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Parameter Forecasting for Vehicle Paint Quality Optimisation

dc.contributor.authorGursch, Heimo
dc.contributor.authorKörner, Stefan
dc.contributor.authorKrasser, Hannes
dc.contributor.authorKern, Roman
dc.contributor.editorWeyers, Benjamin
dc.contributor.editorDittmar, Anke
dc.date.accessioned2017-06-17T20:19:07Z
dc.date.available2017-06-17T20:19:07Z
dc.date.issued2016
dc.description.abstractPainting a modern car involves applying many coats during a highly complex and automated process. The individual coats not only serve a decoration purpose but are also curial for protection from damage due to environmental influences, such as rust. For an optimal paint job, many parameters have to be optimised simultaneously. A forecasting model was created, which predicts the paint flaw probability for a given set of process parameters, to help the production managers modify the process parameters to achieve an optimal result. The mathematical model was based on historical process and quality observations. Production managers who are not familiar with the mathematical concept of the model can use it via an intuitive Web-based Graphical User Interface (Web-GUI). The Web-GUI offers production managers the ability to test process parameters and forecast the expected quality. The model can be used for optimising the process parameters in terms of quality and costs.
dc.identifier.doi10.18420/muc2016-ws04-0003
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/263
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofMensch und Computer 2016 – Workshopband
dc.relation.ispartofseriesMensch und Computer
dc.titleParameter Forecasting for Vehicle Paint Quality Optimisation
dc.typeText/Conference Paper
gi.citation.publisherPlaceAachen
gi.conference.date4.-7. September 2016
gi.conference.locationAachen
gi.conference.sessiontitleSmart Factories
gi.document.qualitydigidocde_DE

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