Service oriented computing to Self-Learning production system

Uddin, M. Kamal; Dvoryanchikova, A.; Lastra, José L. Martínez; Scholze, Sebastian; Stokić, Dragan; Cândido, Gonçalo; Barata, José

doi:10.1109/indin.2011.6034875

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…1) [16] is constituted by two main components: the Extractor and the Adapter operating in a cooperative manner. These two together are responsible for identifying the current context under which the production system is operating (Extractor), and adapt the production system behavior with the purpose of improving its performance in face of contextual change (Adapter).…”

Section: Architecturementioning

confidence: 99%

“…Since the system response must take into account not only the particular context, but most important, the entire lifecycle behavior of both system and expert, a Learning Module has been provided, containing a set of machine learning algorithms capable for extracting patterns and regularities from gathered contextual data and operator decisions over time in [16] order to create a representative model of the process used to predict its future behaviour. All processed data and knowledge generated are stored in Data Access Layer repositories for continuous evolution and evaluation.…”

Section: Architecturementioning

confidence: 99%

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Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Cândido

Orio

Barata

et al. 2013

2013 IEEE International Symposium on Industrial Electronics

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In an increasingly globalised environment, modern manufacturing companies struggle to improve their integrated monitoring and control solutions with respect to total cost of ownership by reducing down-times during production, improving system performances and throughput and enabling for a faster fault detection as well as reducing energy consumption.The research currently done under the scope of Self-Learning Production Systems (SLPS) tries to fill this gap by providing an innovative and integrated approach for developing more intelligent monitoring and control solutions. This paper introduces the research background and describes the generic SLPS architecture with the main focus on the Adapter component, which represents the entity responsible for adapting the system behaviour based on the available contextual information. The envisioned Adapter architecture as well as the generic Adaptation Process are introduced.The proposed solution was then employed to manage the energy consumed by machine tools in CNC machines, taking into account machine idle times patterns. Both the objectives and approach of this application scenario are presented, as well as the Adapter role whenever the system needs to be adapted to improve the manufacturing line sustainability.

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Section: Architecturementioning

confidence: 99%

Section: Architecturementioning

confidence: 99%

Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Cândido

Orio

Barata

et al. 2013

2013 IEEE International Symposium on Industrial Electronics

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“…The research motivation behind this work relates with the strategic objective of strengthening EU leadership in production technologies in the global marketplace by developing innovative self-learning solutions to enable tight integration of control and maintenance of production systems [7]. This approach requires a paradigm shift in production systems domain aiming to allow adaptation and merging the world of control with other manufacturing activities of the production systems so-called secondary.…”

Section: Self-learning Production Systemsmentioning

confidence: 99%

Adapter for Self-Learning Production Systems

Cândido

Orio

Barata

et al. 2012

Technological Innovation for Value Creation

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Part 7: Smart SystemsInternational audienceTo face globalization challenges, modern production companies need to integrate the monitoring and control of secondary processes into shop floor core system to remain competitive and improve system performance and throughput. The research currently being done under the scope of Self-Learning Production Systems tries to fill this gap. Current work introduces the domain and a generic architecture, while focus over the responsible element for executing system adaptations according to current context: the Adapter. The Adapter architecture and its components are introduced as well as the generic Adaptation process. Early prototype scenarios applied to concrete real-world scenarios are also presented

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“…industrial robots, hydraulic components, aircraft parts) for automotive industries is considered in this work. The control system architecture is based on SOA principles (Uddin et al , 2011), where all the production relevant entities offer WSs to a Microsoft.Net‐based control platform. A control application software runs the FMS in real‐time invoking data from available services (WS Description Language (WSDL) files).…”

Section: Ontology‐based Context‐sensitive Computing: An Fms Use Casementioning

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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Service oriented computing to Self-Learning production system

Cited by 6 publications

References 8 publications

Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Adapter for Self-Learning Production Systems

Ontology‐based context‐sensitive computing for FMS optimization

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Service oriented computing to Self-Learning production system

Cited by 6 publications

References 8 publications

Self-Learning Production Systems (SLPS) &#x2014; Energy management application for machine tools

Self-Learning Production Systems (SLPS) &#x2014; Energy management application for machine tools

Adapter for Self-Learning Production Systems

Ontology‐based context‐sensitive computing for FMS optimization

Contact Info

Product

Resources

About

Self-Learning Production Systems (SLPS) — Energy management application for machine tools

Self-Learning Production Systems (SLPS) — Energy management application for machine tools