An intelligent process model: predicting springback in single point incremental forming

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dc.contributor.authorKhan, Muhamad S.
dc.contributor.authorCoenen, Frans
dc.contributor.authorDixon, Clare
dc.contributor.authorEl-Salhi, Subhieh
dc.contributor.authorPenalva, Mariluz
dc.contributor.authorRivero, Asun
dc.contributor.institutionFABRIC_INTEL
dc.contributor.institutionSG
dc.date.accessioned2024-07-24T12:08:32Z
dc.date.available2024-07-24T12:08:32Z
dc.date.issued2015-02
dc.descriptionPublisher Copyright: © 2014, Springer-Verlag London.
dc.description.abstractThis paper proposes an intelligent process model (IPM), founded on the concept of data mining, for predicting springback in the context of sheet metal forming, in particular, single point incremental forming (SPIF). A limitation with the SPIF process is that the application of the process results in geometric deviations, which means that the resulting shape is not necessarily the desired shape. Errors are introduced in a nonlinear manner for a variety of reasons, but a contributor is the geometry of the desired shape. A local geometry matrix (LGM) representation is used that allows the capture of local geometries in such a way that they are suited to input to a classifier generator. It is demonstrated that a rule-based classifier can be used to train the classifier and generate a classification model. The resulting model can then be used to predict errors with respect to new shapes so that some correction strategy can be applied. The reported evaluation of the proposed IPM indicates that very promising results can be obtained with regard to reducing the shape deviations due to springback.en
dc.description.statusPeer reviewed
dc.format.extent12
dc.identifier.citationKhan , M S , Coenen , F , Dixon , C , El-Salhi , S , Penalva , M & Rivero , A 2015 , ' An intelligent process model : predicting springback in single point incremental forming ' , International Journal of Advanced Manufacturing Technology , vol. 76 , no. 9-12 , pp. 2071-2082 . https://doi.org/10.1007/s00170-014-6431-1
dc.identifier.doi10.1007/s00170-014-6431-1
dc.identifier.issn0268-3768
dc.identifier.urihttps://hdl.handle.net/11556/3879
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=84925513574&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofInternational Journal of Advanced Manufacturing Technology
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsData mining
dc.subject.keywordsSingle point incremental forming
dc.subject.keywordsSpringback correction
dc.subject.keywordsControl and Systems Engineering
dc.subject.keywordsSoftware
dc.subject.keywordsMechanical Engineering
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsIndustrial and Manufacturing Engineering
dc.titleAn intelligent process model: predicting springback in single point incremental formingen
dc.typejournal article
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