An ontology-based semantic foundation for flexible manufacturing systems

Uddin, M. Kamal; Dvoryanchikova, A.; Lobov, Andrei; Lastra, José L. Martínez

doi:10.1109/iecon.2011.6119276

Cited by 16 publications

(7 citation statements)

References 15 publications

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“…In contrast to our approach, they address only UC1 and UC2 and do not propose an engineering methodology on how to use their domain ontology. In [5], a domain ontology model is presented that captures and formally represents the manufacturing semantics from heterogeneous data sources. However, they are not taking into account the various roles of engineers that occur during the modeling task and considered mainly UC2 and UC6.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-based integration of industrial plant models

Abele

Grimm

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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The planning and engineering of industrial plants is characterized by a heterogeneous landscape of tools and data formats covering multiple engineering aspects, such as electrical engineering, mechanical engineering, software engineering, etc. To provide plant engineers in different roles with an integrated view on engineering data, we propose a conceptual modelling approach based on MOF layering for representing plant knowledge across multiple disciplins. Furthermore, we suggest to instantiate this conceptual modelling approach on a semantic technology stack featuring semantic data representation based on RDF and web-based plant navigation in a semantic Wiki.

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Section: Related Workmentioning

confidence: 99%

Knowledge-based integration of industrial plant models

Abele

Grimm

2013

IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society

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“…In (Yang et al, 2005) paper it is described a Web Service Oriented Architecture (SOA) and platform that uses semantics and specifically OWL-S for automatic integration of manufacturing systems. It focuses on the dynamic discovery, selection of web services and other business processes that can be described using OWL-S. Another approach is presented in (Uddin et al, 2011), which focuses on the ontology and formal description of data sources using a domain ontology. Finally the (Chondrogiannis et al, 2011) and (Martin et al, 2008) approaches are accomplishing interoperability of heterogeneous medical information systems by transforming SPARQL queries to the different clinical ontologies and code systems that they have linked as well as different database schemas that are mapped to domain ontologies.…”

Section: Related Workmentioning

confidence: 99%

Using Semantic Technologies for More Intelligent Steel Manufacturing

Matskanis

Mouton

Ebel

et al. 2015

Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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In recent years, the steel industry has significantly raised its demands regarding product quality, optimization of production cost, environmental issues and lead-time. The demand for improved production performance has in turn increased the demand on information systems, in particular highlighting the need for improved factory-and company-wide collaboration and information exchange. The heterogeneity in structure, technology and architecture of the information systems deployed in manufacturing plants presents further challenges to the design and implementation of a data exchange system for process optimization.

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“…The potential advantages of bridging these two domains of context‐sensitive computing and FMS are mainly to facilitate manufacturing knowledge management between different design tools and contextual knowledge exchange in a complex, adaptive environment through support applications. Manufacturing context modeling, run‐time context extraction and context management remain in the core of such research, and ontologies are considered as the catalyst for context modeling, formal knowledge representation (KR), sharing and mediation in distributed environments (Uddin et al , 2011). Ontology‐based context‐sensitive computing to FMS aims to improve the overall transparency of the complex machines/operations and automatic integration of dynamic changes, providing intelligence to the lower levels of the factory, facilitating optimized decision making at runtime.…”

Section: Introductionmentioning

confidence: 99%

Ontology‐based context‐sensitive computing for FMS optimization

Uddin

Puttonen

Scholze

et al. 2012

Assembly Automation

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2014),"Maximizing agreement on diverse ontologies with "wisdom of crowds" relation classification", Online Information Review, Vol. 38 Iss 5 pp. 616-633 http://dx.If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -The purpose of this paper is to present an ontology-based approach of context-sensitive computing for the optimization of flexible manufacturing systems (FMS). Design/methodology/approach -A context-sensitive computing approach is presented, integrated on top of FMS control platform. The approach addresses how to extract manufacturing contexts at source, how to process contextual entities by developing an ontology-based context model and how to utilize this approach for real time decision making to optimize the key performance indicators (KPIs). A framework for such an optimization support system is proposed. A practical FMS use case within SOA-based control architecture is considered as an illustrative example and the implementation of the core functionalities to the use case is reported. Findings -Continuous improvement of the factory can be enhanced utilizing context-sensitive support applications, which provides an intelligent interface for knowledge acquisition and elicitation. This can be used for improved data analysis and diagnostics, real time feedback control and support for optimization.Research limitations/implications -The performance of context-sensitive computing increases with the extraction, modeling and reasoning of as much contexts as possible. However, more computational resources and processing times are associated to this. Hence, the trade-off should be in between the extent of context processing and the required outcome of the support applications. Practical implications -This paper includes the practical implications of context-sensitive applications development in manufacturing, especially in the dynamic operating environment of FMS. Originality/value -Reported results provide a modular approach of context-sensitive computing and a practical use case implementation to achieve context awareness in FMS. The results are seen extendable to other manufacturing domains.

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An ontology-based semantic foundation for flexible manufacturing systems

Cited by 16 publications

References 15 publications

Knowledge-based integration of industrial plant models

Knowledge-based integration of industrial plant models

Using Semantic Technologies for More Intelligent Steel Manufacturing

Ontology‐based context‐sensitive computing for FMS optimization

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