Scientific fundamentals of Industry 4.0

Birgit Vogel-Heuser; Ulrich Jumar

doi:10.1515/auto-2019-0012

Article Open Access

Scientific fundamentals of Industry 4.0

Birgit Vogel-Heuser
Prof. Dr.-Ing. Birgit Vogel-Heuser is a full professor and director of the Institute of Automation and Information Systems at the Technical University of Munich. Her main research interests are systems engineering, software engineering, and modeling of distributed and reliable embedded systems. She is coordinator of the Collaborative Research Centre (CRC) 768: Managing cycles in innovation processes – integrated development of product-service systems based on technical products, member of acatech, chair of the VDI/VDE working group on industrial agents and vice chair of the IFAC TC 3.1 computers in control.
and Ulrich Jumar
Prof. Dr.-Ing. Ulrich Jumar is head of the Institut für Automation und Kommunikation e.V. Magdeburg (ifak). His main research interests are modelling and simulation, as well as control theory in various fields of automation.

Published/Copyright: June 8, 2019

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From the journal at - Automatisierungstechnik Volume 67 Issue 6

The term Industry 4.0 has already been around for some years now. Nevertheless, principles and fundamentals are still not common knowledge and lack publications – especially outside the German-speaking community.

Starting with this AT issue, a series of contributions aims to lay the scientific foundation for Industry 4.0. It is not the goal of this series to provide papers that build upon each other, but to gather different aspects and views, and to provide these in a comprehensive way. Thus, the series gives an overview of the state of the art with a focus on the respective sub-aspects.

On the one hand, the definition of Industry 4.0 seems to be quite clear and comprehensive: Industry 4.0 refers to the intelligent networking of machines and processes for industry with the help of information and communication technology [1]. On the other hand, there are several definitions with different foci. Therefore, we would like to summarize the definition referring to Wikipedia and IEEE TASE [2] in the following:

“Service Orientation: CPPS offering services via the Internet based on a service oriented reference architecture.
Intelligent self-organizing CPPS providing the ability of CPPS to make decisions on their own (decentralization).
The ability of CPS, humans and CPPS to connect and communicate with each other (interoperability):
1. Information aggregation and representation for the human in the loop during engineering and maintenance of aPS;
2. A virtual copy of CPPS on different levels of detail, e. g., from sensors and actuators to the entire CPPS (virtualization);
3. Relevant process and engineering information for data analysis (real time capability);
The ability to flexibly adapt to changing requirements by replacing or expanding individual modules (cross-disciplinary modularity).
Big Data algorithms and technologies provided in real-time (real-time capability).
Optimization of the manufacturing process based on these algorithms and data to increase Overall Equipment Effectiveness (OEE).
Data integration across disciplines and along the life cycle based on standardized data models and a model driven modular engineering process.
Secure communication enabling a worldwide network of aPS supporting economic industrial partnership across companies’ borders.
Access to data securely stored in a Cloud/Intranet.”

We will start this series with the paper “Applying Knowledge Bases to Make Factories Smarter” by Felix Ocker, Christiaan J. J. Paredis, and Birgit Vogel-Heuser. Knowledge-based systems form a subclass of artificial intelligence, which serves as an enabler for Industry 4.0. The paper gives an overview of existing knowledge bases from the engineering domain as well as generic top-level ontologies that allow engineers to combine domain-specific knowledge bases. The series will continue in 2019 and 2020, covering various Industry 4.0 topics such as digital twins, system architectures for realizing an active administration shell, and testing of Industry 4.0 systems to name just a few.

This series can only be successful, if all researchers in this research area are willing to contribute and share their knowledge and recent results. Therefore, we look forward to more papers being submitted. Up to now, we thank Christian Diedrich, Ulrich Epple, Alexander Fay, Stefan Kowalewski, and Michael Weyrich, for their support, to name only the core team of this series’ initiators.

We would like to share the strength of the German platform “Industrie 4.0” internationally and reach the international community, too – this is why we publish this series in English and make it available open access.

About the authors

Prof. Dr.-Ing. Birgit Vogel-Heuser

Prof. Dr.-Ing. Birgit Vogel-Heuser is a full professor and director of the Institute of Automation and Information Systems at the Technical University of Munich. Her main research interests are systems engineering, software engineering, and modeling of distributed and reliable embedded systems. She is coordinator of the Collaborative Research Centre (CRC) 768: Managing cycles in innovation processes – integrated development of product-service systems based on technical products, member of acatech, chair of the VDI/VDE working group on industrial agents and vice chair of the IFAC TC 3.1 computers in control.

Prof. Dr.-Ing. Ulrich Jumar

Prof. Dr.-Ing. Ulrich Jumar is head of the Institut für Automation und Kommunikation e.V. Magdeburg (ifak). His main research interests are modelling and simulation, as well as control theory in various fields of automation.

References

1. “What is Industrie 4.0?”, Plattform-i40.de, 2019. [Online]. Available online: https://www.plattform-i40.de/I40/Navigation/EN/Industrie40/WhatIsIndustrie40/what-is-industrie40.html. [Accessed: 04-Feb-2019].Search in Google Scholar

2. B. Vogel-Heuser and D. Hess, “Guest Editorial Industry 4.0–Prerequisites and Visions”, IEEE Transactions on Automation Science and Engineering, vol. 13, no. 2, pp. 411–413, 2016.10.1109/TASE.2016.2523639Search in Google Scholar

Published Online: 2019-06-08

Published in Print: 2019-06-26

This work is licensed under the Creative Commons Attribution 4.0 Public License.

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