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A Jumpstart Framework for Semantically Enhanced OPC-UA

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A Correction to this article was published on 29 April 2019

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Abstract

Decentralization is the norm of future smart production as it assists in contextual dynamic decision-making and thereby increases the flexibility required to produce highly customized products. When manufacturing business software is operated as a cloud based solution, it experiences network latency and connectivity issues. To overcome these problems, the production control should be delegated to the manufacturing edge layer and hence, the argument of decentralization is even more applicable to this narrative. In order to accomplish the assigned manufacturing task effectively, the edge layer is required to possess contextual awareness to make run-time decisions in production. Semantic technologies, on the other hand, assist in discerning the meaning, reasoning and drawing inferences from the data. There are several specifications and frameworks to automate the discovery, orchestration and invocation of web services; the prominent are OWL-S, SAWSDL and WSMO. This paper derives a hybrid approach that integrates OWL-S and SAWSDL specifications to overcome the downsides, yet retain the benefits of both approaches to the OPC-UA application methods. Consequently, the proposed semantically enriched OPC-UA concept enables the edge layer to create flexible production orchestration plans in a manufacturing scenario controlled by cloud MES. Furthermore, the derived hybrid approach is applied to a real use case to demonstrate its feasibility in industrial environments.

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  • 29 April 2019

    The original article can be found.

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Correspondence to Badarinath Katti.

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This research work is supported by a doctoral grant from SAP SE. A non-provisional patent, with ID 15/926,856, on the research of CMES controlled edge manufacturing has been filed in USA on 20-Mar-2018 with the authors as the inventors.

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Katti, B., Plociennik, C. & Schweitzer, M. A Jumpstart Framework for Semantically Enhanced OPC-UA. Künstl Intell 33, 131–140 (2019). https://doi.org/10.1007/s13218-019-00579-0

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