TY - JOUR A1 - Pittner, Andreas A1 - Weiß, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael T1 - Fast temperature field generation for welding simulation and reduction of experimental effort N2 - 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. KW - Heat flow KW - Neural networks KW - Simulating KW - Temperature KW - Welding PY - 2011 SN - 0043-2288 SN - 1878-6669 VL - 55 IS - 09-10 SP - 83 EP - 90 PB - Springer CY - Oxford AN - OPUS4-24603 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pittner, Andreas A1 - Weiß, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael T1 - Methodology to improve applicability of welding simulation N2 - 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. KW - Welding simulation KW - GMA-laser hybrid welding KW - Laser beam welding KW - Neural networks KW - Global optimisation KW - Stochastic search method KW - Inverse heat conduction problem KW - Model prediction PY - 2008 U6 - https://doi.org/10.1179/136217108X329322 SN - 1362-1718 SN - 1743-2936 VL - 13 IS - 6 SP - 496 EP - 508 PB - Maney CY - London AN - OPUS4-18300 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pittner, Andreas A1 - Weiss, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael T1 - A methodology for the fast temperature field generation for welding simulation T2 - 17th International Conference "Computer Technology in Welding and Manufacturing" CY - Cranfield, UK DA - 2008-06-18 KW - Welding simulation KW - Temperature field generation KW - Short calculation time KW - Multiple experiments KW - Inverse heat conduction problem KW - Neural networks PY - 2008 SN - 978-1-903761-07-6 SP - 1 EP - 12 PB - TWI CY - Cambridge AN - OPUS4-18290 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Pittner, Andreas A1 - Weiss, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael ED - V.I. Makhnenko, T1 - Fast generation and prediction of welding temperature fields for multiple experiments N2 - 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. T2 - 4th International Conference - Mathematical modelling and information technologies in welding and related processes CY - Katsiveli, Crimea, Ukraine DA - 2008-05-27 KW - Welding simulation KW - GMA-laser hybrid welding KW - Laser beam welding KW - Neural networks KW - Global optimisation KW - Stochastic search method KW - Inverse heat conduction problem KW - Model prediction PY - 2008 SP - 134 EP - 140 CY - Kiev, Ukraine AN - OPUS4-19639 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - RPRT A1 - Pittner, Andreas A1 - Weiss, D. A1 - Schwenk, Christopher A1 - Rethmeier, Michael T1 - Fast temperature field generation for welding simulation and reduction of experimental effort N2 - 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. KW - Welding simulation KW - Temperature field generation KW - Short calculation time KW - Multiple experiments KW - Inverse heat conduction problem KW - Neural networks PY - 2009 IS - SC-Auto-32-09 SP - 1 EP - 11 PB - International Institute of Welding CY - Paris AN - OPUS4-19744 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Pittner, Andreas A1 - Schwenk, Christopher A1 - Rethmeier, Michael A1 - Weiß, D. T1 - Automated generation of temperature fields for numerical welding simulation KW - Welding simulation KW - Temperature field generation KW - Optimization KW - Neural networks PY - 2009 SN - 0288-4771 VL - 27 IS - 2 SP - 219 EP - 224 CY - Tokyo, Japan AN - OPUS4-19826 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fabry, Cagtay A1 - Pittner, Andreas A1 - Rethmeier, Michael T1 - Design of neural network arc sensor for gap width detection in automated narrow gap GMAW N2 - 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. KW - GMA welding KW - Narrow gap welding KW - Sensor KW - Neural networks KW - Adaptive control PY - 2018 U6 - https://doi.org/10.1007/s40194-018-0584-8 SN - 1878-6669 SN - 0043-2288 VL - 62 IS - 4 SP - 819 EP - 830 PB - Springer CY - Berlin AN - OPUS4-45234 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -