@book{45191,
  editor       = {{Gräßler, Iris and Maier, Günter W. and Steffen, Eckhard and Roesmann, Daniel}},
  isbn         = {{9783031261039}},
  publisher    = {{Springer International Publishing}},
  title        = {{{The Digital Twin of Humans}}},
  doi          = {{10.1007/978-3-031-26104-6}},
  year         = {{2023}},
}

@inbook{45187,
  author       = {{Pöhler, Alexander and Gräßler, Iris}},
  booktitle    = {{The Digital Twin of Humans}},
  isbn         = {{9783031261039}},
  pages        = {{171--185}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Individual Assembly Guidance}}},
  doi          = {{10.1007/978-3-031-26104-6_8}},
  year         = {{2023}},
}

@inbook{45111,
  author       = {{Roesmann, Daniel and Gräßler, Iris}},
  booktitle    = {{The Digital Twin of Humans}},
  isbn         = {{9783031261039}},
  pages        = {{187–203}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Integration of Human Factors for Assembly Systems of the Future}}},
  doi          = {{10.1007/978-3-031-26104-6_9}},
  year         = {{2023}},
}

@inproceedings{46488,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>New trends and technologies in product creation increase complexity, but at the same time create new potentials such as efficiency rise in task processing by Artificial Intelligence. Established models in the early phase of product creation such as the W-model or the Aachener Innovation Management model, do not fully exploit these new potentials in the field of strategic product planning and innovation management (SPPIM). For this reason, existing models are analysed in SPPIM in order to derive a requirements profile consisting of potentials and goals for a new model. A new model in SPPIM lays the foundation to support companies in enabling a more efficient task fulfilment by taking advantage of new technologies and trends. To guide the development of advanced SPPIM models, the derived potentials and goals are applied to the guideline VDI 2220:1980.</jats:p>}},
  author       = {{Gräßler, Iris and Koch, Anna-Sophie and Tusek, Alena Marie}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  pages        = {{2915--2924}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{POTENTIALS AND GOALS OF MODELS IN STRATEGIC PRODUCT PLANNING AND INNOVATION MANAGEMENT}}},
  doi          = {{10.1017/pds.2023.292}},
  volume       = {{3}},
  year         = {{2023}},
}

@inbook{46796,
  author       = {{Hesse, Philipp and Gräßler, Iris}},
  booktitle    = {{Climate Protection, Resource Efficiency, and Sustainable Engineering: Transdisciplinary Approaches to Design and Manufacturing technology.}},
  editor       = {{Horwath, Ilona and Schweizer, Swetlana}},
  pages        = {{128--138}},
  publisher    = {{transcript}},
  title        = {{{Interdependency study of design guidelines}}},
  year         = {{2023}},
}

@inbook{46792,
  author       = {{Hesse, Philipp and Gräßler, Iris}},
  booktitle    = {{Climate Protection, Resource Efficiency, and Sustainable Engineering: Transdisciplinary Approaches to Design and Manufacturing technology.}},
  editor       = {{Horwath, Ilona and Schweizer, Swetlana}},
  pages        = {{89--98}},
  publisher    = {{transcript}},
  title        = {{{Sustainable product life cycle}}},
  year         = {{2023}},
}

@techreport{46501,
  author       = {{Gräßler, Iris and Ovtcharova, Jivka  and Dattner, Michael  and Dietert, Tilko  and Dietz, Patrick  and Elstermann, Matthes  and Fayet, Celestin  and Hauck, Andreas  and Häuser, Frank  and Fischer, Holger  and Herzog, Michael  and Köhler, Christian  and Lachenmaier, Jens  and Lachmayer, Roland  and Meussen, Bernhard  and Mozgova, Iryna  and Möser, Sebastian  and Pottebaum, Jens and Schluse, Michael  and Schneider, Jannik  and Stetter, Ralf  and Thurnes, Christian  and Tusek, Alena Marie and Wurst, Johanna }},
  title        = {{{Begriffe der strategischen Produktplanung und -entwicklung. Produkt und hybride Leistung}}},
  year         = {{2023}},
}

@inproceedings{34395,
  author       = {{Gräßler, Iris and Hieb, Michael and Roesmann, Daniel and Unverzagt, Marc}},
  editor       = {{Lohweg, Volker}},
  pages        = {{95--106}},
  publisher    = {{Springer Vieweg}},
  title        = {{{Creating Synthetic Training Data for Machine Vision Quality Gates}}},
  doi          = {{10.1007/978-3-662-66769-9_7 }},
  year         = {{2023}},
}

