@inproceedings{11166,
  abstract     = {{n order to utilize the full potential of an assembly assistant, it is necessary for the system to be able to adapt to the worker’s individual needs and capabilities. The required amount of necessary assistance changes during the assembly work because the worker memorizes the steps and learns the task and might be bothered by unnecessary assistance. Finding the point in time where the learning phase of the worker is finished is therefore an important aspect. In this work, we propose and evaluate a novel method to identify the end of the worker’s learning phase. The method makes use of learning curve models and curve fitting in order to determine the progress of the worker.}},
  author       = {{Sehr, Philip and Moriz, Natalia and Heinz-Jakobs, Mario and Trsek, Henning}},
  booktitle    = {{2022 27th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{9781665499965}},
  location     = {{Stuttgart}},
  publisher    = {{IEEE}},
  title        = {{{Am I Done Learning? - Determining Learning States in Adaptive Assembly Systems}}},
  doi          = {{10.1109/ETFA52439.2022.9921676}},
  year         = {{2022}},
}

@inproceedings{11161,
  author       = {{Mankowski, Andre and Antonow, Daniel and Moriz, Natalia and Trsek, Henning}},
  booktitle    = {{ Automation 2022}},
  isbn         = {{978-3-18-092399-4 }},
  location     = {{Baden-Baden}},
  number       = {{2399}},
  pages        = {{219--230}},
  publisher    = {{VDI Verlag GmbH }},
  title        = {{{Kognitive Automations-Architektur für Cyber-Physische-Produktionssysteme (CPPS)}}},
  year         = {{2022}},
}

@article{6839,
  abstract     = {{Pasteurization is a crucial processing method in the food industry to ensure the safety of consumables. A major part of contemporary pasteurization processes involves using flash pasteurizer systems, where liquids are pumped through a pipe system to heat them for a predefined time. Accurately monitoring the amount of heat treatment applied to a product is challenging. This monitoring helps ensure that the correct heat impact (expressed in pasteurization units) is applied, which is commonly calculated as a product of time and temperature, taking achievability of the inactivation of the microorganisms into account. The state-of-the-art method involves a calculation of the applied pasteurization units using a one-point temperature measurement and the holding time for this temperature. Concerns about accuracy lead to high safety margins, reducing the quality of the pasteurized product. In this study, the applied pasteurization level was estimated using regression models trained with NIR spectroscopy data collected while pasteurizing fruit juices of different types and brands. Several conventional regression models were trained in combination with different preprocessing methods, including a novel prediction outlier detection method. Generalized juice models trained with the concatenated data of all types of juices demonstrated cross-validated scores of RMSECV ∼2.78 ± 0.09 and r<jats:sup>2</jats:sup> 0.96 ± 0.01, while separate juice models displayed averaged cross-validated scores of RMSECV ∼1.56 ± 0.04 and r<jats:sup>2</jats:sup> 0.98 ± 0.01. Thus, the model accuracy ±10–30 % is well within the standard safety margins. }},
  author       = {{Sürmeli, Baris Gün and Weishaupt, Imke and Schwarzer, Knut and Moriz, Natalia and Schneider, Jan}},
  issn         = {{1751-6552}},
  journal      = {{Journal of Near Infrared Spectroscopy}},
  keywords     = {{Beverage pasteurization, heat impact control, prediction outlier elimination}},
  number       = {{6}},
  pages        = {{339--351}},
  publisher    = {{Sage Publishing}},
  title        = {{{Heat impact control in flash pasteurization by estimation of applied pasteurization units using near infrared spectroscopy}}},
  doi          = {{10.1177/09670335211057233}},
  volume       = {{29}},
  year         = {{2021}},
}

@misc{11155,
  author       = {{Mankowski, Andre and Priss, Philip and Suton, Jana and Moriz, Natalia and Trsek, Henning}},
  booktitle    = {{Produktion : Technik und Wirtschaft für die deutsche Industrie }},
  issn         = {{0344-6166}},
  number       = {{16}},
  pages        = {{19}},
  publisher    = {{Verl. Moderne Industrie}},
  title        = {{{Projekt DEVEKOS nach vier Jahren erfolgreich abgeschlossen : Neue objektbasierte I4.0-Maschinenarchitektur}}},
  year         = {{2021}},
}

