US 12,282,319 B2
Method and device for setting operating parameters of a laser material processing machine
Alexander Ilin, Ludwigsburg (DE); Anna Eivazi, Renningen (DE); Heiko Ridderbusch, Schwieberdingen (DE); Julia Vinogradska, Stuttgart (DE); and Petru Tighineanu, Ludswigsburg (DE)
Assigned to ROBERT BOSCH GMBH, Stuttgart (DE)
Filed by Robert Bosch GmbH, Stuttgart (DE)
Filed on Oct. 25, 2021, as Appl. No. 17/452,163.
Claims priority of application No. 10 2020 213 813.3 (DE), filed on Nov. 3, 2020.
Prior Publication US 2022/0137608 A1, May 5, 2022
Int. Cl. G05B 19/418 (2006.01); G06F 18/214 (2023.01); G06F 18/2415 (2023.01)
CPC G05B 19/41885 (2013.01) [G05B 19/4183 (2013.01); G05B 19/4188 (2013.01); G06F 18/214 (2023.01); G06F 18/24155 (2023.01)] 14 Claims
OG exemplary drawing
 
1. A method for setting operating parameters of a laser material processing machine, using Bayesian optimization of a data-based model, the method comprising the following steps:
receiving via an input interface of a test stand, from sensors of the laser material processing machine, at least one experimentally ascertained measured variable of the laser material processing machine;
training the data-based model, in the Bayesian optimization, to output a model output variable which characterizes an operating mode of the laser material processing machine, as a function of the operating parameters, the training of the data- based model taking place as a function of the at least one experimentally ascertained measured variable of the laser material processing machine, and the training also taking place as a function of at least one simulatively ascertained simulation variable, the measured variable and the simulation variable each characterizing the operating mode of the laser material processing machine, the measured variable and/or the simulation variable being transformed during the training using an affine transformation, wherein the measured variable and/or the simulation variable is multiplied during the affine transformation by a factor, and the factor is selected as a function of a simulative model uncertainty and as a function of an experimental model uncertainty;
setting, via an output interface of the test stand, the operating parameters of the laser material processing machine using the trained data-based model;
the laser material processing machine performing laser material processing based on the set operating parameters.