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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.
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.
Fast temperature field generation for welding simulation and reduction of experimental effort
(2009)
The quality of welding processes is governed by the occurring induced distortions yielding an
increase in production costs due to necessary reworking. Especially for more complex specimens
it is difficult to evaluate the optimal configuration of welding sequences in order to
minimise the distortion. Even experienced welding operators can solve this task only by trial
and error which is time and cost consuming.
In modern engineering the application of welding simulation is already known to be able to
analyse the heat effects of welding virtually. However, the welding process is governed by
complex physical interactions. Thus, recent weld thermal models are based on many simplifications.
The state of the art is to apply numerical methods in order to solve the transient heat
conduction equation. Therefore, it is not possible to use the real process parameters as input
for the mathematical model. The model parameters which allow calculating a temperature
field that is in best agreement with the experiments cannot be defined directly but inversely by
multiple simulations runs. In case of numerical simulation software based on finite discretisation
schemes this approach is very time consuming and requires expert users. The weld
thermal model contains an initial weakness which has to be adapted by finding an optimal set
of model parameters. This process of calibration is often done against few experiments. The
range of model validity is limited. An extension can be obtained by performing a calibration
against multiple experiments.
The focus of the paper is to show a combined modelling technique which provides an efficient
solution of the inverse heat conduction problem mentioned above. On the one hand the inverse
problem is solved by application of fast weld thermal models which are closed form
solutions of the heat conduction equation. In addition, a global optimisation algorithm allows
an automated calibration of the weld thermal model. This technique is able to provide a temperature
field automatically that fits the experimental one with high accuracy within minutes
on ordinary office computers. This fast paradigm permits confirming the application of welding
simulation in an industrial environment as automotive industry.
On the other hand, the initial model weakness is compensated by calibrating the model
against multiple experiments. The unknown relationship between model and process parameters
is approximated by a neural network. The validity of the model is increased successively
and enables to decrease experimental effort. For a test case it is shown, that this approach
yields accurate temperature fields within very short amount of time for unknown process parameters
as input data to the model contributing to the requirement to construct a substitute
system of the real welding process.
Analytical and numerical methods are used to estimate the temperature field due to the heat effects of welding. Numerical techniques are more adapted for industrial complex applications where analytical solutions do not exist yet. However, computational time is much lower with analytical models and a combination of both methods is investigated. Therefore, the two approaches are introduced and confronted in this paper. The finite-element software Ansys has been used for numerical simulations and Scilab for analytical simulations. In order to get a similar result quality, both methods have to be analysed and compared with respect to boundary conditions. These configurations are presented in this paper. Before starting any analysis, the analytical and numerical models have to be comparable. For the numerical simulation, every in- or output is given in discrete form and, for the analytical simulation, in continuous form. Thus, an analysis of the energy input distribution in both models is compulsory to ensure that the same amount of energy is applied. After this first study, a comparison of the analytical and numerical temperature field simulation is done from a fix point source in an infinite volume in steady state to a moving point source in a finite dimension in a transient state. A good agreement between the analytical and the numerical simulation results is found. However, some techniques, like a consideration of an image heat source for the analytical model or the selection of boundary conditions for the numerical model, need to be taken into consideration when the degree of complexity of the study (finite dimension or cooling time) increases. The limit of the comparison is reached when the geometry becomes too complex and when the effect of variable thermal properties with temperature cannot be neglected.
Continuous cooling transformation (CCT) behavior affects the transient state of
material properties employed in a numerical welding simulation, having a direct influence
on the developing stress state. Three different CCT diagrams for S355J2 steel
were employed to understand the influence of variations in CCT behavior on the numerical
calculation of welding-induced residual stresses. The CCT diagrams were
constructed from transformation data contained in the Sysweld software database,
measured dilatometric data from Gleeble experiments, and transformation data calculated
from the JMatPro software. The calculated transverse and longitudinal residual
stress distributions provided a qualitative correction only in comparison to experimental
measurements, with the largest deviation occurring near the weld
interface. Overall, the results indicate a weak dependency of the calculated residual
stresses due to anticipated CCT variations. The most significant effect on the calculated
residual stresses was shown to be related to the proportion of formed martensite.
It is suggested that CCT data of approximate accuracy is sufficient for reliable
calculation of welding-induced residual stresses.