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Methodology to improve applicability of welding simulation

  • The objective of this paper is to demonstrate a new simulation technique which allows fast and automatic generation of temperature fields as input for subsequent thermomechanical welding simulation. The basic idea is to decompose the process model into an empirical part based on neural networks and a phenomenological part that describes the physical phenomena. The strength of this composite modelling approach is the automatic calibration of mathematical models against experimental data without the need for manual interference by an experienced user. As an example for typical applications in laser beam and GMA-laser hybrid welding, it is shown that even 3D heat conduction models of a low complexity can approximate measured temperature fields with a sufficient accuracy. In general, any derivation of model fitting parameters from the real process adds uncertainties to the simulation independent of the complexity of the underlying phenomenological model. The modelling technique presentedThe objective of this paper is to demonstrate a new simulation technique which allows fast and automatic generation of temperature fields as input for subsequent thermomechanical welding simulation. The basic idea is to decompose the process model into an empirical part based on neural networks and a phenomenological part that describes the physical phenomena. The strength of this composite modelling approach is the automatic calibration of mathematical models against experimental data without the need for manual interference by an experienced user. As an example for typical applications in laser beam and GMA-laser hybrid welding, it is shown that even 3D heat conduction models of a low complexity can approximate measured temperature fields with a sufficient accuracy. In general, any derivation of model fitting parameters from the real process adds uncertainties to the simulation independent of the complexity of the underlying phenomenological model. The modelling technique presented hybridises empirical and phenomenological models. It reduces the model uncertainties by exploiting additional information which keeps normally hidden in the data measured when the model calibration is performed against few experimental data sets. In contrast, here the optimal model parameter set corresponding to a given process parameter is computed by means of an empirical submodel based on relatively large set of experimental data. The approach allows making a contribution to an efficient compensation of modelling inaccuracies and lack of knowledge about thermophysical material properties or boundary conditions. Two illustrating examples are provided.zeige mehrzeige weniger

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Metadaten
Autoren/innen:Andreas Pittner, D. Weiß, Christopher Schwenk, Michael Rethmeier
Dokumenttyp:Zeitschriftenartikel
Veröffentlichungsform:Verlagsliteratur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):Science and technology of welding and joining
Jahr der Erstveröffentlichung:2008
Herausgeber (Institution):Institute of Materials, Minerals and Mining
Verlag:Maney
Verlagsort:London
Jahrgang/Band:13
Ausgabe/Heft:6
Erste Seite:496
Letzte Seite:508
Freie Schlagwörter:GMA-laser hybrid welding; Global optimisation; Inverse heat conduction problem; Laser beam welding; Model prediction; Neural networks; Stochastic search method; Welding simulation
DOI:https://doi.org/10.1179/136217108X329322
ISSN:1362-1718
ISSN:1743-2936
Verfügbarkeit des Dokuments:Physisches Exemplar in der Bibliothek der BAM vorhanden ("Hardcopy Access")
Bibliotheksstandort:Sonderstandort: Publica-Schrank
Datum der Freischaltung:19.02.2016
Referierte Publikation:Ja
Datum der Eintragung als referierte Publikation:19.11.2008