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A k-nearest neighbour technique for experience-based adaptation of assembly stations
Ratchev, S.M.
Ratchev, S.M.
Publication Date
2014-01
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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.
Peer-Reviewed
Yes
Open Access status
openAccess
Accepted for publication
2014-06-27
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Abstract
We 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.
Version
Accepted manuscript
Citation
Scrimieri 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.
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Article