The determination of appropriate process parameters is crucial for the development of laser welding processes. This usually requires extensive
and time-consuming experimentation combined with expert knowledge. To reduce the number of experiments required to determine appropriate
process parameters, Bayesian optimization was used in this work. Bead on plate laser welding of AA5754 samples was performed while
optimizing the laser power, the welding speed, the focus position and the power distribution in the core-ring fiber laser system with the objective
of achieving welds with a specific weld depth and low number of defects at high welding speeds. The welds were evaluated using X-ray imaging
and height measurements. A cost function was developed to quantify the overall weld quality based on the weld properties. It is demonstrated
that the Bayesian optimizer can determine appropriate process parameters for the given objective, based on a cost function, within a comparatively
small number of 29 experiments.
%0 Generic
%1 Haas.2024.Improving
%A Haas, Michael
%A Onuseit, Volkher
%A Powell, John
%A Zaiß, Felix
%A Wahl, Johannes
%A Menold, Tobias
%A Hagenlocher, Christian
%A Michalowski, Andreas
%D 2024
%K myown Welding Laser BayesianOptimization X-ray
%T Improving the weld seam quality in laser welding processes by means of Bayesian optimization
%X The determination of appropriate process parameters is crucial for the development of laser welding processes. This usually requires extensive
and time-consuming experimentation combined with expert knowledge. To reduce the number of experiments required to determine appropriate
process parameters, Bayesian optimization was used in this work. Bead on plate laser welding of AA5754 samples was performed while
optimizing the laser power, the welding speed, the focus position and the power distribution in the core-ring fiber laser system with the objective
of achieving welds with a specific weld depth and low number of defects at high welding speeds. The welds were evaluated using X-ray imaging
and height measurements. A cost function was developed to quantify the overall weld quality based on the weld properties. It is demonstrated
that the Bayesian optimizer can determine appropriate process parameters for the given objective, based on a cost function, within a comparatively
small number of 29 experiments.
@presentation{Haas.2024.Improving,
abstract = {The determination of appropriate process parameters is crucial for the development of laser welding processes. This usually requires extensive
and time-consuming experimentation combined with expert knowledge. To reduce the number of experiments required to determine appropriate
process parameters, Bayesian optimization was used in this work. Bead on plate laser welding of AA5754 samples was performed while
optimizing the laser power, the welding speed, the focus position and the power distribution in the core-ring fiber laser system with the objective
of achieving welds with a specific weld depth and low number of defects at high welding speeds. The welds were evaluated using X-ray imaging
and height measurements. A cost function was developed to quantify the overall weld quality based on the weld properties. It is demonstrated
that the Bayesian optimizer can determine appropriate process parameters for the given objective, based on a cost function, within a comparatively
small number of 29 experiments.},
added-at = {2024-09-23T09:27:54.000+0200},
author = {Haas, Michael and Onuseit, Volkher and Powell, John and Zaiß, Felix and Wahl, Johannes and Menold, Tobias and Hagenlocher, Christian and Michalowski, Andreas},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2155f6c41af538ddfb9505e482c5b03b4/ifsw},
eventdate = {15-19 September 2024},
eventtitle = {13th CIRP Conference on Photonic Technologies [LANE 2024]},
interhash = {22e6c5992b446d3c9991215364a70849},
intrahash = {155f6c41af538ddfb9505e482c5b03b4},
keywords = {myown Welding Laser BayesianOptimization X-ray},
language = {English},
timestamp = {2024-09-23T09:27:54.000+0200},
title = {Improving the weld seam quality in laser welding processes by means of Bayesian optimization},
venue = {Fürth, Germany},
year = 2024
}
