@misc{13338, abstract = {{This work discusses the additive manufacturing of an axicon lens using cyclic olefin copolymer (TOPAS), and its characterization between 100 GHz and 300 GHz. The proposed manufacturing process followed by dip-coating post-processing provides an improved surface finish. Additionally, the terahertz output of the lens remains intact over the entire frequency range.}}, author = {{Shrotri, Abhijeet Narendra and Joshi, Suraj and Vogel, Lea and Starsaja, Annamarija and Stübbe, Oliver and Preu, Sascha}}, booktitle = {{2025 50th International Conference on Infrared, Millimeter, and Terahertz Waves (IRMMW-THz)}}, keywords = {{Manufacturing processes, Surface waves, Three-dimensional printing, Surface finishing, Surface treatment, Lenses}}, location = {{ Helsinki, Finland }}, pages = {{2}}, publisher = {{IEEE}}, title = {{{Terahertz Axicon Lenses}}}, doi = {{10.1109/irmmw-thz61557.2025.11319870}}, year = {{2026}}, } @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}}, } @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}}, } @article{6932, abstract = {{n order to ensure the safety and security of industrial systems with regard to all life cycle phases from development through operation to disposal, specific regulatory and normative requirements are imposed. Due to the digitalization, interconnection, and constantly increasing complexity of manufacturing systems in the context of Industrie 4.0, the manual effort necessary to achieve the required safety and security is becoming ever greater and almost impossible to manage, especially for small and medium-sized enterprises. Therefore, this paper examines the existing challenges in this area in more detail and gives an outlook on the possible solutions to ensure safety and security much quicker and with less manual effort. The overall vision is a (partially) automated risk assessment of modular systems with respect to safety and security, including the alignment of the corresponding processes from both domains and the formalization of the information models needed.}}, author = {{Ehrlich, Marco and Bröring, Andre and Harder, Dimitri and Auhagen-Meyer, Torben and Kleen, Philip and Wisniewski, Lukasz and Trsek, Henning and Jasperneite, Jürgen}}, issn = {{1613-7620}}, journal = {{Elektrotechnik und Informationstechnik : e & i}}, keywords = {{safety, security, alignment, automation, processes, models}}, number = {{6}}, pages = {{454--461}}, publisher = {{Springer}}, title = {{{Alignment of safety and security risk assessments for modular production systems}}}, doi = {{10.1007/s00502-021-00927-9}}, volume = {{138}}, year = {{2021}}, } @misc{12235, abstract = {{Metalimnetic oxygen minima are observed in many lakes and reservoirs, but the mechanisms behind this phenomena are not well understood. Thus, we simulated the metalimnetic oxygen minimum (MOM) in the Rappbode Reservoir with a well-established two-dimensional water quality model (CE-QUAL-W2) to systematically quantify the chain of events leading to its formation. We used high-resolution measured data to calibrate the model, which accurately reproduced the physical (e.g. water level and water temperature), biogeochemical (e.g. nutrient and oxygen dynamics) and ecological (e.g. algal community dynamics) features of the reservoir, particularly the spatial and temporal extent of the MOM. The results indicated that around 60% of the total oxygen consumption rate in the MOM layer originated from benthic processes whereas the remainder originated from pelagic processes. The occurrence of the cyanobacterium Planktothrix rubescens in the metalimnion delayed and slightly weakened the MOM through photosynthesis, although its decaying biomass ultimately induced the MOM. Our research also confirmed the decisive role of water temperature in the formation of the MOM since the water temperatures, and thus benthic and pelagic oxygen consumption rates, were higher in the metalimnion than in the hypolimnion. Our model is not only providing novel conclusions about the drivers of MOM development and their quantitative contributions, it is also a new tool for understanding and predicting ecological and biogeochemical water quality dynamics.}}, author = {{Mi, Chenxi and Shatwell, Tom and Ma, Jun and Wentzky, Valerie Carolin and Boehrer, Bertram and Xu, Yaqian and Rinke, Karsten}}, booktitle = {{Water research : a journal of the International Water Association}}, issn = {{1879-2448}}, keywords = {{Rappbode reservoir, CE-QUAL-W2, Planktothrix rubescens, Metalimnion, Oxygen consumption, Benthic processes}}, number = {{5}}, publisher = {{Elsevier BV}}, title = {{{The formation of a metalimnetic oxygen minimum exemplifies how ecosystem dynamics shape biogeochemical processes: A modelling study}}}, doi = {{10.1016/j.watres.2020.115701}}, volume = {{175}}, year = {{2020}}, } @misc{12812, abstract = {{Discerning unexpected from expected data patterns is the key challenge of anomaly detection. Although a multitude of solutions has been applied to this modern Industry 4.0 problem, it remains an open research issue to identify the key characteristics subjacent to an anomaly, sc. generate hypothesis as to why they appear. In recent years, machine learning models have been regarded as universal solution for a wide range of problems. While most of them suffer from non-self-explanatory representations, Gaussian Processes (GPs) deliver interpretable and robust statistical data models, which are able to cope with unreliable, noisy, or partially missing data. Thus, we regard them as a suitable solution for detecting and appropriately representing anomalies and their respective characteristics. In this position paper, we discuss the problem of automatic and interpretable anomaly detection by means of GPs. That is, we elaborate on why GPs are well suited for anomaly detection and what the current challenges are when applying these probabilistic models to large-scale production data.