Method and device for operating a laser material processing machine

A test stand for a laser material processing machine, the test stand configured to:

• perform laser material processing using first process parameters, and detecting a variable characterizing a result of the laser material processing;
• vary the process parameters by means of Bayesian optimization based on the first process parameters and the detected variable to produce second process parameters;
• ascertain an estimated result of the laser material processing as a function of the second process parameters produced using the Bayesian optimization, which estimates the variable characterizing the result of the laser material processing;
• evaluating a cost function based on the estimated variable;
• when the evaluated cost function is above a threshold, further varying the process parameters by means of the Bayesian optimization based on the second process parameters and the estimated variable to produce third process parameters;
• and
• when the evaluated cost function is below the threshold, performing the laser material processing using the second process parameters.

A non-transitory machine-readable memory medium on which is stored a computer program for operating a laser material processing machine, the computer program, when executed by a computer, causing the computer to perform the following steps:

• perform laser material processing using first process parameters, and detecting a variable characterizing a result of the laser material processing;
• vary the process parameters by means of Bayesian optimization based on the first process parameters and the detected variable to produce second process parameters;
• ascertain an estimated result of the laser material processing as a function of the second process parameters produced using the Bayesian optimization, which estimates the variable characterizing the result of the laser material processing;
• evaluating a cost function based on the estimated variable;
• when the evaluated cost function is above a threshold, further varying the process parameters by means of the Bayesian optimization based on the second process parameters and the estimated variable to produce third process parameters;
• and
• when the evaluated cost function is below the threshold, performing the laser material processing using the second process parameters.