Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook

Nilsson, Jacob; Sandin, Fredrik

doi:10.1109/indin.2018.8471971

Cited by 52 publications

(28 citation statements)

References 28 publications

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“…This means that a number of experts are required over and over again to reprogram these complex systems in accordance with evolving needs and standards. This is a time-consuming and expensive task [45], and such systems are hard to produce and difficult to maintain. The author [23] also concluded that manual integrations between APIs reduces the agility, and that inaccuracies in the integration may also lead to financial losses and unexpected delays in production.…”

Section: Application Integration Dealing With the Coordination And Cooperationmentioning

confidence: 99%

The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS

Margaria

Chaudhary

Guevara

et al. 2021

Lecture Notes in Computer Science

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With the heterogeneity of the industry 4.0 world, and more generally of the Cyberphysical Systems realm, the quest towards a platform approach to solve the interoperability problem is front and centre to any system and system-of-systems project. Traditional approaches cover individual aspects, like data exchange formats and published interfaces. They may adhere to some standard, however they hardly cover the production of the integration layer, which is implemented as bespoke glue code that is hard to produce and even harder to maintain. Therefore, the traditional integration approach often leads to poor code quality, further increasing the time and cost and reducing the agility, and a high reliance on the individual development skills. We are instead tackling the interoperability challenge by building a model driven/low-code Digital Thread platform that 1) systematizes the integration methodology, 2) provides methods and techniques for the individual integrations based on a layered Domain Specific Languages (DSL) approach, 3) through the DSLs it covers the integration space domain by domain, technology by technology, and is thus highly generalizable and reusable, 4) showcases a first collection of examples from the domains of robotics, IoT, data analytics, AI/ML and web applications, 5) brings cohesiveness to the aforementioned heterogeneous platform, and 6) is easier to understand and maintain, even by not specialized programmers. We showcase the power, versatility and the potential of the Digital Thread platform on four interoperability case studies: the generic extension to REST services, to robotics through the UR family of robots, to the integration of various external databases (for data integration) and to the provision of data analytics capabilities in R.

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Section: Application Integration Dealing With the Coordination And Cooperationmentioning

confidence: 99%

The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS

Margaria

Chaudhary

Guevara

et al. 2021

Lecture Notes in Computer Science

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“…With 5G networks still under development (Nordrum & Clark, 2017), other wireless technologies are being adopted in the meantime, leading to the need for networks' coexistence solutions (de Moura Leite et al, 2017). Furthermore, I4.0 requires the understanding of data heterogeneity in the context of CPSs integration (Jirkovsky et al, 2017;Matzler & Wollschlaeger, 2017) as well as the interoperability (Salminen & Pillai, 2007;Nilsson & Sandin, 2018) within the agent-based ecosystem (Kao & Chen, 2010) for unambiguous communication (Zhang et al, 2018), efficient collaboration (Olszewska, 2017), and cooperation (Hildebrandt et al, 2017). Thence, information and data used for smart manufacturing should follow a semantic standard (Macia-Perez et al, 2009) throughout the whole industrial environment.…”

Section: Challenges Of Industry 40mentioning

confidence: 99%

Ontologies for Industry 4.0

Ragavan

Khamis

Fiorini³

et al. 2019

The Knowledge Engineering Review

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The current fourth industrial revolution, or 'Industry 4.0' (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

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“…To fully exploit the potential of the IoT and Industry4.0, engineering resources should to a larger extent be focused on high-level benefits of interoperability and system integration [4]. Automated approaches to establish and maintain interoperability are needed to enable on-demand service composition and meet the demands for flexible production and high efficiency given the high complexity and diversity of automation systems driven by the rapid technological development [6]. Architectures similar to the model presented here have been independently developed by Maló [12] who describe architectures that allow for maximum interoperability.…”

Section: Related Work On Interoperability Solutionsmentioning

confidence: 99%

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

Nilsson

Sandin

Delsing

2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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Modern large-scale automation systems integrate thousands to hundreds of thousands of physical sensors and actuators. Demands for more flexible reconfiguration of production systems and optimization across different information models, standards and legacy systems challenge current system interoperability concepts. Automatic semantic translation across information models and standards is an increasingly important problem that needs to be addressed to fulfill these demands in a cost-efficient manner under constraints of human capacity and resources in relation to timing requirements and system complexity. Here we define a translator-based operational interoperability model for interacting cyber-physical systems in mathematical terms, which includes system identification and ontology-based translation as special cases. We present alternative mathematical definitions of the translator learning task and mappings to similar machine learning tasks and solutions based on recent developments in machine learning. Possibilities to learn translators between artefacts without a common physical context, for example in simulations of digital twins and across layers of the automation pyramid are briefly discussed.

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Semantic Interoperability in Industry 4.0: Survey of Recent Developments and Outlook

Cited by 52 publications

References 28 publications

The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS

The Interoperability Challenge: Building a Model-Driven Digital Thread Platform for CPS

Ontologies for Industry 4.0

Interoperability and machine-to-machine translation model with mappings to machine learning tasks

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