@inproceedings{11157,
  abstract     = {{In production environments, manual assembly is often used, when there is a high demand for flexibility which needs to be met economically. In this circumstance, assembly assistance systems are often used to ensure production standards. However, the individual requirements of each worker call for a way for the system to adapt towards the workers needs. This often requires modelling and configuration effort to include expert knowledge into an assistance system. This hinders an economical operation in an industrial environment. In this paper, an approach is presented, facilitating methods of online modelling, to generate a model, which represents the workers behavior during an assembly process. This behavior model is then used to deduce suitable adaptive assembly assistance in real time.}},
  author       = {{Sehr, Philip and Moriz, Natalia and Heinz-Jakobs, Mario and Trsek, Henning}},
  booktitle    = {{2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  isbn         = {{978-1-7281-2990-7}},
  location     = {{Vasteras, Sweden }},
  publisher    = {{IEEE}},
  title        = {{{Model-based approach for adaptive assembly assistance}}},
  doi          = {{10.1109/ETFA45728.2021.9613614}},
  year         = {{2021}},
}

@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}},
}

@inproceedings{4512,
  author       = {{Geng, Christoph and Moriz, Natalia and Bunte, Andreas and Trsek, Henning}},
  booktitle    = {{KommA 2020 – Jahreskolloquium Kommunikation in der Automation}},
  location     = {{Lemgo}},
  title        = {{{Concept for Rule-Based Information Aggregation in Modular Production Plants}}},
  year         = {{2020}},
}

@inproceedings{4513,
  author       = {{Bunte, Andreas and Ressler, Henrik and Moriz, Natalia}},
  booktitle    = {{25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) }},
  location     = {{Wien}},
  title        = {{{Automated Detection of Production Cycles in Production Plants using Machine Learning}}},
  year         = {{2020}},
}

@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}},
}

@inbook{4797,
  abstract     = {{Die Funktion der Montage wird im Zeitverlauf immer wieder grundlegend verändert, indem neue Gestaltungsparadigmen umgesetzt werden. Damit es zu solchen Paradigmenwechseln kommt, bedarf es neben veränderten Marktanforderungen zwingend neuer Erkenntnisse. Diese können sich dabei auf den Einsatz einzelner Technologien, die Anwendung neuer Organisationsprinzipien oder eine veränderte Rolle der Beschäftigten in der Montage beziehen. Die zunehmende Individualisierung der Erzeugnisgestaltung und sich verkürzende Innovationslebenszyklen führen aktuell zu einem Anstieg der Komplexität in der Montage. Damit nehmen die zu verarbeitenden Informationsmengen deutlich zu und es stellt sich die Frage, wie mit diesen großen Mengen an Informationen umzugehen ist. Dabei kommen dem Einsatz von Informations- und Kommunikationstechnologien eine Schlüsselrolle in der Montagesystemgestaltung zu. Wie diese Rolle und mit ihr verbundene Entwicklungen aussehen, wird in fünf Handlungsfeldern beschrieben. Zum Handlungsfeld der Montageassistenzsysteme, das im Mittelpunkt dieses Buches steht, werden zudem einzelne Trends dargestellt. Abschließend werden Hinweise für die betriebliche Praxis formuliert.}},
  author       = {{Hinrichsen, Sven and Moriz, Natalia and Bornewasser, Manfred}},
  booktitle    = {{Informatorische Assistenzsysteme in der variantenreichen Montage: Theorie und Praxis}},
  editor       = {{Bornewasser, Manfred  and Hinrichsen, Sven}},
  isbn         = {{978-3-662-61373-3}},
  pages        = {{187--213}},
  publisher    = {{Springer Vieweg}},
  title        = {{{Entwicklungstrends in der Montage}}},
  doi          = {{https://doi.org/10.1007/978-3-662-61374-0_10}},
  year         = {{2020}},
}

@inproceedings{4780,
  author       = {{Bunte, Andreas and Wunderlich, Paul and Moriz, Natalia and Li, Peng and Mankowski, Andre and Rogalla, Antje and Niggemann, Oliver}},
  booktitle    = {{24nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Why Symbolic AI is a Key Technology for Self-Adaption in the Context of CPPS}}},
  year         = {{2019}},
}

@inproceedings{4800,
  author       = {{Sehr, Philip and Moriz, Natalia}},
  booktitle    = {{Proceedings 2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Partitioning Manual Assembly Workstations for Flexible Manufacturing}}},
  year         = {{2019}},
}

