Reconstruction of Physical Correlations Using Symbolic Regression

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DC Field	Value	Language
dc.contributor.author	Stautner, Stautner	de
dc.contributor.author	Weinert, Klaus	de
dc.date.accessioned	2004-12-07T08:20:57Z	-
dc.date.available	2004-12-07T08:20:57Z	-
dc.date.created	2001	de
dc.date.issued	2002-04-05	de
dc.identifier.uri	http://hdl.handle.net/2003/5415	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15286	-
dc.description.abstract	Modeling the particle ow mechanisms in orthogonal cutting of turning processes is a vital task in mechanical engineering. This paper presents a new approach that differs from techniques like finite element analyzes (FEA) or molecular dynamics (MD). Using symbolic regression, a genetic programming system evolves mathematical formulae that describe the trajectories of single particles of steel recorded during the turning process by a high-speed camera.	en
dc.format.extent	1551752 bytes	-
dc.format.mimetype	application/pdf	-
dc.language.iso	en	de
dc.publisher	Universität Dortmund	de
dc.relation.ispartofseries	Reihe Computational Intelligence ; 116	de
dc.subject.ddc	004	de
dc.title	Reconstruction of Physical Correlations Using Symbolic Regression	en
dc.type	Text	de
dc.type.publicationtype	report	-
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 531

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