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The microstructure of a fusion weld has great influences on the susceptibility of the base material to hot cracking and on the mechanical properties of the weld. Small, globulitic grains are necessary for increased strength, ductility and toughness and for a low inclination to hot cracking instead of large, oblong grains. This study reports on the factors which exert the main influences on such grain refinement in the weld. Thus, the influences of the thermal conditions, the chemical composition of the weld metal and the number and type of solidification nuclei on the microstructure were classified for the TIG welding of three different aluminium alloys.
An approach to develop an arc sensor for gap-width estimation during automated NG-GMAW with a weaving electrode motion is introduced by combining arc sensor readings with optical measurement of the groove shape to allow precise analyses of the process. The two test specimen welded for this study were designed to feature a variable groove geometry in order to maximize efficiency of the conducted experimental efforts, resulting in 1696 individual weaving cycle records with associated arc sensor measurements, process parameters and groove shape information. Gap width was varied from 18 to 25 mm and wire feed rates in the range of 9 to 13 m/min were used in the course of this study. Artificial neural networks were used as a modelling tool to derive an arc sensor for estimation of gap width suitable for online process control that can adapt to changes in process parameters as well as changes in the weaving motion of the electrode. Wire feed rate, weaving current, sidewall dwell currents and angles were used as inputs to calculate the gap width. Evaluation the proposed arc sensor model show very good estimation capabilities for parameters sufficiently covered during experiments.
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.
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.
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.
Die in dieser Studie vorgestellte einfache Probengeometrie zeigt sich in Verbindung mit der
angewandten Versuchs- und Auswertemethodik geeignet, um den umwandlungsplastischen (TRIP)-Parameter K unter Verwendung des thermomechanischen Simulators Gleeble® 3500 experimentell zu bestimmen.
Der TRIP-Parameter K wurde für die Grundwerkstoffe S355J2+N und S960QL, sowie für den hochlegierten Zusatzwerkstoff CN13-4 -IG bestimmt.
Die numerische Einflussanalyse zeigt, dass bei der Berechnung von (Oberflächen-)Schweißeigen-spannungen die Vernachlässigung des TRIP-Effektes kaum einen Einfluss auf die Längseigenspannungen hat. Im Gegensatz da zu resultieren gegenüber dem Referenzfall signifikante Abweichungen der
Quereigenspannungen im und um den Schweißnahtbereich.
Die Verdopplung des TRIP-Parameters K gegenüber dem Referenzfall hat nur einen vernachlässigbaren Einfluss auf die Längs- sowie Quereigenspannungen auf der Oberfläche.
Für eine qualitative und quantitative numerische Nachbildung der experimentell bestimmten Längs- und Quereigenspannungen ist die Berücksichtigung der Größenordnung des material-abhängigen TRIP-Parameters K notwendig.
The development within the offshore wind sector towards more powerful turbines combined with increasing water depth for new wind parks is challenging both the designer as well as the manufacturer of bottom fixed support structures. Besides XL-monopiles, the market developed an innovative and economic jacket support structure which is based on automatically manufactured tubular joints combined with standardized pipes. Besides the improvements for a serial manufacturing process the automatically welded tubular joints show a great potential in terms of fatigue resistance e.g. due to a smooth weld geometry without sharp notches. However, these benefits are not considered yet within the fatigue design process of automatically manufactured jacket substructures according to current standards due to the lack of suitable S-N curves. Therefore, 32 axial fatigue tests on single and double-sided automatically welded tubular X-joints have been performed to determine a new hot spot stress related S-N curve. Based on these constant amplitude fatigue tests a new S-N curve equal to a FAT 126 curve was computed which implicitly includes the benefits of the automatically welding procedure.
A combined experimental numerical approach is applied to determine the transformation induced plasticity (TRIP)-parameter K for different strength low-alloy steels of grade S355J2+N and S960QL as well as the super martensitic filler CN13-4-IG containing 13 wt% chromium and 4 wt% nickel. The thermo-physical analyses were conducted using a Gleeble® 3500 facility. The thermal histories of the specimens to be tested were extracted from corresponding simulations of a real gas metal arc weldment. In contrast to common TRIP-experiments which are based on complex specimens a simple flat specimen was utilized together with an engineering evaluation method. The evaluation method was validated with literature values for the TRIP-parameter. It could be shown that the proposed approach enables a correct description of the TRIP behavior.
This paper investigates gas metal arc welding (GMAW) with respect to energy consumption and its associated environmental impacts. Different material transfer modes and power levels for single wire GMAW (SGMAW) and tandem GMAW (TGMAW) are evaluated by means of the indicator electrical deposition efficiency. Furthermore, the wall-plug efficiency of the equipment is measured in order to describe the total energy consumption from the electricity grid. The results show that the energy efficiency is highly affected by the respective process and can be significantly enhanced by a TGMAW process. The wall-plug efficiency of the equipment shows no significant dependency on the power range or the material transfer mode. Moreover, the method of life cycle assessment (LCA) is adopted in order to investigate the influences of energy efficient welding on the environmental impacts. In the comparative LCA study, the demand of electrical energy is reduced up to 24%. In consequence, the indicator values for global warming potential (100), acidification potential, eutrophication potential, and photochemical ozone creation potential are reduced up to 11%.