TY  - CONF
A1  - Meng, Xiangmeng
A1  - Bachmann, Marcel
A1  - Kising, Pascal
A1  - Yang, Fan
A1  - Rethmeier, Michael
T1  - Prediction of weld pool and keyhole geometries in high-power laser beam welding through a physics-informed generative artificial intelligence approach
T2  - Proceedings of the ICALEO 2025, 44th International Congress on Applications of Lasers & Electro-Optics
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  - International Congress of Applications of Lasers & Electro-Optics 2025
CY  - Orlando, USA
DA  - 12.10.2025
KW  - Laser beam welding
KW  - Generative artificial intelligence
KW  - Machine learning
KW  - numerical simulation
PY  - 2025
SP  - 1
EP  - 10
AN  - OPUS4-65075
LA  - eng
AD  - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany
ER  -