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In various applications, welding-induced residual stresses have a substantial impact on the integrity of welded constructions. Tensile residual stress can promote stress-corrosion cracking, brittle fracture, and reduces the fatigue life in service, as well as influences component design due to critical stress concentrations within the component.
In the present paper, a six bead multi-pass gas metal arc weld of 20 mm thick structural steel S355J2+N is experimentally and numerically investigated. The studies include transient 2D and 3D numerical calculations which consider temperature-dependent material properties, phase transformations, 'thermal' tempering, transformation plasticity, volume change due to phase transformation, an elasticplastic material model, and isotropic strain hardening. The experimentally determined and calculated residual stresses are in a good agreement. Furthermore, the influence of the preheat and interpass temperature on welding-induced residual stresses is shown in the present investigation.
This article studies the three dimensional transient weld pool dynamics and the influence of groove angle on welding of low carbon structural steel plates using the ForceArc® process. The deformation of the weld bead is also calculated with an accurate coupling of the heat transfer with fluid flow through continuity, momentum and the energy equations combined with the effect of droplet impingement, gravity, electromagnetic force, buoyancy, drag forces and surface tension force (Marangoni effect). Different angles of V groove are employed under the same welding parameters and their influence on the weld pool behavior and weld bead geometry is calculated and analyzed, which is needed for subsequent calculations of residual stress and distortion of the workpiece.
Such a simulation is an effective way to study welding processes because the influence of all welding parameters can be analyzed separately with respect to heat transfer, weld pool dynamic, and microstructure of the weld. Good agreement is found between the predicted and experimentally determined weld bead cross-section and temperature cycles. It is found that the main flow pattern is more or less the same although the groove angle increases, but it will evoke larger amount of fluid to flow downward to get deeper penetration.
For differentiating the applicability of various numerical welding simulation methods as well as unifying the prerequisites and the steps to be taken in simulation, normative codes are required for the user. Since any standard-like documents are currently still lacking in this field, the DIN German Institute for Standardization in partnership with the Research Association of DVS German Welding Society have set up a standards committee dealing with the preparation of respective documents.
This article focuses on the presentation of the new DIN Spec 32534-1 explaining the major simulation steps and specifying the application fields and the key terms of welding simulation. In addition, a generally valid simulation structure has been established which is intended to serve as a recommendation for customers and suppliers in formulating and handling a service order as well as for persons who start doing welding simulation for the first time. It additionally gives an overview of the other subject areas dealt with in the standards committee as well as of the international activities in this field.
The results of numerical welding simulations strongly depend on its temperature field. In the present paper, the temperature field of a pulsed gas metal arc weld of structural steel S355J2+N (ASTM A572 Gr. 50) with a thickness of 5 mm is experimentally and numerically investigated. In the case of temperature field validation, volumetric Gauss and double-ellipsoid Goldak heat sources are applied. Additionally, different heat source configurations, including adaptations of thermal conductivity, are analyzed regarding their influence on the calculation of welding-induced distortion.
The investigations clarify the influence of heat source configurations on the calculated results, thus, contribute to an improved prediction of welding-induced distortion.
Fusion welding processes are widely used for joining metal structures, such as pipes, ships, and cars. In general, these joining processes offer a very good compromise between reliability, safety, cost and maintenance which are important issues in the current economical context. The negative heat effects of welding, i.e. distortions and residual stresses of the welded parts, are well known and many researches in this field have already been done in the last decades in order to minimize them. On the experimental side, many sophisticated procedures have become state of the art to deal with this problem. On the computational side, the improvement of the simulation algorithms and the computing power enables the simulations of many physical phenomena occurring during the welding process. The implementation of welding simulation techniques is nevertheless not an easy task and often associated with expert knowledge which hinders their global application in an industrial environment. This paper is focused on the industrial requirements of a welding simulation software with special respect to the needs of the automotive industry. The necessary information to run a welding simulation and the expectations of a weld specialist without deep knowledge in numerical methods are investigated. These expectations are tested on an automotive welded assembly with a commercially available welding simulation software designed especially for the needs of the automotive industry. A welding experiment is done and the measured temperature distributions and distortions serve as reference to validate the simulation results. The result quality of the simulations of temperature fields and distortions is in best agreement with experimental data. The workflow is well adapted for the considered industrial requirements and the time-tosolution as well as the computational costs are acceptable, whereas the efficient calibration of the heat input model is still a point which will be further investigated in current and future research works.
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.
