digital engineering

Digital Twins Using Semantic Modeling and AI

Digital Twins Using Semantic Modeling and AI

Self-learning development and simulation of industrial production facilities
Wolfram Höpken ORCID Icon, Ralf Stetter ORCID Icon, Markus Pfeil ORCID Icon, Thomas Bayer ORCID Icon, Bernd Michelberger, Markus Till, Timo Schuchter, Alexander Lohr
The AI-driven, self-learning digital twin continuously adapts to real system behavior, ensuring an optimal representation of the production process. A comprehensive semantic model serves as the foundation for advanced artificial intelligence (AI) approaches. Insights derived from AI methods are integrated into this model, enhancing the interpretability and explainability of AI systems. Techniques from the field of eXplainable AI (XAI) facilitate the automated description of AI models and their findings, as well as the development of self-explanatory models.
Industry 4.0 Science | Volume 41 | Edition 2 | Pages 30-36
Flexible Reference Model for Planning and Optimization

Flexible Reference Model for Planning and Optimization

Generierung digitaler Fabrikmodelle durch den digitalen Zwilling
Jürgen Köbler, David Wußler, Michael Schlecht, Sarah Kirchenbaur, Roland de Guio, Max Blöchle, Benedikt Schwaiger
In the first article, the reference model was already explained in its essential features [1]. In the second part, the further development to a flexible reference model will be shown. The focus is on the extension to implement different source systems, the implementation of further planning tools, and the implementation of AI tools to achieve dynamic production engineering in the form of holistic and integrated factory planning. This paper explains the development of a holistic demonstrator as a proof of concept.
Industrie 4.0 Management | Volume 38 | 2022 | Edition 5 | Pages 45-48 | DOI 10.30844/IM_22-5_45-48
Digital Engineering Challenges

Digital Engineering Challenges

Use of virtual engineering for services and processes
Veit Köppen, Gunter Saake
Digital Engineering is becoming more important due to its potentials in cost reduction as well as a unique instrument to control complex products and processes. Nowadays it is used in the industry to cope with more and more individual products accompanied by efficiency requirements on the production processes. The proceeding technical evolution makes it possible to participate in the domain of services from digital engineering. In this article we address challenges for linking process execution and planning. A logistic example illustrates the fusion of real and virtual world.
Industrie Management | Volume 26 | 2010 | Edition 2 | Pages 49-52
Interoperable Test Environments for Distributed Applications

Interoperable Test Environments for Distributed Applications

Michael Schenk, Marco Schumann
In many cases, digital engineering is the only realistic answer to important trends in industry. This includes increasingly customized products, decentralized value chains, increasing complexity and functionality of products as well as the need to reduce the time to market. Digital Engineering has already resulted in many technology-driven changes of the product development process. Probably the most important change is the ability to execute some of the iteration loops of development and test completely in the virtual environment. Further improvements can only be achieved by simultaneously combining development and simulation tools from many different domains, such as mechanical engineering, physics simulation, and software engineering. This article identifies current research issues in the field of distributed interoperable testing environments. Moreover, two examples of current applications are given to illustrate what functionalities can already be utilized today.
Industrie Management | Volume 25 | 2009 | Edition 2 | Pages 47-50