Method and device for ascertaining the energy input of laser welding using artificial intelligence

A method for training a data-based model to ascertain a variable which characterizes an energy input of a laser welding machine into a workpiece, as a function of operating parameters of the laser welding machine, the method comprising:

• training the data-based model as a function of an ascertained number of spatters.

A method for setting operating parameters of a laser welding machine using Bayesian optimization of a data-based model, the method comprising the following steps:

• training the data-based model as a function of an ascertained number of spatters;
• and
• setting the operating parameters of the laser welding machine using the trained data-based model.

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

• train the data-based model as a function of an ascertained number of spatters;
• and
• set the operating parameters of the laser welding machine using the trained data-based model.

A non-transitory machine-readable memory medium on which is stored a computer program for training a data-based model to ascertain a variable which characterizes an energy input of a laser welding machine into a workpiece, as a function of operating parameters of the laser welding machine, the computer program, when executed by a computer, causing the computer to perform the following:

• training the data-based model as a function of an ascertained number of spatters.