Kornfeinung im Schweißgut kann die mechanischen Eigenschaften der Schweißnaht und die Schweißeignung des Grundwerkstoffs deutlich verbessern. Eine Möglichkeit korngefeintes Schweißgut zu erreichen, ist das Versetzen des Schmelzbades mit kornfeinenden Mitteln. In dieser Studie wird gezeigt wie Titan- und Borzusätze Korngröße und -struktur von WIG-Schweißnähten der Al-Legierung 5083 (Al Mg4,5Mn0,7) beeinflussen. Dazu wurden in einem Gießprozess stäbchenförmige Einlagen hergestellt, die aus Grundwerkstoff und definierten Zusätzen der Kornfeinungslegierung Al Ti5B1 bestanden. Sie wurden als Ersatz für einen Schweißzusatzwerkstoff in einer Nut im Grundwerkstoff untergebracht und im WIG-Verfahren überschweißt. Durch die Steigerung des Titan- und Borgehalts im Schweißgut konnte dessen mittlere Korngröße deutlich verringert werden. Außerdem wurde eine Änderung der Kornstruktur beobachtet. Die Ergebnisse können als Grundlage genutzt werden, um die empfohlene chemische Zusammensetzung von Schweißzusätzen für Lichtbogenschweißen von Aluminium anzupassen.
A single-layer pulsed gas metal arc weld of structural steel S355J2+N with a thickness of 5 mm is experimentally and numerically investigated. Two tack welds are considered in the numerical simulation into two different ways. First, the tack welds are represented by elements belonging to the initial material. This implies that the 'tack weld material' was not exposed to any thermal load or phase transformation before actual welding was performed. The weld seam is shortened and there is an influence on the stiffness of the whole structure affecting the calculation result. Secondly, the tack welds were simulated as conducted in the experimental welding procedure. The cases considering tack welding are compared to a simulation neglecting tack welding and to the experimental results.
The influence of tack welds on the calculated welding-induced distortion is clarified and a contribution to an improved simulation-based prediction of welding-induced distortion is possible by modeling tack welding according to the realistic fabrication procedure.
Welding is a key technology in the area of industrial production due to its flexibility and efficiency. However, new materials and welding techniques necessitate permanent research activities in order to keep up with the demands. A detailed knowledge about the process itself and the heat effects of welding, e.g., temperatures, distortions, and stresses, is the basis for a target-oriented optimization instead of a trial-and-error approach. Numerical welding simulation is a powerful tool to meet these demands. Complementary to an experimental investigation, it enables the analysis of the specimen during the welding process, commonly known as computational welding mechanics (CWM). Whereas simulation is nowadays a common tool in different development processes, the modeling of welding still remains difficult because of the multiple physical effects taking place. One of the most important problems for the user is the lack of knowledge about the material properties as input data for the simulation. Furthermore, any scattering of the data causes uncertainties that can have major effects on the calculations. The objective of this paper is to give an overview about the experimental determination and analysis of the material properties needed as input data for a welding simulation. The measurement techniques and the occurring deviations of the results are discussed. Additionally, the collected data for three representative alloys (dual-phase steel, austenitic steel, precipitation-hardenable aluminum alloy) are analyzed. Finally, the temperature-dependent thermophysical and thermomechanical material properties for these three alloys are given in a ready-to-use format for a numerical welding simulation.
Welding is one of the most widely used joining processes in structural applications, like in car body production in the automotive industry. It is well-known that distortions and residual stresses occur during and after the welding process. Many procedures exist to decrease these negative heat effects of welding, but are often coupled with highly cost intensive experiments. For several decades, simulation models have been developed to understand and predict the heat effects of welding and to reduce experimental effort. In the production planning of various Original Equipment Manufacturers (OEM), some simulation tools are already well established, e.g. for crash test, forming or casting simulations. For welding, the demand is high but the implementation of welding simulation software is still not established yet. Welding is a complex process and the development of a flexible simulation tool, which produces good simulation results without expert knowledge in simulation, is not an easy task. In this paper, a welded assembly from the automotive industry has been simulated and compared to experimental data. Temperature fields and transient distortion distributions have been measured with thermocouples and with an optical 3D deformations analysis tool, respectively. The simulation has been run with a commercially available welding simulation software. The simulated temperature fields match the numerical ones perfectly. The simulated distortions are also qualitatively in best agreement with the experimental ones. Quantitatively, a difference of approximately 20 % between the simulated and the measured distortions is visible; this is acceptable considering the simplifications and assumptions of the simulation model. The global time to solution to get these results without expert knowledge in welding simulation was between 4 and 6 weeks, which is a reasonable time frame for an industrial application of welding simulation.