%0 Generic %1 jarwitz2024application %A Jarwitz, Michael %A Michalowski, Andreas %D 2024 %K myown welding laser peer Modeling %T Application of a physics-informed hybrid model with additional output constraints for the prediction of the threshold of deep-penetration laser welding %X The 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 blackbox 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.

@presentation{jarwitz2024application, abstract = {The 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 blackbox 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.}, added-at = {2025-02-03T17:43:53.000+0100}, author = {Jarwitz, Michael and Michalowski, Andreas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2f51f7f36f0337c98dcb01b837a8d4c9d/ifsw}, eventdate = {4-7 Novemeber 2024}, eventtitle = {43rd International Congress on Applications of Lasers & Electro-Optics (ICALEO 2024)}, interhash = {03d81cab9525981894415535861d70a6}, intrahash = {f51f7f36f0337c98dcb01b837a8d4c9d}, keywords = {myown welding laser peer Modeling}, timestamp = {2025-02-03T17:43:53.000+0100}, title = {Application of a physics-informed hybrid model with additional output constraints for the prediction of the threshold of deep-penetration laser welding}, venue = {Hollywood, CA, USA}, year = 2024 }