### Filtern

#### Dokumenttyp

#### Schlagworte

- Neural networks (3)
- Welding simulation (3)
- Optimization (2)
- Construction products (1)
- Ecotoxicity tests (1)
- Eluates (1)
- GMA-laser hybrid welding (1)
- Global optimisation (1)
- Heat flow (1)
- Inverse heat conduction problem (1)

#### Organisationseinheit der BAM

A European round robin test according to ISO 5725-2 was conceptually prepared, realised, and evaluated. The aim was to determine the inter-laboratory variability of the overall process for the ecotoxicological characterization of construction products in eluates and bioassays. To this end, two construction products BAM-G1 (granulate) and HSR-2 (roof sealing sheet), both made of EPDM polymers (rubber), were selected. The granular construction product was eluted in a one stage batch test, the planar product in the Dynamic Surface Leaching test (DSLT). A total of 17 laboratories from 5 countries participated in the round robin test: Germany (12), Austria (2), Belgium (1), Czech Republic (1) and France (1). A test battery of four standardised ecotoxicity tests with algae, daphnia, luminescent bacteria and zebrafish eggs was used. As toxicity measures, EC50 and LID values were calculated. All tests, except the fish egg test, were basically able to demonstrate toxic effects and the level of toxicity. The reproducibility of test results depended on the test specimens and the test organisms. Generally, the variability of the EC50 or LID values increased with the overall level of toxicity. For the very toxic BAM-G1 eluate a relative high variability of CV ¼ 73%e110% was observed for EC50 in all biotests, while for the less toxic HSR-2 eluate the reproducibility of EC50 varied with sensitivity: it was very good (CV ¼ 9.3%) for the daphnia test with the lowest sensitivity, followed by the algae test (CV ¼ 36.4%). The luminescent bacteria test, being the most sensitive bioassay for HSR-2 Eluate, showed the highest variability (CV ¼ 74.8%). When considering the complex overall process the reproducibility of bioassays with eluates from construction products was acceptable.

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.