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The sound numerical prediction of welding-induced thermal stresses, residual stresses, and distortions strongly depends on the accurate description of a welded material’s thermomechanical deformation behaviour. In this work, we provide experimental data on the viscoplastic deformation behaviour of a grade-s960ql steel up to a temperature of 1000 ◦C. In addition, a multi-phase viscoplastic material model is proposed, which accounts for the experimentally observed isothermal deformation behaviour of grade-s960ql steel base and austenitised material, as well as for athermal contributions that originate from solid-state phase transformations. The multi-phase viscoplastic and a classic rateindependent isotropic hardening material model were applied in the numerical simulations of both-ends-fixed bar Satoh tests and a single-pass gas metal arc weld. The influence of material modelling choices on the agreement between numerical simulation and experimental results is discussed, and recommendations for further work are given.
Evaluating innovative process technologies has become highly important within the last decades. As standard tools different Life Cycle Assessment methods have been established, which are continuously improved. While those are designed for evaluating single processes they run into difficulties when it comes to assessing environmental impacts of process innovations at macroeconomic level. In this paper we develop a multi-step evaluation framework building on multi regional inputeoutput data that allows estimating macroeconomic impacts of new process technologies, considering the network characteristics of the global economy.
Our procedure is as follows: i) we measure differences in material usage of process alternatives, ii) we identify where the standard processes are located within economic networks and virtually replace those by innovative process technologies, iii) we account for changes within economic systems and evaluate impacts on emissions.
Within this paper we exemplarily apply the methodology to two recently developed innovative technologies: longitudinal large diameter steel pipe welding and turning of high-temperature resistant materials. While we find the macroeconomic impacts of very specific process innovations to be small, its conclusions can significantly differ from traditional process based approaches. Furthermore, information gained from the methodology provides relevant additional insights for decision makers extending the picture gained from traditional process life cycle assessment.
In order to generate a material data base for computational welding mechanics, temperature and strain-rate dependent stress-strain experiments were performed by using a Gleeble®3500 testing system. The object of the investigation was HSLA transformable steel S960QL and related solid phases as bainite, martensite and austenite. For the production of these solid phases, the base material was heat treated according to an average weld temperature cycle which was extracted within the heat affected zone of a thermal numerical weld simulation of a GMA weld. The hot tensile tests were carried out via cost-saving flat specimen geometries. Two experimental series with different strain-rates were conducted, where the longitudinal strain-rate was controlled by specification of the transversal strain-rate applying Poisson’s-ratio. Subsequently, the resulting stress-strain curves were approximated in accordance with the Ramberg-Osgood-materials law. Consequently, it is shown that the temperature and strain-rate dependent stress-strain behavior of metals can be successfully characterized by means of a Gleeble®-system.
However, this requires a control of the longitudinal strain-rate by specification of the transversal strain-rate. The related experimental procedure and the method of evaluation are explained in detail. With regard to all tested solid phases, a significant strain-rate dependency can only be observed upwards from temperatures of 400 °C. Based on experimental results, Ramberg-Osgood-parameters will be presented to describe the stress-strain behavior of steel S960QL and related solid phases for temperatures between 25 °C and 1200 °C. Furthermore, the use of costsaving flat specimen-geometry appears reasonable.
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.
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 measurements 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 mm to 25 mm and wire feed rates in the range of 9 m/min to 13 m/min were used in the course of this study. Artificial neural networks were applied 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 defined as inputs to calculate the gap width. The evaluation of the proposed arc sensor model shows very good estimation capabilities for parameters sufficiently covered during the experiments.
The determination of mechanical properties of welded Steel structures such as strength or ductility is a subject of high interest for the majority of Companies in the area of metal Processing. The material Parameters can be obtained by performing the tensile test on the samples made from a part of a component. In some cases, it is highly expensive to produce the tensile specimens especially from the weld metal, which contains different type of microstructure such as weld seam or heat affected zone in an extremely small area. Therefore, a method is described in this paper to determine the material Parameters of high strength Steel structures and welded joints locally and without any additional effort to perform the tensile test. In this method, instrumented indentation technique (IIT), an indenter is pushed on the flat surface of a specimen in a certain period of time and simultaneously the applied force and the corresponding indentation path are measured. The data related to the force-indentation diagram is given as input to an artificial neural network (ANN) to obtain the material Parameters. The ANN can be trained by generating the large qualitative data sets with numerical Simulation of the IIT procedure. The Simulation must be run several times with the different material model parameter sets to generate the numerous
force-indentation diagrams as the inputs of ANN. Then, the trained ANN is validated by performing the IIT on the welded joints and comparing the obtained material Parameters from ANN with the tensile test.
Consequently, the mechanical properties of welded joints can be determined by performing the IIT and evaluating the resulting data by the ANN.
