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Analysis of institutional authors

Garmendia AAuthorGuerra EAuthorDe Lara JAuthor

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Proceedings Paper

Modelling Production System Families with AutomationML

Publicated to:IEEE International Conference on Emerging Technologies and Factory Automation, ETFA. 2020-September 1057-1060 - 2020-09-01 2020-September(), DOI: 10.1109/ETFA46521.2020.9211894

Authors: Garmendia A; Wimmer M; Mazak-Huemer A; Guerra E; De Lara J

Affiliations

Johannes Kepler University Linz - Author
Montanuniversitat Leoben - Author
Universidad Autónoma de Madrid - Author

Abstract

© 2020 IEEE. The description of families of production systems usually relies on the use of variability modelling. This aspect of modelling is gaining increasing interest with the emergence of Industry 4.0 to facilitate the product development as new requirements appear. As a consequence, there are several emerging modelling techniques able to apply variability in different domains. In this paper, we introduce an approach to establish product system families in AutomationML. Our approach is based on the definition of feature models describing the variability space, and on the assignment of presence conditions to AutomationML model elements. These conditions (de-)select the model elements depending on the chosen configuration. This way, it is possible to model a large set of model variants in a compact way using one single model. To realize our approach, we started from an existing EMF-based AutomationML workbench providing graphical modelling support. From these artifacts, we synthesized an extended graphical modelling editor with variability support, integrated with FeatureIDE. Furthermore, we validated our approach by creating and managing a production system family encompassing six scenarios of the Pick and Place Unit Industry 4.0 demonstrator.

Keywords

AutomationmlFeature modellingModel-driven engineeringProduct linesSoftwareVariability

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal IEEE International Conference on Emerging Technologies and Factory Automation, ETFA due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2020, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Electrical and Electronic Engineering.

From a relative perspective, and based on the normalized impact indicator calculated from the Field Citation Ratio (FCR) of the Dimensions source, it yields a value of: 1.72, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: Dimensions Jun 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2025-06-21, the following number of citations:

  • WoS: 2
  • Scopus: 3
  • OpenCitations: 3

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-06-21:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 10 (PlumX).

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Austria.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (GARMENDIA JORGE, ANTONIO) and Last Author (LARA JARAMILLO, JUAN DE).