Semantic modeling for collaboration and cooperation of systems in the production domain

Hildebrandt, Constantin; Scholz, André; Fay, Alexander; Schröder, Tizian; Hadlich, Thomas; Diedrich, Christian; Dubovy, Martin; Eck, Christian; Wiegand, Ralf

doi:10.1109/etfa.2017.8247585

Cited by 45 publications

(14 citation statements)

References 16 publications

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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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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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“…Usman [11] emphasizes the combination of design and manufacturing features. Semantische Allianz für Industrie 4.0 (SemAnz40) helps to exchange product and process information in the context of smart factories [13]. This supports cooperation and collaboration of production systems.…”

Section: Domain-specific Knowledge Basesmentioning

confidence: 99%

“…For UFO only a specification including some descriptions is available online. 13 A full description of the included classes and properties is given in [60].…”

Section: Top-level Ontologiesmentioning

confidence: 99%

Applying knowledge bases to make factories smarter

Ocker

Paredis

Vogel‐Heuser

2019

At - Automatisierungstechnik

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Knowledge Bases (KBs) enable engineers to capture knowledge in a formalized way. This formalization allows us to combine knowledge, thus creating the basis for smart factories while also supporting product and production system design. Building comprehensive and reusable KBs is still a challenge, though, especially for knowledge-intensive domains like engineering and production. To cope with the sheer amount of knowledge, engineers should reuse existing KBs. This paper presents a comprehensive overview of domain-specific KBs for production and engineering, as well as generic top-level ontologies. The application of such top-level ontologies offers new insights by integrating knowledge from various domains, stakeholders, and companies. To bridge the gap between top-level ontologies and existing domain KBs, we introduce an Intermediate Engineering Ontology (IEO).

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“…R21. Semantic models: Models are needed that describe the requirements of products (regarding their production steps) and the capabilities of production resources in a semantically unambiguous way, so that control algorithms can select suitable production resources for the production of individual products (Hildebrandt, Scholz et al 2017). R22.…”

Section: R15 Big Datamentioning

confidence: 99%

“…finding and combining appropriate manufacturing resources to achieve a given production goal in a dynamic manufacturing environment. Hildebrandt, Scholz et al (2017) show the advantage of including existing semantically-rich standards, such as eCl@ss, into these engineering tasks.…”

Section: Semantic Models Of Factoriesmentioning

confidence: 99%

An overview of current technologies and emerging trends in factory automation

Dotoli

Fay

Miśkowicz

et al. 2018

International Journal of Production Research

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In this paper we provide an overview of recent theoretical approaches and technologies that respond to the fundamental challenges of modern factory automation. We classify these major methods and technologies into several groups and, for seven of them-namely: vertical integration of factory automation systems; distributed and decentralised control, smart sensors and actuators in factories; networked control systems and wireless sensors and actuators; autonomy and selforganisation of factories; advanced sensing for factory automation; semantic models of factories; engineering methods of factory automation systems-we report recent research contributions and formulate open technical problems in the domain of modern factory automation.

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Semantic modeling for collaboration and cooperation of systems in the production domain

Cited by 45 publications

References 16 publications

Ontologies for Industry 4.0

Ontologies for Industry 4.0

Applying knowledge bases to make factories smarter

An overview of current technologies and emerging trends in factory automation

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