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Schweißen mit dem Faserlaser
(2007)
Schweißen mit dem Faserlaser
(2007)
Laser Plasma Hybrid Welding of Austenitic Stainless Steels - Phenomena of Process Instability
(2007)
Laser plasma hybrid welding has been proved to be a very stable hybrid welding process and
welds of high quality can be produced, especially if high surface quality and low spattering is
demanded such as in welding fabrication of high alloyed austenitic stainless steels.
In particular cases, even though welds display high outer quality, X-ray examinations
revealed weld defects which may range from low porosity to blowhole-like cavities. The
phenomena and the main influencing parameters such as arc current, welding speed and
focal point position are discussed. Parameter fields will be suggested for welding plates of
different austenitic stainless steel grades with thicknesses ranging from 3 to 8 mm. The
results are based on welding experiments carried out using a 4.4 kW diode pumped Nd:YAGlaser.
Thus, also the influence of the feeding fibre diameter has been investigated and it was found
that the resulting beam shape has a major effect on the welding performance.
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.