Towards a Model-Driven Architecture for Interactive Digital Twin Cockpits

Dalibor, Manuela; Michael, Judith; Rumpe, Bernhard; Varga, Simon; Wortmann, Andreas

doi:10.1007/978-3-030-62522-1_28

Cited by 44 publications

(16 citation statements)

References 15 publications

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“…These models carry explicit domain expertise and serve as a foundation for communication, documentation, analysis, and synthesis in agile development projects [129]. They can be systematically transformed into concrete implementations [49] such as Digital Twins [13,43], privacy-preserving IoT systems [104], information systems [54], or assistive systems [105]. Digital shadows [88,128,135] relate to models, can carry models themselves, and serve as (aggregated) abstractions of models for automated processing [13,43].…”

Section: Model-driven Digital Shadowsmentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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Section: Model-driven Digital Shadowsmentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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“…MontiArc comes with a Java code generator that generates Java classes conforming to the specified components, methods to access port values, and a mechanism to inject handwritten behavior specifications. In previous work, we built a DT with MontiArc, that enables automatic experiment execution on injection molding machines [5] and also provides a cockpit for visualizing the current state of the machine [18]. We define a Digital Twin (DT) of a system as a set of models of the system, a set of contextual data traces, and a set of services to use the data and models purposefully with respect to the original system [5].…”

Section: Digital Twin Architecturementioning

confidence: 99%

“…The local similarities define the critical parameters with influence on the metric (ll. [4][5][6][7][8]. The values of backPressure and dosingTime are more sensitive to changes.…”

Section: Application Examplementioning

confidence: 99%

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Self-Adaptive Manufacturing with Digital Twins

Bolender

Bürvenich

Dalibor

et al. 2021

2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)

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Digital Twins are part of the vision of Industry 4.0 to represent, control, predict, and optimize the behavior of Cyber-Physical Production Systems (CPPSs). These CPPSs are long-living complex systems deployed to and configured for diverse environments. Due to specific deployment, configuration, wear and tear, or other environmental effects, their behavior might diverge from the intended behavior over time. Properly adapting the configuration of CPPSs then relies on the expertise of human operators. Digital Twins (DTs) that reify this expertise and learn from it to address unforeseen challenges can significantly facilitate self-adaptive manufacturing where experience is very specific and, hence, insufficient to employ deep learning techniques. We leverage the explicit modeling of domain expertise through case-based reasoning to improve the capabilities of Digital Twins for adapting to such situations. To this effect, we present a modeling framework for self-adaptive manufacturing that supports modeling domain-specific cases, describing rules for case similarity and case-based reasoning within a modular Digital Twin. Automatically configuring Digital Twins based on explicitly modeled domain expertise can improve manufacturing times, reduce wastage, and, ultimately, contribute to better sustainable manufacturing.

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“…In the Humanities, the DT concept unfold a massive potential to transform the landscape of how DH methods can assist in the representation, analysis and understanding of our past, which in turn can provide useful learnings for the present and future. It promises a tremendous innovation potential, and most of the current research on digital twins is focusing on specific implementations for concrete use cases and the generalization towards reusable abstract models [4]. Developing a mirror of a traditional Digital Humanities record system through the digital twin lens is time-consuming, complicated and requires deep interdisciplinary knowledge in the humanities domain and model creation and software development.…”

Section: Introductionmentioning

confidence: 99%

Historical Civil Registration Record Transcription Using an eXtreme Model Driven Approach

Khan¹,

Schieweck²,

Breathnach³

et al. 2021

Proceedings of ISP RAS

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Modelling is considered as a universal approach to define and simplify real-world applications through appropriate abstraction. Model-driven system engineering identifies and integrates appropriate concepts, techniques, and tools which provide important artefacts for interdisciplinary activities. In this paper, we show how we used a model-driven approach to design and improve a Digital Humanities dynamic web application within an interdisciplinary project that enables history students and volunteers of history associations to transcribe a large corpus of image-based data from the General Register Office (GRO) records. Our model-driven approach generates the software application from data, workflow and GUI abstract models, ready for deployment.

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Towards a Model-Driven Architecture for Interactive Digital Twin Cockpits

Cited by 44 publications

References 15 publications

A Computer Science Perspective on Digital Transformation in Production

A Computer Science Perspective on Digital Transformation in Production

Self-Adaptive Manufacturing with Digital Twins

Historical Civil Registration Record Transcription Using an eXtreme Model Driven Approach

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