Data mining techniques for quality improvement of electron beam welding process

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creativework.publisherInstitute of Physicsen
dc.contributor.authorKolev G.
dc.contributor.authorAsenova-Robinzonova A.
dc.contributor.authorKoleva L.
dc.contributor.authorKoleva E.
dc.date.accessioned2024-07-10T14:27:06Z
dc.date.accessioned2024-07-10T14:51:07Z
dc.date.available2024-07-10T14:27:06Z
dc.date.available2024-07-10T14:51:07Z
dc.date.issued2024-01-01
dc.description.abstractBesides the fulfilment of the technological requirements for the geometry of the obtained welded joints by electron beam welding, there is a necessity to avoid the conditions, which more probably will lead to defect appearance. It is assumed that the appearance of defects is more probable under some regime conditions. For the modelling of the dependence of bivariate quality characteristics (such as the defect appearance) on the process parameters two different modelling approaches are applied and compared - logistic regression and neural networks. The implemented model-based approaches are compared and applied for the prediction of the defect appearance, depending on the variation of the electron beam process parameters.
dc.identifier.doi10.1088/1742-6596/2710/1/012039
dc.identifier.issn1742-6596
dc.identifier.issn1742-6588
dc.identifier.scopusSCOPUS_ID:85186137797en
dc.identifier.urihttps://rlib.uctm.edu/handle/123456789/907
dc.language.isoen
dc.source.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85186137797&origin=inward
dc.titleData mining techniques for quality improvement of electron beam welding process
dc.typeConference Paper
oaire.citation.issue1
oaire.citation.volume2710
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