Filtern
Erscheinungsjahr
Dokumenttyp
- Zeitschriftenartikel (40)
- Beitrag zu einem Tagungsband (23)
- Vortrag (12)
- Beitrag zu einem Sammelband (4)
- Forschungsbericht (3)
- Buchkapitel (1)
- Forschungsdatensatz (1)
Sprache
- Englisch (54)
- Deutsch (28)
- Mehrsprachig (1)
- Serbisch (1)
Schlagworte
- Neural networks (7)
- Welding simulation (7)
- Aluminium (6)
- Grain refinement (5)
- Gründungsstrukturen (5)
- Laser beam welding (5)
- Welding (5)
- Ökobilanzierung (5)
- Fatigue tests (4)
- Inverse heat conduction problem (4)
Organisationseinheit der BAM
Eingeladener Vortrag
- nein (12)
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.
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.