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Model-Driven Development of a Digital Twin for Injection Molding

Digital Twins (DTs) of Cyber-Physical Production Systems (CPPSs) enable the smart automation of production processes, collection of data, and can thus reduce manual efforts for supervising and controlling CPPSs. Realizing DTs is challenging and requires significant efforts for their conception and i...

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Autores principales: Bibow, Pascal, Dalibor, Manuela, Hopmann, Christian, Mainz, Ben, Rumpe, Bernhard, Schmalzing, David, Schmitz, Mauritius, Wortmann, Andreas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266454/
http://dx.doi.org/10.1007/978-3-030-49435-3_6
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author Bibow, Pascal
Dalibor, Manuela
Hopmann, Christian
Mainz, Ben
Rumpe, Bernhard
Schmalzing, David
Schmitz, Mauritius
Wortmann, Andreas
author_facet Bibow, Pascal
Dalibor, Manuela
Hopmann, Christian
Mainz, Ben
Rumpe, Bernhard
Schmalzing, David
Schmitz, Mauritius
Wortmann, Andreas
author_sort Bibow, Pascal
collection PubMed
description Digital Twins (DTs) of Cyber-Physical Production Systems (CPPSs) enable the smart automation of production processes, collection of data, and can thus reduce manual efforts for supervising and controlling CPPSs. Realizing DTs is challenging and requires significant efforts for their conception and integration with the represented CPPS. To mitigate this, we present an approach to systematically engineering DTs for injection molding that supports domain-specific customizations and automation of essential development activities based on a model-driven reference architecture. In this approach, reactive CPPS behavior is defined in terms of a Domain-Specific Language (DSL) for specifying events that occur in the physical system. The reference architecture connects to the CPPS through a novel DSL for representing OPC-UA bindings. We have evaluated this approach with a DT of an injection molding machine that controls the machine to optimize the Design of Experiment (DoE) parameters between experiment cycles before the products are molded. Through this, our reference implementation of the DT facilitates the time-consuming setup of a DT and the subsequent injection molding activities. Overall, this facilitates to systematically engineer digital twins with reactive behavior that help to optimize machine use.
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spelling pubmed-72664542020-06-03 Model-Driven Development of a Digital Twin for Injection Molding Bibow, Pascal Dalibor, Manuela Hopmann, Christian Mainz, Ben Rumpe, Bernhard Schmalzing, David Schmitz, Mauritius Wortmann, Andreas Advanced Information Systems Engineering Article Digital Twins (DTs) of Cyber-Physical Production Systems (CPPSs) enable the smart automation of production processes, collection of data, and can thus reduce manual efforts for supervising and controlling CPPSs. Realizing DTs is challenging and requires significant efforts for their conception and integration with the represented CPPS. To mitigate this, we present an approach to systematically engineering DTs for injection molding that supports domain-specific customizations and automation of essential development activities based on a model-driven reference architecture. In this approach, reactive CPPS behavior is defined in terms of a Domain-Specific Language (DSL) for specifying events that occur in the physical system. The reference architecture connects to the CPPS through a novel DSL for representing OPC-UA bindings. We have evaluated this approach with a DT of an injection molding machine that controls the machine to optimize the Design of Experiment (DoE) parameters between experiment cycles before the products are molded. Through this, our reference implementation of the DT facilitates the time-consuming setup of a DT and the subsequent injection molding activities. Overall, this facilitates to systematically engineer digital twins with reactive behavior that help to optimize machine use. 2020-05-30 /pmc/articles/PMC7266454/ http://dx.doi.org/10.1007/978-3-030-49435-3_6 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Bibow, Pascal
Dalibor, Manuela
Hopmann, Christian
Mainz, Ben
Rumpe, Bernhard
Schmalzing, David
Schmitz, Mauritius
Wortmann, Andreas
Model-Driven Development of a Digital Twin for Injection Molding
title Model-Driven Development of a Digital Twin for Injection Molding
title_full Model-Driven Development of a Digital Twin for Injection Molding
title_fullStr Model-Driven Development of a Digital Twin for Injection Molding
title_full_unstemmed Model-Driven Development of a Digital Twin for Injection Molding
title_short Model-Driven Development of a Digital Twin for Injection Molding
title_sort model-driven development of a digital twin for injection molding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7266454/
http://dx.doi.org/10.1007/978-3-030-49435-3_6
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