@inproceedings{4801,
  abstract     = {{Die manuelle Montage ist in einzelnen Branchen des Verarbeitenden Gewerbes von hoher Bedeutung. Industrielle Trends nach individualisierten Produkten in geringen Losgrößen steigern dabei die Komplexität in der manuellen Montage. In der Folge kann es zu einer erhöhten mentalen Beanspruchung der Beschäftigten kommen, wodurch Montagefehler häufiger auftreten und die Produktivität sinkt. Der vorliegende Beitrag präsentiert zwei Anwendungsfälle, in denen verschiedene Ansätze gezeigt werden, diesem Effekt entgegen zu wirken. Im ersten Anwendungsfall wird ein informatorisches Assistenzsystem eingesetzt, um Montageinformationen besser an die Bedürfnisse der Mitarbeiter anzupassen. Im zweiten Anwendungsfall wird ein bestehendes Montagesystem modularisiert. Die Anordnung der Bauteile in den einzelnen Modulen wird algorithmisch so optimiert, dass Laufwege zwischen den Modulen minimiert werden.}},
  author       = {{Sehr, Philip and Moriz, Natalia and Bendzioch, Sven and Hinrichsen, Sven}},
  booktitle    = {{AUTOMATION 2019: Autonomous Systems and 5G in Connected Industries}},
  pages        = {{671 -- 682}},
  publisher    = {{VDI}},
  title        = {{{Ansätze für Komplexitätsreduktion in manuellen Montageprozessen}}},
  year         = {{2019}},
}

@inproceedings{4802,
  author       = {{Böttcher, Björn and Moriz, Natalia and Niggemann, Oliver}},
  booktitle    = {{Automation 2015}},
  publisher    = {{VDI-Verlag}},
  title        = {{{Intelligente Entwurfsassistenz für Automatisierungssysteme - Vorteile deklarativer Paradigmen im Systementwurf}}},
  year         = {{2015}},
}

@inproceedings{4803,
  author       = {{Moriz, Natalia and Böttcher, Björn and Niggemann, Oliver and Lackhove, Josef}},
  booktitle    = {{19th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Assisted Design for Automation Systems - from Formal Requirements to Final Designs}}},
  year         = {{2014}},
}

@inproceedings{4804,
  author       = {{Böttcher, Björn and Moriz, Natalia and Niggemann, Oliver}},
  booktitle    = {{21st European Conference on Artificial Intelligence Frontiers in Artificial Intelligence and Applications, 2014}},
  editor       = {{Schaub, T., Friedrich, G. O'Sullivan, B.}},
  pages        = {{pp. 977--978}},
  publisher    = {{Vol. 263}},
  title        = {{{From Formal Requirements on Technical Systems to Complete Designs - A Holistic Approach}}},
  year         = {{2014}},
}

@inproceedings{4805,
  author       = {{Böttcher, Björn and Badinger, Johann and Moriz, Natalia and Niggemann, Oliver}},
  booktitle    = {{18thIEEE International Conference on Emerging Technologies and Factory Automation (ETFA)}},
  title        = {{{Design of Industrial Automation Systems - Formal Requirements in the Engineering Process}}},
  year         = {{2013}},
}

@inproceedings{4806,
  author       = {{Schetinin, Nikolai and Moriz, Natalia and Kumar, Barath and Faltinski, Sebastian and Niggemann, Oliver and Maier, Alexander}},
  booktitle    = {{International Conference on Industrial Technology (ICIT) 25.-27. February 2013, Cape Town, South Africa, }},
  title        = {{{Why do verification approaches in automation rarely use HIL-test?}}},
  year         = {{2013}},
}

@inproceedings{4807,
  author       = {{Faltinski, Sebastian and Moriz, Natalia and Schetinin, Nikolai and Niggemann, Oliver}},
  booktitle    = {{AUTOMATION 2012}},
  title        = {{{AutomationML als Grundlage für einen durchgängigen Modellierung-, Simulations- und Integrationsprozess in der Anlageplanung. }}},
  year         = {{2012}},
}

@inproceedings{4808,
  author       = {{Faltinski, Sebastian and Niggemann, Oliver and Moriz, Natalia and Mankowski, Andre}},
  booktitle    = {{2012 IEEE International Conference on Industrial Technology (ICIT)}},
  title        = {{{AutomationML: From Data Exchange to System Planning and Simulation}}},
  year         = {{2012}},
}

@inproceedings{4809,
  author       = {{Gräser, Olaf and Kumar, Barath and Moriz, Natalia and Maier, Alexander and Niggemann, Oliver}},
  title        = {{{AutomationML as a Basis for Offline- and Realtime-Simulation}}},
  year         = {{2011}},
}

@inproceedings{4810,
  author       = {{Moriz, Natalia and Faltinski, Sebastian and Gräser, Olaf and Niggemann, Oliver and Barth, Mike and Fay, Alexander}},
  booktitle    = {{VDI Kongress AUTOMATION 2011 }},
  publisher    = {{Baden Baden}},
  title        = {{{Integration und Anwendung von objektorientierten Simulationsmodellen in AutomationML}}},
  year         = {{2011}},
}