Rainer Drath (Pforzheim University of Applied Sciences, Tiefenbronner Str. 65, 75175 Pforzheim, Germany)
Presented at
6th AutomationML Conference: 20 Years of AutomationML
22–23 September 2026, Hochschule Pforzheim, Germany
Hosted by Hochschule Pforzheim
Type
Technical presentation — accepted abstract
Open access
© 2026 The Authors. Published under the CC BY-NC-ND 4.0 licence. Peer-review under responsibility of the scientific committee of the AutomationML Conference 2026.
The full contribution will be published on this page after the conference.
Abstract
AutomationML (AML) has emerged as a foundational standard for exchanging heterogeneous engineering data across industrial automation. Despite its widespread adoption, existing domain-specific attribute libraries often suffer from semantic fragmentation, inconsistent modeling practices, and limited cross-domain interoperability. This contribution introduces and presents a novel and comprehensive, cross-disciplinary series of AutomationML attribute libraries, systematically developed for several engineering domains. The open-source libraries provide a standardized, semantically rich set of more than 500 re-usable core attribute types, like “length”, “weight”, and physical constants, align with international engineering standards and support consistent, tool-agnostic modeling, furthermore a sustainable development process for future enrichment of the libraries, and a comprehensive concept to interconnect the attribute types with other semantic standards as ECLASS or IEC CDD. The author demonstrates the library’s practical applicability through representative use cases. The implementation showcases how standardized attribute types enable automated validation, semantic traceability, interconnecting existing semantic standards and seamless data exchange across heterogeneous engineering tools. By bridging disciplinary silos and reducing modeling redundancy, the proposed library establishes a unified foundation for advanced engineering workflows and Industry 4.0 applications. The library is intended to become publicly available for community testing and serves as a reference candidate for future AutomationML standardization.
Keywords: attribute core library; cross-domain engineering; semantic data exchange; digital twin; Industry 4.0
Speaker
Prof. Dr.-Ing. Rainer Drath is Professor of Mechatronic Systems Engineering at Pforzheim University, Germany. He studied Electrical and Automation Engineering at the Technical University of Ilmenau and earned his doctorate in automation engineering.
Prior to joining academia in 2017, he held various research and leadership positions at ABB Corporate Research, where he worked on systems engineering, virtual engineering, functional safety, and Industry 4.0. Prof. Drath is internationally recognized for his contributions to digital engineering and industrial interoperability. He is one of the principal architects of AutomationML (IEC 62714) and has made significant contributions to the development of CAEX and related international standards. His research focuses on systems engineering, digital twins, Industry 4.0, engineering automation, and model-based engineering. He has authored more than 300 publications and several books and has received multiple international awards for his scientific contributions.
BibTeX
@inproceedings{drath2026corelibraries,
author = {Drath, Rainer},
title = {{AML} Core Property Libraries for Engineering},
booktitle = {Proceedings of the 6th AutomationML Conference: 20 Years of AutomationML},
address = {Pforzheim, Germany},
month = sep,
year = {2026},
publisher = {AutomationML e.V.},
url = {https://www.automationml.org/conferences/conference-highlights/aml-core-property-libraries-for-engineering/}
}
How to Cite
R. Drath: “AML Core Property Libraries for Engineering”, Proceedings of the 6th AutomationML Conference: 20 Years of AutomationML, Pforzheim, Germany, 22–23 September 2026.
This page presents the accepted abstract of a contribution to the AutomationML Conference 2026. The full text will be added after the conference. If you are an author and would like a correction, please contact office@automationml.org.