}}, author = {{Berns, Fabian and Lange-Hegermann, Markus and Beecks, Christian}}, booktitle = {{ Proceedings of the International Conference on Innovative Intelligent Industrial Production and Logistics IN4PL - Volume 1}}, editor = {{Panetto, H. and Madani, K. and Smirnov, A.}}, isbn = {{978-989-758-476-3}}, keywords = {{Anomaly Detection, Gaussian Processes, Explainable Machine Learning, Industry 4.0}}, location = {{Budapest, HUNGARY}}, pages = {{87--92}}, publisher = {{SCITEPRESS - Science and Technology Publications}}, title = {{{Towards Gaussian Processes for Automatic and Interpretable Anomaly Detection in Industry 4.0}}}, doi = {{10.5220/0010130300870092}}, year = {{2020}}, } @misc{549, author = {{Springer, André and Nothdurft, Sarah and Lahdo, Rabi and Seffer, Oliver}}, booktitle = {{8th International Conference on Production Engineering and Management}}, isbn = {{978-3-946856-03-0}}, keywords = {{Dissimilar metal joints, Laser processes, Multi-material components}}, location = {{Lemgo}}, publisher = {{Technische Hochschule Ostwestfalen-Lippe}}, title = {{{Dissimilar Metal Joints - Laser Based Manufacturing Processes for Components of Tomorrow}}}, year = {{2018}}, } @inproceedings{4255, abstract = {{Increasingly, production processes are enabled and controlled by Information Technology (IT), a development being also referred to as “Industry 4.0”. IT thereby contributes to flexible and adaptive production processes, and in this sense factories become “smart factories”. In line with this, IT also more and more supports human workers via various assistance systems. This support aims to both support workers to better execute their tasks and to reduce the effort and time required when working. However, due to the large spectrum of assistance systems, it is hard to acquire an overview and to select an adequate system for a smart factory based on meaningful criteria. We therefore synthesize a set of comparison criteria into a consistent framework and demonstrate the application of our framework by classifying three examples.}}, author = {{Fellmann, Michael and Robert, Sebastian and Büttner, Sebastian and Mucha, Henrik and Röcker, Carsten}}, booktitle = {{ Machine Learning and Knowledge Extraction : First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy, August 29 – September 1, 2017, Proceedings}}, editor = {{Holzinger, Andreas}}, isbn = {{978-3-319-66807-9}}, keywords = {{Assistance systems, Smart factory, Production processes}}, location = {{Reggio, Italy}}, pages = {{59--68}}, publisher = {{Springer}}, title = {{{Towards a Framework for Assistance Systems to Support Work Processes in Smart Factories}}}, doi = {{10.1007/978-3-319-66808-6_5}}, volume = {{10410}}, year = {{2017}}, } @inproceedings{4330, abstract = {{Catchwords such as “Cyber-Physical-Systems” and “Industry 4.0” describe the current development of systems with embedded intelligence. These systems can be characterized by an increasing technical complexity that must be addressed in the user interface. In this paper we analyze the specific requirements posed by the interaction with cyber-physical-systems, present a coordinated approach to these requirements and illustrate our approach with a practical example of an assistance system for assembly workers in an industrial production environment.}}, author = {{Paelke, Volker and Röcker, Carsten}}, booktitle = {{Design, User Experience, and Usability: Design Discourse}}, isbn = {{978-3-319-20885-5}}, keywords = {{Industrial IT, User-Centered design, Usability, User interfaces, Cyber-Physical-Systems, Industry 4.0, Augmented reality, Development processes and methods}}, location = {{Los Angeles, CA, USA}}, pages = {{75--85 }}, publisher = {{Springer}}, title = {{{User Interfaces for Cyber-Physical Systems: Challenges and Possible Approaches. }}}, doi = {{10.1007/978-3-319-20886-2_8}}, volume = {{9186}}, year = {{2015}}, } @misc{10164, abstract = {{Cognitive radios (CR) can sense and detect temporarily available spectral holes for an opportunistic operation to improve the spectral efficiency and coexistence of industrial radio systems. It will be of particular interest for a CR system to apply predictive modeling in order to forecast the behavior of the coexisting environment. A secondary cognitive user shall use preemptive tuning of its operating parameters following the predictive model. However, a considerable challenge is to generate an accurate model and predict efficiently in order to meet strict time related requirements of industrial applications. Such predictive modeling has already gained some attention but real-time experimental results have never been reported to the best of our knowledge. In this contribution we investigate the performance of a Markov model based CR system using simulative and experimental environments for its application in industrial systems.}}, author = {{Ahmad, Kaleem and Meier, Uwe and Witte, Stefan}}, booktitle = {{Proceedings of 2012 IEEE 17th International Conference on Emerging Technologies & Factory Automation (ETFA 2012)}}, isbn = {{978-1-4673-4735-8 }}, keywords = {{cognitive radio, Markov processes, radio spectrum management}}, location = {{Krakow, Poland }}, publisher = {{IEEE}}, title = {{{Predictive Oppertunistic Spectrum Access Using Markov Models}}}, doi = {{10.1109/ETFA.2012.6489557}}, year = {{2012}}, }