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Organisationseinheit der BAM
Eingeladener Vortrag
- nein (1)
Grain refinement is an important possibility to enhance the mechanical properties such as strength, ductility and toughness of aluminium weld metal. In this study, grain refinement was achieved through the addition of commercial grain refiner Al Ti5B1 to gas tungsten arc weld metal of the aluminium alloys 1050A (Al 99.5) and 5083 (Al Mg4.5Mn0.7). The grain refiner additions led to a significant reduction of the weld metal mean grain size (Alloy 1050A, 86 %; Alloy 5083, 44 %) with a change in grain shape from columnar to equiaxed. Tensile tests showed for Alloy 5083 that the weld metal's ductility can be increased through grain refinement. No improvement in weld metal strength (i.e. yield strength and ultimate tensile strength) was observed. Furthermore, tear tests with notched specimens revealed that the resistance against initiation and propagation of cracks in the weld metal can be enhanced through grain refinement. The toughness was observed to increase clearly by grain refinement in weld metal of commercial pure Al (Alloy 1050A). In Alloy 5083 weld metal, the toughness was not improved through grain refinement, likely because of a semi-continuous network of brittle intermetallic phases that facilitate crack propagation.
Modeling of gas metal arc welding process using an analytically determined volumetric heat source
(2013)
High peak temperature and continuous deposition of electrode droplets in the weld puddle inhibit real-time monitoring of thermal cycles and bead dimensions in gas metal arc welding. A three-dimensional numerical heat transfer model is presented here to compute temperature field and bead dimensions considering a volumetric heat source to account for the transfer of arc energy into the weld pool. The heat source dimensions are analytically estimated as function of welding conditions and original joint geometry. The deposition of electrode material is modeled using deactivation and activation of discrete elements in a presumed V-groove joint geometry. The computed values of bead dimensions and thermal cycles are validated with the corresponding measured results. A comparison of the analytically estimated heat source dimensions and the corresponding numerically computed bead dimensions indicate that the former could rightly serve as the basis for conduction heat transfer based models of gas metal arc welding process.
Refinement of the weld metal grain structure can improve the mechanical properties of the weld and decrease the susceptibility to solidification cracking of the weld metal. In this study, commercial Al Ti5B1 grain refiner was used to refine the microstructure of LB (laser beam) and GTA (gas tungsten arc) aluminum welds by inoculation. The grain refiner additions led to a significant decrease in the weld metal mean grain size whereby a transition from columnar to equiaxed grain structure (Columnar to Equiaxed Transition, CET) was observed. The development of both grain size and shape depended upon the base metal (Al alloys 1050A, 5083 and 6082) and upon the welding process. The GTA welding process allowed a more pronounced and a more efficient refinement than in LB welds. Furthermore, the influence of the solidification conditions on the CET was investigated through temperature measurements in the weld metal. The temperature profiles revealed a faster solidification of LB welds than in GTA welds. The results from the temperature measurements were also used to estimate (according to an existing model) the critical thermal gradient at which the CET occurs.
Influence of solute content and solidification parameters on grain refinement of aluminum weld metal
(2013)
Grain refinement provides an important possibility to enhance the mechanical properties (e.g., strength and ductility) and the weldability (susceptibility to solidification cracking) of aluminum weld metal. In the current study, a filler metal consisting of aluminum base metal and different amounts of commercial grain refiner Al Ti5B1 was produced. The filler metal was then deposited in the base metal and fused in a GTA welding process. Additions of titanium and boron reduced the weld metal mean grain size considerably and resulted in a transition from columnar to equiaxed grain shape (CET). In commercial pure aluminum (Alloy 1050A), the grain-refining efficiency was higher than that in the Al alloys 6082 and 5083. Different welding and solidification parameters influenced the grain size response only slightly. Furthermore, the observed grain-size reduction was analyzed by means of the undercooling parameter P and the growth restriction parameter Q, which revealed the influence of solute elements and nucleant particles on grain size.
Fast temperature field generation for welding simulation and reduction of experimental effort
(2011)
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 minimize 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 discretization 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 optimization 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.
Austenite grain growth during welding is a critical factor for controlling weld microstructure in addition to nominal composition and thermal cycles. Recently, experimental data suggesting a decrease in martensite start temperature with a decrease in prior austenite grain size has been published. However, the actual sensitivity of this phenomenon on residual stresses evolution in the heat-affected zone has not been investigated, yet. Therefore, a numerical model was modified to consider this phenomenon. Numerical simulations were performed for welding of a low-alloy structural steel with minimum yield strength of 355 MPa (S355J2+N) and a heat-resistant steel P91 or 9Cr–1Mo, respectively. The results clarify the influence of prior austenite grain size on the residual stress development and show the importance martensite transformation temperatures and final martensite fraction. Consequently, the residual stress evolution of P91, which completely transforms to martensite while cooling, based on the enhanced model leads to maximum stress differences of 200 MPa in the heat-affected zone.
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.