TY - JOUR A1 - El-Sari, B. A1 - Biegler, M. A1 - Graf, B. A1 - Rethmeier, Michael T1 - Distortion-based validation of the heat treatment simulation of Directed Energy Deposition additive manufactured parts N2 - Directed energy deposition additive manufactured parts have steep stress gradients and an anisotropic microstructure caused by the rapid thermo-cycles and the layer-upon-layer manufacturing, hence heat treatment can be used to reduce the residual stresses and to restore the microstructure. The numerical simulation is a suitable tool to determine the parameters of the heat treatment process and to reduce the necessary application efforts. The heat treatment simulation calculates the distortion and residual stresses during the process. Validation experiments are necessary to verify the simulation results. This paper presents a 3D coupled thermo-mechanical model of the heat treatment of additive components. A distortion-based validation is conducted to verify the simulation results, using a C-ring shaped specimen geometry. Therefore, the C-ring samples were 3D scanned using a structured light 3D scanner to compare the distortion of the samples with different post-processing histories. KW - Directed Energy Deposition KW - Additive Manufacturing KW - Heat Treatment KW - Numerical Simulation KW - Finite Element Method PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-513153 DO - https://doi.org/10.1016/j.procir.2020.09.146 VL - 94 SP - 362 EP - 366 PB - Elsevier B.V. AN - OPUS4-51315 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Meng, Xiangmeng T1 - Prediction of weld pool and keyhole geometries in high-power laser beam welding through a physics-informed generative approach N2 - The weld pool and keyhole geometries are critical characteristics in evaluating the stability of the high-power laser beam welding (LBW) process and determining the resultant weld quality. However, obtaining these data through experimental or numerical methods remains challenging due to the difficulties in experimental measurements and the high computational demands of numerical modelling. This paper presents a physics-informed generative approach for predicting weld pool and keyhole geometries in the LBW process. With the help of a well experimentally validated numerical model considering the underlying physics in the LBW, the geometries of the weld pool and keyhole under various welding conditions are calculated, serving as the dataset of the generative model. A Conditional Variational Autoencoder (CVAE) model is employed to generate realistic 2D weld pool and keyhole geometries from the welding parameters. We utilize a β-VAE model with the Evidence Lower Bound (ELBO) loss function and include Kullback-Leibler divergence annealing to better optimize model performance and stability during training. The generated results show a good agreement with the ground truth from the numerical simulation. The proposed approach exhibits the potential of physics-informed generative models for a rapid and accurate prediction of the weld pool geometries across a diverse range of process parameters, offering a computationally efficient alternative to full numerical simulations for process optimization and control in laser beam welding processes. T2 - The 45th annual International Congress on Applications of Lasers & Electro-Optics CY - Orlando, FL, USA DA - 12.10.2025 KW - Laser beam welding KW - Generative artificial intelligence KW - Machine Learning KW - Numerical Simulation KW - Weld pool KW - Keyhole dynamics PY - 2025 AN - OPUS4-64812 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -