07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/8
Browse
3 results
Search Results
Item Open Access Application of output constraints to a physics-informed hybrid model for the prediction of the threshold of deep-penetration laser welding(2024) Jarwitz, Michael; Michalowski, AndreasPhysics-informed hybrid models, the combination of physics and machine learning, have already shown considerable benefits for quantitative predictions of process constraints, such as the threshold of deep-penetration laser welding. However, despite the improved prediction accuracy and extrapolation capability of such models, there can still be cases where the predictions of the model, including the confidence region, result in values that are not consistent with physical boundary conditions. Therefore, this paper presents the application of additional output constraints to a physics-informed hybrid model to further improve the compliance of the model with physics. Gaussian processes are used for the machine learning model and output warping is used to incorporate the output constraints directly into the model. The approach is demonstrated at the example of a hybrid model for the prediction of the threshold of deep-penetration laser welding.Item Open Access Application of a physics-informed hybrid model with additional output constraints for the prediction of the threshold of deep-penetration laser welding(2025) Jarwitz, Michael; Michalowski, AndreasThe quantitative prediction of process constraints, such as the threshold of deep-penetration laser welding, plays a crucial role for the fast and reliable development of robust process windows for laser manufacturing processes. A physics-informed hybrid model with additional output constraints for the prediction of the threshold of deep-penetration laser welding is presented. A “residual model” approach is used, where a machine learning model, employing Gaussian processes, is used to model and compensate for the deviations between experiments and a physical model, and output warping is used to incorporate additional output constraints into the model. The main benefits that result from applying such a model are found to be (1) an increased prediction accuracy compared to only using the physical model, leading to a reduction of the mean relative error of about 76%; (2) a reduction of the number of required training data compared to only using a black-box machine learning model; (3) an increased prediction accuracy compared to only using a black-box machine learning model; (4) and an increased compliance with physical boundary conditions by applying the additional output constraints.Item Open Access Influence of geometry variations during pyrometric temperature measurement in laser material processing(2024) Traunecker, David; Jarwitz, Michael; Michalowski, Andreas