Abstract: A method for analyzing a laser machining process for machining workpieces includes the steps of acquiring at least one sensor data set for the laser machining process and determining a value of at least one physical property of a machining result of the laser machining process based on the at least one sensor data set using a transfer function. The transfer function is formed by a trained neural network. A system for analyzing a laser machining process and a laser machining system including such a system are also disclosed.

Abstract: A method and a system for monitoring a laser machining process includes the steps of: inputting at least one process signal data set of the laser machining process into an autoencoder formed by a deep neural network; generating a reconstructed process signal data set by means of the autoencoder; determining a reconstruction error based on the at least one process signal data set and the at least one reconstructed process signal data set; and detecting an anomaly of the laser machining process based on the determined reconstruction error. A laser machining method includes the method and a laser machining system includes the system.

Abstract: A system for monitoring a laser machining process on a workpiece is disclosed. The system includes: a neuromorphic image sensor configured to generate image data of the laser machining process, and a computing unit configured to determine input data based on the image data, and to determine output data based on the input data by means of a transfer function, the output data containing information about the laser machining process. Further, a method for monitoring a laser machining process on a workpiece is disclosed.