and reduce implementation effort. Finally, an implementation based on Docker, Kubernetes, and Kafka for the virtualization and orchestration of the individual modules and as messaging technology for module communication is used to evaluate a real-world use case.}},
author = {{Strohschein, Jan and Fischbach, Andreas and Bunte, Andreas and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas}},
booktitle = {{The International Journal of Advanced Manufacturing Technology}},
issn = {{1433-3015}},
keywords = {{Cognition, Industry 40, Big data platform, Machine learning, CPPS, Optimization, Algorithm selection, Simulation}},
number = {{11-12}},
pages = {{3513--3532}},
publisher = {{Springer }},
title = {{{Cognitive capabilities for the CAAI in cyber-physical production systems}}},
doi = {{10.1007/s00170-021-07248-3}},
volume = {{115}},
year = {{2021}},
}
@article{4518,
abstract = {{This paper introduces CAAI, a novel cognitive architecture for artificial intelligence in cyber-physical production systems. The goal of the architecture is to reduce the implementation effort for the usage of artificial intelligence algorithms. The core of the CAAI is a cognitive module that processes the user's declarative goals, selects suitable models and algorithms, and creates a configuration for the execution of a processing pipeline on a big data platform. Constant observation and evaluation against performance criteria assess the performance of pipelines for many and different use cases. Based on these evaluations, the pipelines are automatically adapted if necessary. The modular design with well-defined interfaces enables the reusability and extensibility of pipeline components. A big data platform implements this modular design supported by technologies such as Docker, Kubernetes, and Kafka for virtualization and orchestration of the individual components and their communication. The implementation of the architecture is evaluated using a real-world use case. The prototypic implementation is accessible on GitHub and contains a demonstration.}},
author = {{Fischbach, Andreas and Strohschein, Jan and Bunte, Andreas and Stork, Jörg and Faeskorn-Woyke, Heide and Moriz, Natalia and Bartz-Beielstein, Thomas}},
issn = {{1433-3015}},
journal = {{The International Journal of Advanced Manufacturing Technology}},
keywords = {{CPPS, Artificial intelligence, Industry 40, Reference architecture, Optimization, SMBO, Cognition, Big data platform, Modularization, AutoML}},
number = {{1/2}},
pages = {{609--626}},
publisher = {{Springer}},
title = {{{CAAI -- A Cognitive Architecture to Introduce Artificial Intelligence in Cyber-Physical Production Systems}}},
doi = {{10.1007/s00170-020-06094-z}},
volume = {{111}},
year = {{2020}},
}
@article{5435,
abstract = {{Towards renewable energy systems, the coupling of multiple sectors is important and incorporates novel technologies where currently no models exist that correctly represent all transient effects. Therefore, we present a method that incorporates Hardware-in-the-Loop simulations where virtual components as models are coupled to real and experimental facilities in real time. By including experimental components, a higher validity can be obtained and the practical applicability of renewable energy scenario can be discussed more profoundly. In this paper, the considered energy system consists of an experimental biocatalytic methanation reactor, a real photovoltaic park, a regenerative fuel cell and short-term storage units to supply a residential district. A representative control sequence of the methanator is obtained by modeling the scenario as an optimal control problem. A first HIL simulation highlights that modifications of the instrumentation are required for a grid injection of the generated methane. The scientific approach can be applied to any energy system where some of the considered components are available as experimental or real facilities. Non-exisiting components are simply replaced by models. The presented approach helps to determine which parts or process parameters are crucial for the planed operation before the overall energy system is realized on a larger scale. (C) 2019 Elsevier Ltd. All rights reserved.}},
author = {{Griese, Martin and Hoffrath, Marc Philippe and Broeker, Timo and Schulte, Thomas and Schneider, Jan}},
issn = {{1873-6785}},
journal = {{Energy : the international journal}},
keywords = {{Biological methanation, Energy management, HIL simulation, Optimization, Scalable models}},
location = {{Guimaraes, PORTUGAL}},
pages = {{77 -- 90}},
publisher = {{Elsevier}},
title = {{{Hardware-in-the-Loop simulation of an optimized energy management incorporating an experimental biocatalytic methanation reactor}}},
doi = {{10.1016/j.energy.2019.05.092}},
volume = {{181}},
year = {{2019}},
}
@inproceedings{577,
abstract = {{A rising number of product variants together with decreasing lot sizes are a result of the trend of individualization. Besides the upcoming organizational issues, changes in the production technologies are required. Direct digital manufacturing contributes to solve this problem by enabling the production of parts right from the CAD data.Process capability analysis is applied in several industries to prove the reliable compliance of products with quality requirements. As it is based on statistical methods, new challenges arise in the context of single-part production.The paper describes and compares different approaches for the adoption of process capability analysis for single-part production with special focus on additive manufacturing technologies. The statistical background and the applicability of different capability parameters are discussed. An overview of existing research work is given and supplemented by own approaches for the adoption of statistical methods for single-part production. The aim of the research work is to establish a first approach for the qualification of new technologies in single-part production.}},
author = {{Huxol, Andrea and Davis, Andrea and Villmer, Franz-Josef and Scheideler, Eva}},
booktitle = {{Production Engineering and Management}},
editor = {{Padoano, Elio and Villmer, Franz-Josef}},
isbn = {{978-3-946856-01-6}},
keywords = {{Statistical process control, Process capability analysis, Single-part production, Process optimization}},
location = {{Pordenone, Italy}},
number = {{1}},
pages = {{63--74}},
title = {{{Deployment of Process Capability Analysis for Single-Part Production}}},
year = {{2017}},
}
@inproceedings{579,
abstract = {{Selective Laser Melting (SLM) is a powder bed fusion process to produce additively metal parts. From the current point of view, it seems to be one of the most promising additive manufacturing technologies for the production of end use parts. An increasing number of examples prove the successful application of SLM for technical part production. Nevertheless, they also show the enormous effort that is still required to qualify the production process of every single part individually.The present paper gives an overview of the major influencing factors of the SLM process. To get a comprehensive research approach, existing publications on the topic are taken into account as well as own experimental work, evaluating the effects of the process parameters on the relative density of samples made from tool steel. The experimental setup and the results are described and opportunities for the further research work are discussed.}},
author = {{Huxol, Andrea and Scheideler, Eva and Villmer, Franz-Josef}},
booktitle = {{Production Engineering and Management}},
editor = {{Padoano, Elio and Villmer, Franz-Josef}},
isbn = {{978-3-946856-01-6}},
keywords = {{Selective laser melting, Additive manufacturing, Process parameters, Process optimization}},
location = {{Pordenone, Italy}},
number = {{1}},
pages = {{13--34}},
title = {{{Influencing Factors on Part Quality in Selective Laser Melting}}},
year = {{2017}},
}
@inproceedings{457,
abstract = {{Additive Manufacturing (AM) increasingly enables the realization of structures, which have a much greater freedom of design und can therefore better use nature as a design ideal. Bionic design principles have already been introduced into general design approaches, and several topology optimization systems (TO) are available today to increase structural stiffness and to enable lightweight design. AM and TO, used in synergy, promise completely new application areas. However, staircase effects resulting from a layer-by-layer build process and unavoidable support structures which must be mechanically removed afterwards are disadvantageous with respect to surface texture and strength properties.
