Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System

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dc.identifier.uri http://dx.doi.org/10.15488/1046
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1070
dc.contributor.author Busch, Jan
dc.contributor.author Blankemeyer, Sebastian
dc.contributor.author Raatz, Annika
dc.contributor.author Nyhuis, Peter
dc.date.accessioned 2017-01-12T09:06:27Z
dc.date.available 2017-01-12T09:06:27Z
dc.date.issued 2016
dc.identifier.citation Busch, J.; Blankemeyer, S.; Raatz, A.; Nyhuis, P.: Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System. In: Procedia CIRP 44 (2016), S. 79-84. DOI: http://dx.doi.org/10.1016/j.procir.2016.02.081
dc.description.abstract An active aerodynamic feeding system developed at the IFA offers a large potential regarding output rate, reliability and neutrality towards part geometries. In this paper, the procedure of a genetic algorithm's into the feeding system's control is shown. The genetic algorithm automatically identifies optimal values for the feeding system's parameters which need to be adjusted when setting up for new workpieces. The general functioning of the automatic parameter identification is confirmed during tests on the convergence behaviour of the genetic algorithm. Thereby, a trade-off between the adjustment time of the feeding system and the solution quality is revealed. © 2016 The Authors. eng
dc.description.sponsorship DFG/NY 4/51-1
dc.language.iso eng
dc.publisher Amsterdam : Elsevier B.V.
dc.relation.ispartofseries Procedia CIRP 44 (2016)
dc.rights CC BY-NC-ND 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Algorithm eng
dc.subject Assembly eng
dc.subject Optimisation eng
dc.subject Aerodynamics eng
dc.subject Algorithms eng
dc.subject Assembly eng
dc.subject Economic and social effects eng
dc.subject Feeding eng
dc.subject Genetic algorithms eng
dc.subject Materials handling equipment eng
dc.subject Optimization eng
dc.subject Adjustment time eng
dc.subject Aerodynamic feeding eng
dc.subject Feeding system eng
dc.subject Optimal values eng
dc.subject Optimisations eng
dc.subject Part geometry eng
dc.subject Self-learning eng
dc.subject Solution quality eng
dc.subject Parameter estimation eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 600 | Technik ger
dc.title Implementation and Testing of a Genetic Algorithm for a Self-learning and Automated Parameterisation of an Aerodynamic Feeding System eng
dc.type Article
dc.type Text
dc.relation.issn 2212-8271
dc.relation.doi https://doi.org/10.1016/j.procir.2016.02.081
dc.bibliographicCitation.volume 44
dc.bibliographicCitation.firstPage 79
dc.bibliographicCitation.lastPage 84
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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