Abstract: A method for training a data-based model to ascertain an energy input of a laser welding machine into a workpiece as a function of operating parameters of the laser welding machine. The training is carried out as a function of an ascertained number of spatters.

Abstract: A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the laser material processing is sufficiently good. The Bayesian optimization taking place with the aid of a data-based process model, and it being taken into consideration during the variation of the process parameters how probable it is that a variable which characterizes a quality of the result is within predefinable boundaries.

Abstract: A computer-implemented method for operating a laser material processing machine. Process parameters are varied with the aid of Bayesian optimization until a result of the manufacturing, in particular the laser material processing, is sufficiently good. The Bayesian optimization is carried out with the aid of a data-based process model in a first phase, the data-based process model being trained as a function of estimated results. In a second phase, the data-based process model is trained as a function of the ascertained result resulting upon activation of the laser material processing machine.

Abstract: A computer-implemented method for operating a laser material processing machine. An estimated result is ascertained as a function of predefined process parameters, which characterize how good an actual result of the laser material processing will be, and the process parameters are varied by means of Bayesian optimization with the aid of a data-based model, until an actual result of the laser material processing is sufficient enough.