Model-Driven Consistency Preservation in AutomationML

Ananieva, Sofia; Bürger, Erik; Stier, Christian

doi:10.1109/coase.2018.8560343

Cited by 3 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hua et al [35] present a concept learning approach in AutomationML using the DL-Learner framework. Ananieva et al [36] develop an approach to detect and repair inconsistencies in systems modeled with AutomationML. • Integration: In this field, AutomationML is a component which is integrated in all kinds of digital automation systems.…”

Section: • Data Exchange: This Research Field Is Focusing On Doing Da...mentioning

confidence: 99%

AutomationML in Industry 4.0 Environment: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

AutomationML is an open neutral XML based data exchange format used in automation systems. It has come into the public for more than 10 years and is being used in many different areas in all kinds of manufacturing applications, such as digital twin, reconfigurable manufacturing systems, heterogeneous data exchange, etc. However, no comprehensive literature review on the research and application progress of AutomationML has been found since the initiation of AutomationML. Based on the study and analysis of AutomationML related publications, this paper gives a detailed illustration on the state-of-theart of AutomationML. Firstly, the background and terminologies related to Au-tomationML are introduced. Secondly, the paper applies a methodology to collect AutomationML related publications, on which an analysis based on a multidimensional literature classification is conducted. Thirdly, according to the analysis results, current research status and whether AutomationML can meet the requirements for industry 4.0 environment are discussed. Finally, conclusion and outlook are illustrated in the end.

show abstract

Section: • Data Exchange: This Research Field Is Focusing On Doing Da...mentioning

confidence: 99%

AutomationML in Industry 4.0 Environment: A Systematic Literature Review

et al. 2021

View full text Add to dashboard Cite

show abstract

Multi-model consistency preservation

Klare

2018

Proceedings of the 21st ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedi

View full text Add to dashboard Cite

Inconsistency management in heterogeneous engineering data in intralogistics based on coupled metamodels

Fan

Wünnenberg

Schypula

et al. 2023

At - Automatisierungstechnik

View full text Add to dashboard Cite

During the development of intralogistics systems (ILS), heterogeneous models are created, which represent discipline-specific views, e.g., control software developed by automation engineers or discrete-event simulation models created by simulation engineers. These models represent discipline-specific views on the system but contain overlapping information. Thereby, keeping the information in different development models consistent is challenging and currently requires high manual effort, which highly depends on the developers’ experience. To overcome this challenge, an approach to link heterogeneous model data and identify potential information inconsistencies within and between models automatically is proposed. The concept is evaluated with a use case containing three typical inconsistencies from five representative engineering models applied in ILS development.

show abstract

Model-Driven Consistency Preservation in AutomationML

Cited by 3 publications

References 5 publications

AutomationML in Industry 4.0 Environment: A Systematic Literature Review

AutomationML in Industry 4.0 Environment: A Systematic Literature Review

Multi-model consistency preservation

Inconsistency management in heterogeneous engineering data in intralogistics based on coupled metamodels

Contact Info

Product

Resources

About