Machine Learning Approach for Optimization of Automated Fiber Placement Processes

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dc.identifier.uri http://dx.doi.org/10.15488/1829
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/1854
dc.contributor.author Brüning, J.
dc.contributor.author Denkena, Berend
dc.contributor.author Dittrich, Marc-André
dc.contributor.author Hocke, T.
dc.date.accessioned 2017-09-07T11:22:18Z
dc.date.available 2017-09-07T11:22:18Z
dc.date.issued 2017
dc.identifier.citation Brüning, J.; Denkena, B.; Dittrich, M.-A.; Hocke, T.: Machine Learning Approach for Optimization of Automated Fiber Placement Processes. In: Procedia CIRP 66 (2017), S. 74-78. DOI: https://doi.org/10.1016/j.procir.2017.03.295
dc.description.abstract Automated Fiber Placement (AFP) processes are commonly deployed in manufacturing of lightweight structures made of carbon fibre reinforced polymer. In general, AFP is connected to individual manufacturing knowledge during process planning and time consuming manual quality inspections. In both cases, automatic solutions provide a high economic potential. Therefore, a machine learning approach for planning, optimizing and inspection of AFP processes is presented. Process data from planning, CNC and online process monitoring is aggregated for the documentation of the part specific manufacturing history and the automated generation of manufacturing knowledge. Within this approach a complete automation of data capturing, data storing, modeling and optimizing is achieved. eng
dc.description.sponsorship BMWi/ZIM KF2328125PO4
dc.language.iso eng
dc.publisher Amsterdam : Elsevier
dc.relation.ispartofseries Procedia CIRP 66 (2017)
dc.rights CC BY-NC-ND 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Assisted process planning eng
dc.subject Machine learning eng
dc.subject Process data visualization eng
dc.subject Artificial intelligence eng
dc.subject Automation eng
dc.subject Carbon eng
dc.subject Carbon fiber reinforced plastics eng
dc.subject Carbon fibers eng
dc.subject Composite materials eng
dc.subject Data visualization eng
dc.subject Fiber reinforced plastics eng
dc.subject Manufacture eng
dc.subject Process monitoring eng
dc.subject Automated generation eng
dc.subject Carbon fibre reinforced polymer eng
dc.subject Economic potentials eng
dc.subject Machine learning approaches eng
dc.subject Manufacturing knowledge eng
dc.subject On-line process monitoring eng
dc.subject Process data visualization eng
dc.subject Quality inspection eng
dc.subject Learning systems eng
dc.subject.ddc 620 | Ingenieurwissenschaften und Maschinenbau ger
dc.title Machine Learning Approach for Optimization of Automated Fiber Placement Processes eng
dc.type Article
dc.type Text
dc.relation.issn 00136848
dc.relation.doi https://doi.org/10.1016/j.procir.2017.03.295
dc.bibliographicCitation.volume 66
dc.bibliographicCitation.firstPage 74
dc.bibliographicCitation.lastPage 78
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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