TY - GEN A1 - Schwenk, Christopher A1 - Rethmeier, Michael A1 - Weiss, D. ED - H.K.D.H. Bhadeshia, ED - H. Cerjak, ED - E. Kozeschnik, T1 - Rapid generation of temperature fields for simulation of welding distortions PY - 2007 SN - 978-3-902465-69-6 SP - 835 EP - 846 CY - Graz, Austria AN - OPUS4-16491 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Reuter, T. A1 - Plotzki, David A1 - Borges de Oliveira, F. A1 - Wohlgemuth, F. A1 - Bauer, F. A1 - Ballach, F. A1 - Kimmig, W. A1 - Wagner, G. A1 - Watzl, C. A1 - Weiß, D. A1 - Hausotte, T. T1 - Numerical measurement uncertainty evaluation for X-ray computed tomography using simulations – A first approach to build a digital twin N2 - The ability of industrial X-ray computed tomography (CT) to scan an object with several internal and external features at once causes increasing adoption in dimensional metrology. In order to evaluate the quality of a measurement value, the task-specific measurement uncertainty has to be determined. Currently, VDI/VDE 2630 part 2.1 gives a guideline to determine the uncertainty of CT measurements experimentally by conducting repeated measurements. This is costly and time-consuming. Thus, the aim is to determine the task-specific measurement uncertainty numerically by simulations (e. g. according to the guide to expression of uncertainty in measurement (GUM) Supplement 1). To achieve that, a digital twin is necessary. This contribution presents a simple first approach how a digital twin can be built. In order to evaluate this approach, a study comparing measurements and simulations of different real CT systems was carried out by determining the differences between the measurement results of the digital twin and of the measurement results of the real-world CT systems. The results have shown a moderate agreement between real and simulated data. To improve on this aspect, a standardized method to characterize CT systems and methods to implement CT parameters into the simulation with sufficient accuracy will be developed. N2 - Die industrielle Röntgencomputertomografie (CT) wird durch ihre Fähigkeit, ein Messobjekt mit unterschiedlichen inneren und äußeren Merkmalen auf einmal zu messen, immer häufiger auch in der dimensionellen Messtechnik eingesetzt. Um die Güte eines Messwertes beurteilen zu können, muss die aufgabenspezifische Messunsicherheit ermittelt werden. Mit der Richtlinie VDI/VDE 2630 Blatt 2.1 kann derzeit die aufgabenspezifische Messunsicherheit von CT-Messungen experimentell durch eine Vielzahl an Wiederholmessungen ermittelt werden. Diese Vorgehensweise ist allerdings kosten- und zeitintensiv. Daher wird die Bestimmung der aufgabenspezifischen Messunsicherheit numerisch durch Simulationen angestrebt (z. B. Leitfaden zur Angabe der Unsicherheit beim Messen (GUM) Supplement 1). Um dieses Ziel zu erreichen, ist es notwendig, einen Digitalen Zwilling von der Messung mit dem verwendeten CT-System zu erstellen. Dieser Beitrag stellt einen ersten Ansatz zur Erstellung eines Digitalen Zwillings vor. Um diesen anschließend zu testen, wurden in einem ersten Test mehrere CT-Scans auf verschiedenen Anlagen durchgeführt und anschließend anhand der verfügbaren Parameter simuliert. Um die Qualität des jeweiligen Digitalen Zwillings einschätzen zu können, wurden die Abweichungen der resultierenden Messwerte von ihren jeweiligen Sollwerten miteinander verglichen. Die Ergebnisse zeigten eine bedingte Übereinstimmung zwischen realen und simulierten Daten. Um diese zu verbessern, wird eine standardisierte Methode zur Charakterisierung der realen CT-Systeme angestrebt. Darüber hinaus sollten Methoden entwickelt werden, die Charakterisierungsparameter in einer Simulationsumgebung hinreichend genau umzusetzen. KW - Computed tomography KW - Measurement uncertainty KW - Simulation KW - Digital twin PY - 2022 U6 - https://doi.org/10.1515/teme-2022-0025 SP - 1 EP - 16 PB - De Gruyter CY - Berlin AN - OPUS4-55428 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 - 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 - CONF A1 - Pittner, Andreas A1 - Schwenk, Christopher A1 - Weiß, D. A1 - Rethmeier, Michael T1 - Application of a combined modelling technique to reduce experimental effort - a case study for laser-GMA-hybrid welding T2 - VI. International conference "Beam technologies & laser application" CY - Saint Petersburg, Russia DA - 2009-09-23 PY - 2009 SP - 97 EP - 102 CY - Saint Petersburg, Russia AN - OPUS4-20882 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - GEN A1 - Pittner, Andreas A1 - Schwenk, Christopher A1 - Weiß, D. A1 - Rethmeier, Michael ED - Cerjak, H. ED - Enzinger, N. T1 - An efficient solution of the inverse heat conduction problem for welding simulation PY - 2010 SN - 978-3-85125-127-2 SP - 761 EP - 791 PB - Verlag der Technischen Universität Graz AN - OPUS4-23154 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 -