TY - JOUR A1 - Pittner, Andreas A1 - Weiß, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael T1 - Methodology to improve applicability of welding simulation N2 - 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 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. KW - Welding simulation KW - GMA-laser hybrid welding KW - Laser beam welding KW - Neural networks KW - Global optimisation KW - Stochastic search method KW - Inverse heat conduction problem KW - Model prediction PY - 2008 U6 - https://doi.org/10.1179/136217108X329322 SN - 1362-1718 SN - 1743-2936 VL - 13 IS - 6 SP - 496 EP - 508 PB - Maney CY - London AN - OPUS4-18300 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas A1 - Weiss, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael ED - V.I. Makhnenko, T1 - Fast generation and prediction of welding temperature fields for multiple experiments N2 - The objective of this paper is to demonstrate a new simulation technique which allows the fast and automatic generation to temperature fields based on a combination of empirical and phenomenological modelling techniques. The automatic calibration of the phenomenological model is performed by a multi-variable global optimisation routine which yields the optimal fit between simulated and experimental weld charcteristics without the need for initial model parameters. For exemplary welding processes it is shown that linear 3D heat conduction models can approximate measured temperature fields with a high accuracy. The modelling approach presented comprises the automatic calibration against multiple experiments which permits simulating the temperature field for unknown process parameters. The validation of this composite simulation model is performed for exemplary welding processes and includes the prediction of the fusion line in the cross section and the corresponding thermal cycles. T2 - 4th International Conference - Mathematical modelling and information technologies in welding and related processes CY - Katsiveli, Crimea, Ukraine DA - 2008-05-27 KW - Welding simulation KW - GMA-laser hybrid welding KW - Laser beam welding KW - Neural networks KW - Global optimisation KW - Stochastic search method KW - Inverse heat conduction problem KW - Model prediction PY - 2008 SP - 134 EP - 140 CY - Kiev, Ukraine AN - OPUS4-19639 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -