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The increasing adoption of Open Science principles has been a prevalent topic in the welding science community over the last years. Providing access to welding knowledge in the form of complex and complete datasets in addition to peer-reviewed publications can be identified as an important step to promote knowledge exchange and cooperation. There exist previous efforts on building data models specifically for fusion welding applications; however, a common agreed upon implementation that is used by the community is still lacking. One proven approach in other domains has been the use of an openly accessible and agreed upon file and data format used for archiving and sharing domain knowledge in the form of experimental data. Going into a similar direction, the welding community faces particular practical, technical, and also ideological challenges that are discussed in this paper. Collaboratively building upon previous work with modern tools and platforms, the authors motivate, propose, and outline the use of a common file format specifically tailored to the needs of the welding research community as a complement to other already established Open Science practices. Successfully establishing a culture of openly accessible research data has the potential to significantly stimulate progress in welding research.
This paper presents an approach to reconstruct the three-dimensional transient temperature field for fusion welding processes as input data for computational weld mechanics. The methodology to solve this inverse heat conduction problem fast and automatically focuses on analytical temperature field models for volumetric heat sources and application of global optimisation. The important issue addressed here is the question which experimental data is needed to guarantee a unique reconstruction of the experimental temperature field. Different computational-experimental test cases are executed to determine the influence of various sets of discrete experimental data on the solvability of the optimisation problem. The application of energy distributions utilised for laser beam welding allows reconstructing the temperature field efficiently. Furthermore, the heat input into the workpiece determined by the simulation contributes to the evaluation of the thermal efficiency of the welding process.
Life Cycle Assessment (LCA) and Social Life Cycle Assessment (SLCA) are applied in evaluating possible social and environmental impacts of the state-of-art welding technologies, such as Manual Metal Arc Welding (MMAW), Manual Gas Metal Arc Welding (GMAW), Automatic GMAW and Automatic Laser-Arc Hybrid Welding (LAHW). The LCA results indicate that for 1 meter weld seam, MMAW consumes the largest amount of resources (like filler material and coating on electrodes) and energy, which contributes to comparatively higher environmental impacts in global warming potential, acidification, photochemical ozone creation potential and eutrophication than other chosen processes. With regard to social aspects, the health issues and fair salary are under survey to compare the relative potential risk on human health caused by fumes in different welding technologies, and to indicate the sufficiency of current salary of welders in Germany. The results reflect that the wage status of welders is still fair and sufficient. The manual processes bring much higher potential risk of welders health than the automatic processes, especially MMAW.
In todays automotive frame-and-body construction, laser welds are typically carried out as square butt welds in lap joints. These welds are increasingly produced by remote laser welding optics with working distances of 500 mm and more. This enables simple path programming because clamping devices are traversed over and therefore low cycle times with improved productivity is achieved. However, workpiece tolerances lead to part displacements and varying joint positions over time. These displacements have to be acted against by appropriate flange length that contain for positioning deviations. Using this concept, aspired light-weight optimisations, e.g. reducing flange length, are difficult to achieve. By using seam tracking sensors, part tolerances are automatically detected and counteracted for. In addition, joint edge detection allows constructive changes on the type of weld. Fillet welds reduce flange lengths', improve force flow and open up possibilities for visual quality monitoring. Apart from that, vertical displacements in the form of height tolerances still occur. This needs consideration by adapting the set of welding parameter to the current welding situation. In this respect, one main welding parameter is the lateral beam offset to the upper sheet. Since body-in-white welding applications mainly comprise of zinc-coated steel sheets, special requirements for the welding process are given. Especially zero-gap-welding and welding of joints with gaps larger than 0.2 mm are critical. Using a laser beam oscillation process can stabilise these situations. Approaches to finding parameter sets are presented in this paper.
Microcrack formation during gas metal arc welding of high-strength fine-grained structural steel
(2014)
The recent development of high-performance-modified spray arc processes in gas metal arc welding due to modern digital control technology and inverter power sources enables a focused spray arc, which results in higher penetration depths and welding speed. However, microcracks occurred in the weld metal while approaching the process limits of the modified spray arc, represented by a 20-mm double layer DV-groove butt-weld. These cracks were detected in structural steel exhibiting a yield strength level of up to 960 MPa and are neither dependent on the used weld power source nor a consequence of the modified spray arc process itself. The metallographic and fractographic investigations of the rather exceptional fracture surface lead to the classification of the microcracks as hot cracks. The effects of certain welding parameters on the crack probability are clarified using a statistical design of experiment. However, these microcracks do not impact the design specification for toughness in the Charpy V-notch test (absorbed energy at -40 °C for the present material is 30 J).
During welding, residual stresses build-up created by the steep thermal gradient that occurs in the weld zone from localized heating and cooling, and phase transformations appearing in low-alloyed structural steel is inevitable. Welding of rather simple test plates do not cover the actual structural effects, which have to be considered during real component welding. However, the resulting welding-induced residual stress state is highly influenced by the structural characteristics, i.e. restraint conditions, of the welded construction. Therefore, a unique large-scale testing facility providing a specific shrinkage restraint while welding and subsequent cooling was used for the present investigations. Hereby, a six bead multi-pass gas metal arc weld of 20 mm thick structural steel S355J2 + N was welded under shrinkage restraint. The residual stresses were experimentally and numerically investigated, and compared to an analysis of plates welded under force-free support and free shrinkage conditions.
The experimentally determined and calculated residual stresses using both 2D and 3D numerical models are in a good agreement. Furthermore, the influence of a shrinkage restraint on the residual stress distribution is both experimentally and numerically shown for the present test set-up.
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.
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.