Cross-disciplinary engineering with AutomationML and SysML

Berardinelli, Luca; Biffl, Stefan; Lüder, Arndt; Mätzler, Emanuel; Mayerhofer, Tanja; Wimmer, Manuel; Wolny, Sabine

doi:10.1515/auto-2015-0076

Cited by 32 publications

(22 citation statements)

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“…The engineering of mechatronic production systems requires the strong collaboration of different disciplines, e.g., systems engineering, mechanics, electrics/electronics and control software. According to Berardinelli et al [7] and Vogel-Heuser et al [61], mechatronic production systems are highly complex systems that consist of different mechanical, electrical hardware and automation software (see Fig. 1).…”

Section: Cross-disciplinary Engineering Of Mechatronic Production Sys...mentioning

confidence: 99%

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Using two case studies to explore the applicability of VIATRA for the model-driven engineering of mechatronic production systems

Koltun

Pundel

2022

Softw Syst Model

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The engineering of mechatronic production systems is complex and requires various disciplines (e.g., systems, mechanical, electrical and software engineers). Model-driven engineering (MDE) supports systems development and the exchange of information based on models and transformations. However, the integration and adoption of different modeling approaches are becoming challenges when it comes to cross-disciplinary work. VIATRA is a long-living enduring and mature modeling framework that offers rich model transformation features to develop MDE applications. This study investigates the extent to which VIATRA can be applied in the engineering of mechatronic production systems. For this purpose, two model transformation case studies are presented: “SysML – AutomationML” and “SysML4Mechatronics – AutomationML.” Both case studies are representative of structural modeling and interdisciplinary data exchange during the development of mechatronic production systems. These case studies are derived from other researchers in the community. A VIATRA software prototype implements these case studies as a batch-oriented transformation and serves as one basis for evaluating VIATRA. To report on our observations and findings, we built on an evaluation framework from the MDE community. This framework considers 14 different characteristics (e.g., maturity, size, execution time, modularity, learnability), according to the Goal-Question-Metric paradigm. To be able to evaluate our findings, we compared VIATRA to ATL. We applied all cases to a lab-size mechatronic production system. We found that, with VIATRA, the same functions for model transformation applications can be achieved as with ATL, which is popular for model transformations in both the MDE and the mechatronic production systems community. VIATRA combines the relational, imperative, and graph-based paradigms and enables the development and execution of model-to-model (M2M) and model-to-text (M2T) transformations. Furthermore, the VIATRA internal DSL is based on Xtend and Java, making VIATRA attractive and intuitive for users with less experience in modeling than in object-oriented programming. Thus, VIATRA leads to an interesting alternative for the model-driven engineering of mechatronic production systems. It has the potential to reduce the complexity during the development of model transformations. To conclude, this paper evaluates the applicability of VIATRA, its strengths and limitations. It provides lessons learned and insights that can stimulate further research in the MDE for mechatronic production systems.

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Section: Cross-disciplinary Engineering Of Mechatronic Production Sys...mentioning

confidence: 99%

“…4). These case studies have been widely studied [7,38,43], and we have adapted them to fit the purpose of our study. (ii) A VIATRA software prototype implemented the case studies as batch-oriented transformations, i.e., models are built from scratch (Sect.…”

Section: Introductionmentioning

confidence: 99%

Using two case studies to explore the applicability of VIATRA for the model-driven engineering of mechatronic production systems

Koltun

Pundel

2022

Softw Syst Model

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“…from UML State Charts [25] or sequence diagram [26]) to save development time and cost with fewer faults than manual transformation. As a syntactic standardised data exchange among the different presentations, AutomationML [27] is suggested, and accordingly, the approaches weaving different models and languages through this have appeared such as [28]. One remark is that model transformation is defined on the premise of the inter‐metamodel consistencies which have been either assumed to be consistent or handled by full knowledge regarding the target metamodels (usually well‐known) to build transformation rules.…”

Section: State Of the Artmentioning

confidence: 99%

Analysis of metamodels for model‐based production automation system engineering

Cha

Vogel‐Heuser

Fischer

2020

IET Collaborative Intelligent Manufacturing

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“…Moreover, with AutomationML [15], research and industry have started a promising initiative on modeling automation systems for Industry 4.0 that features research groups for all participating communities. However, the underlying technologies that define models and languages (e.g., XML) can significantly improve from research conducted in the community around model-driven software development.…”

Section: Different Perspectives On Modelingmentioning

confidence: 99%

Modeling languages in Industry 4.0: an extended systematic mapping study

et al. 2019

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Industry 4.0 integrates Cyber-Physical Systems with the Internet of Things to optimize the complete value-added chain. Successfully applying Industry 4.0 requires the cooperation of various stakeholders from different domains. Domain-specific modeling languages promise to facilitate their involvement through leveraging (domain-specific) models to primary development artifacts. Objective: We aim to assess the use of modeling in Industry 4.0 through the lens of modeling languages in a broad sense. Method: Based on an extensive literature review, we updated our systematic mapping study on modeling languages and modeling techniques used in Industry 4.0 [159] to include publications until February 2018. Overall, the updated study considers 3344 candidate publications that were systematically investigated until 408 relevant publications were identified. Based on these, we developed an updated map of the research landscape on modeling languages and techniques for Industry 4.0. Results: Research on modeling languages in Industry 4.0 focuses on contributing methods to solve the challenges of digital representation and integration. To this end, languages from systems engineering and knowledge representation are applied most often but rarely combined. There also is a gap between the communities researching and applying modeling languages for In

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Cross-disciplinary engineering with AutomationML and SysML

Cited by 32 publications

References 0 publications

Using two case studies to explore the applicability of VIATRA for the model-driven engineering of mechatronic production systems

Using two case studies to explore the applicability of VIATRA for the model-driven engineering of mechatronic production systems

Analysis of metamodels for model‐based production automation system engineering

Modeling languages in Industry 4.0: an extended systematic mapping study

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