Filtern
Erscheinungsjahr
Dokumenttyp
- Zeitschriftenartikel (37)
- Beitrag zu einem Tagungsband (12)
- Vortrag (10)
- Beitrag zu einem Sammelband (4)
- Buchkapitel (1)
- Dissertation (1)
- Forschungsbericht (1)
- Forschungsdatensatz (1)
Sprache
- Englisch (67) (entfernen)
Schlagworte
- Neural networks (7)
- Welding simulation (7)
- Welding (6)
- Aluminium (5)
- Grain refinement (5)
- Laser beam welding (5)
- Mechanical properties (5)
- Additive manufacturing (4)
- Fatigue tests (4)
- Inverse heat conduction problem (4)
Organisationseinheit der BAM
- 9 Komponentensicherheit (21)
- 9.3 Schweißtechnische Fertigungsverfahren (21)
- 7 Bauwerkssicherheit (3)
- 5 Werkstofftechnik (2)
- 5.2 Metallische Hochtemperaturwerkstoffe (2)
- 7.7 Modellierung und Simulation (2)
- 8 Zerstörungsfreie Prüfung (2)
- 5.1 Mikrostruktur Design und Degradation (1)
- 5.5 Materialmodellierung (1)
- 7.1 Baustoffe (1)
Eingeladener Vortrag
- nein (10)
The present thesis provides a contribution to the solution of the inverse heat conduction problem in welding simulation. The solution strategy is governed by the need that the phenomenological simulation model utilised for the direct solution has to provide calculation results within short computational time. This is a fundamental criterion in order to apply optimisation algorithms for the detection of optimal model parameter sets. The direct simulation model focuses on the application of functional-analytical methods for solving the corresponding partial differential equation of heat conduction. In particular, volume heat sources with a bounding of the domain of action are applied. Besides the known normal and exponential distribution, the models are extended by the introduction of parabolically distributed heat sources. Furthermore, the movement on finite specimens under consideration of curved trajectories has been introduced and solved analytically. The calibration of heat source models against experimental reference data involves the simultaneous adaptation of model parameters. Here, the global parameter space is searched in a randomised manner. However, an optimisation pre-processing is needed to get information about the sensitivity of the weld characteristics like weld pool dimension or objective function due to a change of the model parameters. Because of their low computational cost functional-analytical models are well suited to allow extensive sensitivity studies which is demonstrated in this thesis. For real welding experiments the applicability of the simulation framework to reconstruct the temperature field is shown. In addition, computational experiments are performed that allow to evaluate which experimental reference data is needed to represent the temperature field uniquely. Moreover, the influence of the reference data like fusion line in the cross section or temperature measurements are examined concerning the response behaviour of the objective function and the uniqueness of the optimisation problem. The efficient solution of the inverse problem requires two aspects, namely fast solutions of the direct problem but also a reasonable number of degrees of freedom of the optimization problem. Hence, a method was developed that allows the direct derivation of the energy distribution by means of the fusion line in the cross section, which allows reducing the dimension of the optimisation problem significantly. All conclusions regarding the sensitivity studies and optimisation behaviour are also valid for numerical models for which reason the investigations can be treated as generic.
The application of welded aluminum constructions has increased over the last decades due to the high demand for lightweight design. In this context aluminum alloys have a significant importance especially the heat treatable Al-Mg-Si alloys. With regards to welding the heat input causes the dissolving of the precipitates yielding a degradation of the strength properties. Furthermore, there is the risk of solidification cracking during welding for Al-Mg-Si alloys, especially AW 6082-T6. As a consequence Al-Mg-Si alloys are welded using a dissimilar filler material based on 5xxxx aluminum alloys that yields a reduction of resulting strength properties in comparison to the base material. Therefore, the paper presents the application of a novel massive filler wire for Al-Mg-Si alloys. The wire consists of AW 6082 with additionally added AlTi5B1 master alloy fostering the grain refinement of the weld metal. Exemplarily for the base material AW 6082-T6 GMA welding experiments are performed using the novel filler wire 6082TiB. It could be shown that solidification cracks could totally be avoided. The tensile strength of the welded joints are evaluated and compared with weldments using a conventional 5183 filler wire. Artificial aging is performed after welding in order to improve the strength properties by solution hardening showing the potential of these alloys.
The digitalization of industrial processes is the most discussed topic in society these days. New business models have been developed to benefit from the opportunities offered by a digitally connected world. However, the focus in on the smart factory consisting of autonomous acting cyber physical systems (CPS). The efficient implementation of such CPS within an industrial environment requires the digitalization of the corresponding production processes. The digital twin of the process under investigation enables to develop sophisticated monitoring and control strategies which are necessary to fulfil the requirements of individual product design.