@inproceedings{46974,
  author       = {{Gräßler, Iris and Hieb, Michael}},
  publisher    = {{CIRP }},
  title        = {{{Creating Synthetic Datasets for Deep Learning used in Machine Vision}}},
  year         = {{2023}},
}

@inproceedings{46219,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>To select design guidelines engineers have to identify relevant from a bewildering amount of design guidelines. In this paper, a rule-based method for selecting design guidelines for material circularity selection is presented. For this purpose, a generic Product Life Cycle model is detailed with regard to Multi Material cycles (gPLC-MM). The presented method is divided into four steps. Core of the presented method is the comparison of circular product strategies with product life phases and material recovery processes. Engineering activities and increments of the product architecture are used to identify design guidelines. The results show that through the material circularity-oriented design guideline identification method, the product architecture is designed for different processes and technologies, to recover materials. The method allows engineers to select guidelines in a more targeted and consolidated way in sustainability-friendly product engineering.</jats:p>}},
  author       = {{Gräßler, Iris and Hesse, Philipp}},
  booktitle    = {{Proceedings of the Design Society}},
  issn         = {{2732-527X}},
  keywords     = {{Sustainability, Circular economy, Conceptual design}},
  location     = {{Bordeaux, France}},
  pages        = {{1077--1086}},
  publisher    = {{Cambridge University Press (CUP)}},
  title        = {{{CONSIDERING ENGINEERING ACTIVITIES AND PRODUCT CHARACTERISTICS TO ACHIEVE MATERIAL CIRCULARITY BY DESIGN}}},
  doi          = {{10.1017/pds.2023.108}},
  volume       = {{3}},
  year         = {{2023}},
}

@inproceedings{52839,
  author       = {{Gräßler, Iris and Hieb, Michael and Roesmann, Daniel and Unverzagt, Marc and Pottebaum, Jens}},
  booktitle    = {{SSRN Electronic Journal}},
  issn         = {{1556-5068}},
  keywords     = {{General Earth and Planetary Sciences, General Environmental Science}},
  publisher    = {{Elsevier BV}},
  title        = {{{Virtual learning environment for teaching the handling of collaborative robots}}},
  doi          = {{10.2139/ssrn.4471596}},
  year         = {{2023}},
}

@inproceedings{46973,
  author       = {{Gräßler, Iris and Hieb, Michael}},
  booktitle    = {{Automation 2023}},
  pages        = {{765--776}},
  publisher    = {{VDI Verlag }},
  title        = {{{Cloud-Computing für die Verwendung synthetischer Trainingsdaten für Machine Vision Quality Gates}}},
  doi          = {{10.51202/9783181024195-765}},
  volume       = {{2419}},
  year         = {{2023}},
}

@inproceedings{52816,
  abstract     = {{Manufacturing companies face the challenge of reaching required quality standards. Using
optical sensors and deep learning might help. However, training deep learning algorithms
require large amounts of visual training data. Using domain randomization to generate synthetic
image data can alleviate this bottleneck. This paper presents the application of synthetic
image training data for optical quality inspections using visual sensor technology. The results
show synthetically generated training data are appropriate for visual quality inspections.}},
  author       = {{Gräßler, Iris and Hieb, Michael}},
  booktitle    = {{Lectures}},
  keywords     = {{synthetic training data, machine vision quality gates, deep learning, automated inspection and quality control, production control}},
  location     = {{Nuremberg}},
  pages        = {{253--524}},
  publisher    = {{AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany}},
  title        = {{{Creating Synthetic Training Datasets for Inspection in Machine Vision Quality Gates in Manufacturing}}},
  doi          = {{10.5162/smsi2023/d7.4}},
  year         = {{2023}},
}

@inproceedings{46450,
  author       = {{Gräßler, Iris and Preuß, Daniel and Brandt, Lukas and Mohr, Michael}},
  booktitle    = {{Proceedings of the Design Society}},
  location     = {{Bordeaux}},
  pages        = {{1595--1604}},
  title        = {{{Efficient Formalisation of Technical Requirements for Generative Engineering}}},
  doi          = {{10.1017/pds.2023.160}},
  year         = {{2023}},
}

