Method and device for setting operating parameters of a laser material processing machine

A test stand for a laser material processing machine, the test stand configured to set operating parameters of the laser material processing machine, using Bayesian optimization of a data-based model, the test stand comprising:

• an input interface configured to receive from sensors of the laser material processing machine, at least one experimentally ascertained measured variable of the laser material processing machine;
• a processor;
• a computer-readable memory medium;
• and
• an output interface,
• wherein the test stand is configured to:
• train 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 received via the input interface, 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;
• wherein the test stand is further configured to:
• set, via the output interface, the operating parameters of the laser material processing machine using the trained data-based model;
• wherein the laser material processing machine is configured to perform laser material processing, based on the set operating parameters.

A non-transitory machine-readable memory medium on which is stored a computer program for setting operating parameters of a laser material processing machine, using Bayesian optimization of a data-based model, the computer program, when executed by a computer, causing the computer to perform 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.