To increase the competitiveness of jacket substructures compared to monopiles a changeover from an individual towards a serial jacket production based on automated manufactured tubular joints combined with standardized pipes has to be achieved. Therefore, this paper addresses fatigue tests of automatically welded tubular X-joints focusing on the location of the technical fatigue crack. The detected location of the technical crack is compared to numerical investigations predicting the most fatigue prone notch considering the structural stress approach as well as the notch stress approach. Besides, the welding process of the automated manufactured tubular X-joints is presented.
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.
The high amount of resource consumption of fusion welding processes offers the potential to reduce their environmental impact. While the driving forces are known froma qualitative perspective, the quantitative assessment of the crucial parameters is not a trivial task. Therefore, herein, a welding-specific methodology to utilize life cycle assessment as a tool for evaluating the environmental impact of fusion welding processes is presented. In this context, two welding processes, resistance spot welding and laser beam welding, are analyzed for two different use cases.
These comprise the welding of shear test specimens and a cap profile made of electrogalvanized sheets of DC 05þ ZE (1.0312) as representative of an automotive application. For both welding processes, the main influences on the resulting environmental impact categories are evaluated and compared. The requirements for ecological efficient welding processes are discussed and implemented.
Offshore wind energy plays an important role for the desired transition towards a carbon dioxide free industry within the next decades. However, the grounding of the offshore wind plants governs the overall installation process besides bureaucratic bottlenecks.
The application of lightweight principles in steel construction, e.g. the usage dissolved load-bearing structures, so called Jackets foundations, offer great potential in reducing the resource consumption, especially with respect to the needed amount of steel.
In this context this paper focuses on a fully digitalization of the welding manufacturing and as well as testing chain to enable a fully automated manufacturing as well as quality assessment of tubular nodes as key element of Jackets foundation structures. Furthermore, the relationship between the seam shape geometry and resulting fatigue strength is evaluated by numerical methods incorporating bionic principles. It is shown that tubular nodes can be welded fully automatically taking geometry tolerances into account. Moreover, the seam shape could be manufactured as requested by the numerical models which offers great potential for an extended lifetime. Subsequently, the improvements in resource efficiency and reduction of carbon dioxide emissions are evaluated by a life-cycle-assessment.
Wire arc additive manufacturing enables the production of near-net shape large-volume metallic components leveraging an established industrial base of welding and cladding technology and adapting it for layer-wise material deposition. However, the complex relationship between the process parameters and resulting mechanical properties of the components still remains challenging. In case of high-strength Al-Mg-Si aluminum alloys, no commercial filler wires are yet available due the high susceptibility of solidification cracking as well as the necessary efforts to obtain acceptable mechanical properties. To address this need, we evaluated a novel filler wire based on AlMg0.7Si doped with a Ti5B1 master alloy to foster fine equiaxed grains within the deposited metal. The correlation between the process parameters and component quality was examined by analyzing the size and distribution of pores as well as the grain morphology. Furthermore, we evaluated the influence of different post-weld heat treatment strategies to achieve mechanical properties corresponding to the reference wrought material. We demonstrated that fine equiaxed grains in the weld metal reduced the susceptibility of solidification cracking significantly. The novel AlMg0.7Si-TiB (S Al 6063-TiB) filler wire facilitated wire arc additive manufacturing of high-strength aluminum components with mechanical properties that were almost as superior as the corresponding wrought base material.
Resistance spot welding (RSW) is widely used in the automotive industry
as the main joining method. Generally, an automotive body contains
around 2000 to 5000 spot welds. Therefore, it is of decisive importance to characterize the mechanical properties of these areas for the further optimization and improvement of an automotive body structure. The present paper aims to introduce a novel method to investigate the mechanical properties and microstructure of the resistance spot weldment of DP1000 sheet steel. In this method, the microstructure of RSW of two sheets was reproduced on one sheet and on a bigger area by changing of the welding parameters, e. g. welding current, welding time, electrode force and type. Then, tensile tests in combination with digital Image correlation (DIC) measurement were performed on the notched tensile specimens to determine the mechanical properties of the weld metal.
The notch must be made on the welded tensile specimen to force the fracture and elongation on the weld metal, enabling the characterization of its properties. Additionally, the parameters of a nonlinear isotropic material model can be obtained and verified by the simulation of the tensile specimens. The parameters obtained show that the strength of DP1000 steel and the velocity of dislocations for reaching the Maximum value of strain hardening, are significantly increased after RSW. The effect of sample geometry and microstructural inhomogeneity of the welded joint on the constitutive property of the weld metal are presented and discussed.
Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibility and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in the form of a beadon-plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is developed, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control, and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the temperature.
The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.
Despite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibil- ity and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in form of a bead-on- plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is devel- oped, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the tem- perature. The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.