@inproceedings{52832,
  author       = {{Weller, Julian and Roesmann, Daniel and Eggert, Sönke and von Enzberg, Sebastian and Gräßler, Iris and Dumitrescu, Roman}},
  booktitle    = {{Procedia CIRP}},
  issn         = {{2212-8271}},
  keywords     = {{General Medicine}},
  pages        = {{514--520}},
  publisher    = {{Elsevier BV}},
  title        = {{{Identification and prediction of standard times in machining for precision steel tubes through the usage of data analytics}}},
  doi          = {{10.1016/j.procir.2023.01.011}},
  volume       = {{119}},
  year         = {{2023}},
}

@article{44215,
  abstract     = {{In der zukünftigen Produktion werden der Aufbau und die Entwicklung der Fähigkeiten der Mitarbeiter:innen ein entscheidender Wettbewerbsvorteil von Unternehmen. In menschenzentrierten Montagesystemen passen sich die Mitarbeiter:innen auf der Grundlage von Lernprozessen an neue und sich ändernde Aufgaben an. Dazu muss der Bezug zu den Fähigkeiten der Mitarbeiter:innen im Zuge der integrierten Produkt-und Prozessentwicklung vorgesehen werden. Daher wurde eine Methodik entwickelt, die diese Fähigkeiten explizit abbildet und sie sowohl in der Entwicklung als auch in der kurzfristigen Personaleinsatzplanung bei der Aufgabenzuordnung berücksichtigt. Zur Anwendung wurde die Methodik prototypisch in einem Software-Werkzeug umgesetzt und in Kombination mit einer diskreten ereignisorientierten Simulation erprobt.}},
  author       = {{Gräßler, Iris and Roesmann, Daniel and Pottebaum, Jens}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  keywords     = {{Management Science and Operations Research, Strategy and Management, General Engineering}},
  number       = {{3}},
  pages        = {{149--152}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Human Factors in der integrierten Produktentwicklung}}},
  doi          = {{10.1515/zwf-2023-1029}},
  volume       = {{118}},
  year         = {{2023}},
}

@inproceedings{46988,
  abstract     = {{Extremwettersituationen sind durch die Kombination von globalen und lokalen Wirkzusammenhän-gen gekennzeichnet. In der Gefahrenanalyse und -reaktion ist deshalb der Umgang mit extremen Daten erforderlich, die von heterogenen Datenquellen bezogen und mittels unterschiedlicher Ver-fahren bis hin zum maschinellen Lernen ausgewertet werden. Die Visualisierung dieser zwangsläufig unsicherheitsbehafteten Daten stellt eine Herausforderung dar. Diese wirkt umso bedeutsamer, je weniger Fachexpertise in Bereichen wie Meteorologie, Geologie oder Sensortechnik in einer Füh-rungs- oder Leitstelle eingebunden werden kann. Das Management kritischer Situationen in Echtzeit bei extremen und komplexen Daten muss daher auf einer Bewertung der Informationsqualität von extremen Daten beruhen. Diese Bewertung ist abhängig vom Anwendungskontext in unterschiedli-chen Führungs- und Assistenzstellen sowie der verfügbaren Infrastruktur mit Geräten zur Visualisie-rung, Schnittstellen von Wetterdiensten, Sensorsysteme und Rettungsrobotik. Im Beitrag wird der Ansatz des EU-Projekts CREXDATA in Bezug auf mögliche pluviale Hochwassersituationen in Inns-bruck vorgestellt. Grundlage bildet die Kategorisierung von extremen Daten, die Schnittstellen zu Datenquellen mit globalem und lokalem Bezug sowie Anwendungsfälle für die Visualisierung von Informationen. Es werden somit Grundlagen präsentiert, die in allen Formen von geobasierten Lage- und Führungsinformationssystemen zum Einsatz kommen können.}},
  author       = {{Pottebaum, Jens and Rechberger, Christina and Hieb, Michael and Gräßler, Iris and Resch, Christian}},
  booktitle    = {{Tagungsband der Fachtagung Katastrophenforschung 2023}},
  isbn         = {{978-3-900397-11-1}},
  location     = {{Leoben}},
  pages        = {{26--29}},
  title        = {{{Extremwettersituationen in alpinen Gebieten: Management kritischer Situationen in Echtzeit bei extremen und komplexen Daten}}},
  year         = {{2023}},
}