The need for a digitalization of the welding process is a logical consequence especially with regards to its industrial importance. The theoretical investigations and derived mathematical models of the welding process are well known since many decades. Anyhow, there is still a lack of industrial applicability of such models for an efficient and safe design of welded components. With respect to structural welding simulation that targets the heat effects of welding in terms of global quantities like temperature, solid phase distribution and residual stresses as well as distortions, the limited predictability of these quantities for arbitrary process parameters hinders its usability.
This presentation aims to give an overview of the current state of the art in structural welding simulation to predict the evolution of welding induced temperatures, stresses and distortions. Emphasis is given to industrial applicability of such models by reduction of the calculation times for large real-world structures and improved prediction of optimal process parameters. Furthermore, the role of such models within a weld data management system is demonstrated. The accumulation and relational storage of simulation and measuring data improves the overall process knowledge. This enables virtual cause and effect analyses of new process parameters as basis for a control system design.
The development within the offshore wind energy 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 support structures. Besides XL-monopiles
the jacket support structure is a reasonable alternative due to the
high rigidity combined with low material consumption. However,
the effort for manufacturing of the hollow section joints reduces
the economic potential of jacket structures significantly. Therefore,
a changeover from an individual towards a serial production
based on automated manufactured tubular joints combined with
standardized pipes has to be achieved. Hence, this paper addresses
the welding process chain of automated manufactured
tubular joints including digitization of the relevant manufacturing parameters such as laser scanning of the weld seam geometry.
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.
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.
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.
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.
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.
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.
The objective of this paper is to demonstrate a new simulation technique which allows fast and automatic generation of temperature fields as input for subsequent thermomechanical welding simulation. The basic idea is to decompose the process model into an empirical part based on neural networks and a phenomenological part that describes the physical phenomena. The strength of this composite modelling approach is the automatic calibration of mathematical models against experimental data without the need for manual interference by an experienced user. As an example for typical applications in laser beam and GMA-laser hybrid welding, it is shown that even 3D heat conduction models of a low complexity can approximate measured temperature fields with a sufficient accuracy. In general, any derivation of model fitting parameters from the real process adds uncertainties to the simulation independent of the complexity of the underlying phenomenological model. The modelling technique presented hybridises empirical and phenomenological models. It reduces the model uncertainties by exploiting additional information which keeps normally hidden in the data measured when the model calibration is performed against few experimental data sets. In contrast, here the optimal model parameter set corresponding to a given process parameter is computed by means of an empirical submodel based on relatively large set of experimental data. The approach allows making a contribution to an efficient compensation of modelling inaccuracies and lack of knowledge about thermophysical material properties or boundary conditions. Two illustrating examples are provided.
Life cycle assessment of fusion welding processes considering upstream and downstream process steps
(2023)
In manufacturing, fusion welding processes consume significant resources, presenting a significant opportunity for reducing environmental impact. Although there is a qualitative understanding of the environmental implications of these processes, a quantitative assessment of key parameters remains complex. This study introduces a welding-specific methodology that employs life cycle assessment (LCA) to quantitatively evaluate the environmental footprint of fusion welding technologies. Our approach identifies and analyses the principal parameters affecting the environmental performance of various welding techniques, including traditional joint welding and additive manufacturing via the Direct Energy Deposition-Arc (DED-Arc) process. Real-time resource usage data is integrated to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact. This facilitates the advancement of sustainable manufacturing practices.
In manufacturing, fusion welding processes use a lot of resources, which presents an opportunity to reduce environmental impact. While there is a general understanding of the environmental impact of these processes, it is difficult to quantitatively assess key parameters. This study introduces a welding-specific methodology that uses life cycle assessment (LCA) to evaluate the environmental impact of fusion welding technologies. Our approach analyses the main parameters that affect the environmental performance of different welding techniques, including traditional methods and additive manufacturing through the Direct Energy Deposition-Arc (DED-Arc) process. We integrate real-time resource usage data to offer an innovative framework for directly deriving environmental impacts. This research contributes to optimising welding processes by providing a precise and quantifiable measure of their ecological impact, facilitating the advancement of sustainable manufacturing practices.
Direct energy deposition additive manufacturing technologies utilizing an electric arc offer a great potential in generating large volume metal components. However, the selection of process parameters that yield the desired near net shape design as well as the requested mechanical component behavior is not a trivial task due to the complex relationship. Exemplarily for additive manufacturing of high-strength precipitation hardening AlMgSi-aluminum alloy this paper shows the application of a newly developed matching solid welding wire doped with TiB as grain refiner. The correlation between process parameters and component quality is examined analyzing the size and distribution of pores as well as the grain morphology. Furthermore, the influences of different post-weld heat treatments are evaluated to meet the reference mechanical properties of the corresponding wrought material. Finally, the digital integration of the entire additive manufacturing chain enables an overall traceability of the relevant process steps which is the basis for a reliable subsequent quality assessment.