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dc.contributor.authorScrimieri, Daniele
dc.contributor.authorRatchev, S.M.
dc.date.accessioned2020-03-04T16:59:51Z
dc.date.accessioned2020-03-16T09:57:13Z
dc.date.available2020-03-04T16:59:51Z
dc.date.available2020-03-16T09:57:13Z
dc.date.issued2014-01
dc.identifier.citationScrimieri D, Ratchev SM (2014) A k-Nearest Neighbour Technique for Experience-Based Adaptation of Assembly Stations. Journal of Control, Automation and Electrical Systems. 25 (6): 679-688.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17725
dc.descriptionYesen_US
dc.description.abstractWe present a technique for automatically acquiring operational knowledge on how to adapt assembly systems to new production demands or recover from disruptions. Dealing with changes and disruptions affecting an assembly station is a complex process which requires deep knowledge of the assembly process, the product being assembled and the adopted technologies. Shop-floor operators typically perform a series of adjustments by trial and error until the expected results in terms of performance and quality are achieved. With the proposed approach, such adjustments are captured and their effect on the station is measured. Adaptation knowledge is then derived by generalising from individual cases using a variant of the k-nearest neighbour algorithm. The operator is informed about potential adaptations whenever the station enters a state similar to one contained in the experience base, that is, a state on which adaptation information has been captured. A case study is presented, showing how the technique enables to reduce adaptation times. The general system architecture in which the technique has been implemented is described, including the role of the different software components and their interactions.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1007/s40313-014-0142-6en_US
dc.rights© 2014 Brazilian Society for Automatics–SBA. This is a post-peer-review, pre-copyedit version of an article published in Journal of Control, Automation and Electrical Systems. The final authenticated version is available online at: https://doi.org/10.1007/s40313-014-0142-6 from Springer website.
dc.subjectArchitecturesen_US
dc.subjectAssemblyen_US
dc.subjectk-nearest neighbour algorithmen_US
dc.subjectKnowledge-based systemsen_US
dc.titleA k-nearest neighbour technique for experience-based adaptation of assembly stationsen_US
dc.status.refereedYesen_US
dc.date.Accepted2014-06-27
dc.date.application2014-07-22
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2020-03-04T16:59:52Z
refterms.dateFOA2020-07-10T11:44:49Z


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