@article{44382,
  abstract     = {{<jats:p>The success of engineering complex technical systems is determined by meeting customer requirements and institutional regulations. One example relevant to the automobile industry is the United Nations Economic Commission of Europe (UN ECE), which specifies the homologation of automobile series and requires proof of traceability. The required traceability can be achieved by modeling system artifacts and their relations in a consistent, seamless model—an effect-chain model. Currently, no in-depth methodology exists to support engineers in developing certification-compliant effect-chain models. For this purpose, a new methodology for certification-compliant effect-chain modeling was developed, which includes extensions of an existing method, suitable models, and tools to support engineers in the modeling process. For evaluation purposes, applicability is proven based on the experience of more than 300 workshops at an automotive OEM and an automotive supplier. The following case example is chosen to demonstrate applicability: the development of a window lifter that has to meet the demands of UN ECE Regulations R156 and R21. Results indicate multiple benefits in supporting engineers with the certification-compliant modeling of effect chains. Three benefits are goal-oriented modeling to reduce the necessary modeling capacity, increasing model quality by applying information quality criteria, and the potential to reduce costs through automatable effect-chain analyses for technical changes. Further, companies in the automotive and other industries will benefit from increased modeling capabilities that can be used for architecture modeling and to comply with other regulations such as ASPICE or ISO 26262.</jats:p>}},
  author       = {{Gräßler, Iris and Wiechel, Dominik and Koch, Anna-Sophie and Sturm, Tim and Markfelder, Thomas}},
  issn         = {{2079-8954}},
  journal      = {{Systems}},
  keywords     = {{Information Systems and Management, Computer Networks and Communications, Modeling and Simulation, Control and Systems Engineering, Software}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  title        = {{{Methodology for Certification-Compliant Effect-Chain Modeling}}},
  doi          = {{10.3390/systems11030154}},
  volume       = {{11}},
  year         = {{2023}},
}

@article{44687,
  abstract     = {{Entwicklungsprojekte stehen in einem Spannungsfeld von Volatilität, Unsicherheit, Komplexität und Ambiguität (VUCA). Resilient Requirements Engineering (RRE) ist ein vielversprechender Ansatz, diesen Rahmenbedingungen gerecht zu werden und erfolgreich zu entwickeln. Es werden Methoden aus den drei Innovationsfeldern des RRE – Vorausschau, Effizienz und Nachhaltigkeit – angewendet, um Effizienzpotenziale in der Produktentwicklung zu nutzen und frühzeitig Nachhaltigkeitsdimensionen in der Ermittlung von Stakeholderbedürfnissen zu verankern.}},
  author       = {{Gräßler, Iris and Oleff, Christian and Preuß, Daniel and Koch, Anna-Sophie}},
  issn         = {{2511-0896}},
  journal      = {{Zeitschrift für wirtschaftlichen Fabrikbetrieb}},
  keywords     = {{Management Science and Operations Research, Strategy and Management, General Engineering}},
  number       = {{4}},
  pages        = {{222--225}},
  publisher    = {{Walter de Gruyter GmbH}},
  title        = {{{Resilient Requirements Engineering}}},
  doi          = {{10.1515/zwf-2023-1030}},
  volume       = {{118}},
  year         = {{2023}},
}

@inproceedings{45661,
  abstract     = {{Effect chain modelling is a method for creating information
models for impact analyses of changes in system elements. For
the estimation of change propagation, dependencies between
requirements must be detected. The high number of require-
ment dependencies in the engineering of complex technical
systems results in the need for automation. In a study, it was
shown that transformer models (BERT) are suitable for the
automated dependency analysis of requirements. However,
there are currently deficits in the applicability of the models
for different projects without an extensive and heterogeneous
training database. This paper investigates how active learning
can be used to train BERT models (active-BERT) in order to
increase the performance of the models for classifying requi-
rement dependencies of projects with heterogeneous require-
ments. The results show that the performance of the models
increases significantly through active learning. Through active-
BERT, engineers are enabled to model effect chains efficiently
and to handle requirement changes effectively.}},
  author       = {{Gräßler, Iris and Preuß, Daniel}},
  booktitle    = {{Stuttgarter Symposium für Produktentwicklung SSP 2023}},
  editor       = {{Hölzle, Katharina and Kreimeyer, Matthias and Roth, Daniel and Maier, Thomas and Riedel, Oliver}},
  issn         = {{2364-4885}},
  location     = {{Stuttgart}},
  publisher    = {{Fraunhofer IAO}},
  title        = {{{Automatisierte Abhängigkeitsanalyse von Anforderungen zur Wirkkettenmodellierung}}},
  year         = {{2023}},
}