The present article addresses the question of how far the notches resulting from the staircase effect of Additive Manufacturing and the support structures removed decrease the strength of components. Most engineers try to follow the inner flow of forces in a part’s design by smoothening surfaces in notched areas. Considering this, a elected component is investigated with finite element analysis (FEA) with special regard for the concentration of tress arising from surface notch effects. An outlook is given as regards how a reduction of the notch effect from the taircase effect can be achieved effectively.}},
author = {{Scheideler, Eva and Villmer, Franz-Josef and Adam, G. and Timmer, Mirco}},
booktitle = {{Production Engineering and Management Proceedings 6th International Conference}},
editor = {{Villmer, Franz-Josef and Padoano, Elio}},
isbn = {{978-3-946856-00-9}},
keywords = {{Additive Manufacturing, Topology optimization, Staircase effect, Support structures, Stress concentration, Lightweight construction, Design rules, Notch effect}},
location = {{Lemgo}},
number = {{1}},
pages = {{39--50}},
title = {{{Topology Optimization and Additive Manufacturing – A Perfect Symbiosis?}}},
year = {{2016}},
}
@inproceedings{598,
abstract = {{The aerospace sector is characterized by long product life cycles and a need for lightweight design. Additive manufacturing is a technology that produces parts layer by layer and thus enables the manufacturing of any complex parts at nearly no extra costs. A topology optimization enhances the part’s
performance for their special purpose. The results are often complex bionic structures that cannot be produced with conventional manufacturing technologies. The paper analyzes how the high potential of this technologycan be applied to aerospace parts. A topology optimization will be conducted for an aircraft part explaining the crucial points and a life cycle analysis examines the achieved sustainable improvements for the aircraft’s life cycle.
}},
author = {{Huxol, Andrea and Villmer, Franz-Josef}},
booktitle = {{Production Engineering and Management}},
editor = {{Padoano, Elio and Villmer, Franz-Josef}},
isbn = {{978-3-941645-11-0}},
keywords = {{Additive manufacturing, topology optimization, aerospace, life cycle costs}},
location = {{Trieste, Italy}},
number = {{1}},
pages = {{207--218}},
title = {{{Hybrid Manufacturing Machines: Combining Additive and Subtractive Manufacturing Technologies}}},
year = {{2015}},
}
@inbook{2394,
abstract = {{For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.}},
author = {{Frahm, Björn}},
booktitle = {{Animal Cell Biotechnology}},
isbn = {{9781627037327}},
issn = {{1064-3745}},
keywords = {{Seed train Optimization Modeling Prediction Space-Time-Yield (STY) Systems approach Bioinformatics Computational biotechnology Suspension Production}},
pages = {{355--367}},
publisher = {{Humana Press}},
title = {{{Seed Train Optimization for Cell Culture}}},
doi = {{10.1007/978-1-62703-733-4_22}},
volume = {{1104}},
year = {{2013}},
}
@inbook{10214,
abstract = {{For the production of biopharmaceuticals a seed train is required to generate an adequate number of cells for inoculation of the production bioreactor. This seed train is time- and cost-intensive but offers potential for optimization. A method and a protocol are described for the seed train mapping, directed modeling without major effort, and its optimization regarding selected optimization criteria such as optimal points in time for cell passaging. Furthermore, the method can also be applied for the set-up of a new seed train, for example for a new cell line. Although the chapter is directed towards suspension cell lines, the method is also generally applicable, e.g. for adherent cell lines.}},
author = {{Frahm, Björn}},
booktitle = {{Animal Cell Biotechnology - Methods and Protocols}},
editor = {{Pörtner, Ralf}},
isbn = {{978-1-62703-732-7}},
issn = {{1940-6029}},
keywords = {{Seed train, Optimization, Modeling, Prediction, Space-Time-Yield (STY), Systems approach, Bioinformatics, Computational biotechnology, Suspension, Production}},
pages = {{355–367}},
publisher = {{Humana Press}},
title = {{{Seed Train Optimization for Cell Culture}}},
doi = {{10.1007/978-1-62703-733-4_22}},
volume = {{1104}},
year = {{2013}},
}