IM+io Fachmagazin, Ausgabe 1/2025

Digitale Archäologie: Wie alte Daten neue Produkte formen

Literaturhinweise

[1]      VDI/VDE-Gesellschaft Mess- und Automatisierungstechnik (GMA): Entwicklung mechatronischer und cyber-physischer Systeme. 2021.

[2]      Albers, Albert; Nikola Bursac; Eike Wintergerst: Produktgenerationsentwicklung – Bedeutung und Herausforderungen aus einer entwicklungsmethodischen Perspektive, in: Stuttgarter Symposium für Produktentwicklung, 2015.

[3]      Mordecai, Yaniv; Dov Dori: Towards a Quantitative Framework for Evaluating the Expressive Power of Conceptual System Models. INCOSE International Symposium 26 2016. S. 42–57.

[4]      Melzer, Sylvia; Tim Weilkiens; Christian Muggeo; Axel Berres: Sustainable Development of Information Systems Using SysML, FAS and DOL, in: 2024 IEEE International Systems Conference (SysCon), 2024. S. 1–8.

[5]      INCOSE: Systems Engineering Vision 2035 – Engineering Solutions for a better World, 2021.

[6]      Berschik, Markus Christian; Thomas Schumacher; Fabian Niklas Laukotka; Dieter Krause; David Inkermann: MBSE WITHIN THE ENGINEERING DESIGN COMMUNITY – AN EXPLORATORY STUDY. Proceedings of the Design Society 3 2023. 2595–2604.

[7]      Friedenthal, Sanford: A practical guide to SysML: The systems modeling language, Third edition, 2014.

[8]      Delligatti, Lenny: SysML Distilled: A Brief Guide to the Systems Modeling Language, 2014.

[9]      Jansen, Sebastian: Eine Methodik zur modellbasierten Partitionierung mechatronischer Systeme, 2007.

[10]    Schumacher, Thomas; David Inkermann: Model Inconsistencies and Solution Approaches to Maintain Consistency in Model-based Systems Engineering, in: O. Michler (Hrsg.), ICONS 2023, 2023. S. 23–28.

[11]    Schumacher, Thomas; Roman Stephan; David Inkermann: Development and Implementation of Digital Heterogeneous Models in Model-based Systems Engineering (Status: submitted), in: 2025.

[12]    Schumacher, Thomas; David Inkermann: Investigation of advantages of models and the modelling process by introducing a model evaluation concept. Proceedings of the Design Society 4 2024. 2735–2744.

[13]    Wang, Quan; Zhendong Mao; Bin Wang; Li Guo: Knowledge Graph Embedding: A Survey of Approaches and Applications. IEEE Trans. Knowl. Data Eng. 29 2017. 2724–2743.

[14]    Ji, Shaoxiong; Shirui Pan; Erik Cambria; Pekka Marttinen; Philip S. Yu: A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Trans. Neural Netw. Learning Syst. 33 2022. 494–514.

[15]    Faheem, Faizan; Zirui Li; Stephan Husung: Analysis of potential errors in technical products by combining knowledge graphs with MBSE approach. Engineering for a Changing World: Proceedings : 60th ISC Ilmenau Scientific Colloquium 2023. 2023.

[16]    Wu, Rundi; Chang Xiao; Changxi Zheng: DeepCAD: A Deep Generative Network for Computer-Aided Design Models, in: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021. S. 6752–6762.

[17]    Yao, Liang; Jiazhen Peng; Chengsheng Mao; Yuan Luo: Exploring Large Language Models for Knowledge Graph Completion. 2023.