Ultrashort pulse (USP) laser processing enables micrometre-precision surface-structuring of a wide variety of materials. The machining of metals is almost melt-free, which opens possibilities for numerous applications in many industrial sectors. The diversity of applications as well as processable materials continuously require process development, which is highly time-consuming. In addition, the process itself often takes long time. Moreover, the constant requirement for more efficiency leads to a further acceleration of the processing. The resulting need for detection and elimination of any instabilities as early as possible increases the importance of monitoring of surface properties during the machining.





The challenge of monitoring ultrashort-pulse (USP) laser microstructuring lies in the stringent requirements for both spatial and temporal accuracy. Additionally, the monitoring system must not interfere with the processing. Here, this challenge is addressed by employing high-speed off-axis collection of secondary optical emissions. The spatial information is derived from the current processing position, which is recorded synchronously with the emission intensities. An FPGA-based system is used for real-time data collection, synchronization, analysis, and feedback generation. Defects that arise during machining are located as they appear on the workpiece surface, and can trigger a correction procedure, such as a laser-polishing pass. Furthermore, we compare this method with a data-driven approach using a model that analyzes heatmaps created from photodiode time series combined with the scan positions. A neural network, trained with labels generated by the analytical algorithm and human assistance, detects defects even when the analytical method fails.