Full metadata record
DC FieldValueLanguage
dc.contributor.authorStiemer, M.-
dc.contributor.authorNezhi, Z.-
dc.contributor.authorRathjen, K.-
dc.contributor.authorZazai, F.-
dc.contributor.authorHagel, M.-
dc.contributor.authorRozgic, M.-
dc.date.accessioned2022-01-11T15:36:24Z-
dc.date.available2022-01-11T15:36:24Z-
dc.date.issued2021-10-14-
dc.identifier.urihttp://hdl.handle.net/2003/40660-
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-22518-
dc.description.abstractIn this work, approaches to the identification of high speed forming processes, whose simu lation requires models from different parts of physics are discussed. Particularly emphasis is laid on situations in which it is possible to break off the coupling and to profit from partial solutions for the design of the whole process. Such situations arise if it is possible to select relevant features that allow for a stable transfer of information between the different models. Creating situations in which a sequential approach to a coupled problem is favourably pos sible requires a profound process understanding. As an example, an electromagnetic form ing process is considered here. Approaches at identifying a coil geometry for electromag netic forming are discussed in case of an exemplary case involving the definition of a suitable feature-list and the study of several methods to tackle the electromagnetic subproblem, in cluding Nelder Mead Simplex Search, a combination of it with a neural network as surrogate model, and optimization via a neural network. These approaches are compared to each other, and quantitative results are given.en
dc.language.isoen-
dc.relation.ispartof9th International Conference on High Speed Formingen
dc.subjectmetal formingen
dc.subjectmachine learningen
dc.subjectfinite element methoden
dc.subjectdesign optimizationen
dc.subject.ddc620-
dc.subject.ddc670-
dc.titleNumerical Identification of Design Parameters for Electromagnetic Formingen
dc.typeText-
dc.type.publicationtypeconferenceObject-
dcterms.accessRightsopen access-
eldorado.secondarypublicationfalse-
Appears in Collections:ICHSF 2021

Files in This Item:
File Description SizeFormat 
11_Stiemer et al.pdfDNB540.73 kBAdobe PDFView/Open


This item is protected by original copyright



This item is protected by original copyright rightsstatements.org