Galvanometer laser machining is a well-established laser machining technique in which the laser beam is positioned on a working field by means of mirrors mounted in galvanometers. Nonetheless, new applications make necessary the development of innovative techniques for increasing the performance of such systems. Aside all the advantages of this technique like given resolution, repeatability and velocity, a limited working area is an important drawback. In this thesis work, the limitations of different state-of-the-art schemes for increasing the working field of galvanometer laser machining systems are examined. The necessity of a new strategy for reducing present deviations introducing a vision system is established. The construction of an error vector and calculation of coordinate’s transformations to improve precision are presented. In this work, the “Arithmetic mean transformation”, “individual correction transformation”, “iterative inverse distance weighting transformation” and a stitching approach denominated “Auto-stitching” are presented and demonstrated as methods for reducing inherent deviations in galvanometer scanning systems for large area processing.

Galvanometer laser machining is a well-established laser machining technique in which the laser beam is positioned on a working field by means of mirrors mounted in galvanometers. Nonetheless, new applications make necessary the development of innovative techniques for increasing the performance of such systems. Aside all the advantages of this technique like given resolution, repeatability and velocity, a limited working area is an important drawback. In this thesis work, the limitations of different state-of-the-art schemes for increasing the working field of galvanometer laser machining systems are examined. The necessity of a new strategy for reducing present deviations introducing a vision system is established. The construction of an error vector and calculation of coordinate’s transformations to improve precision are presented. In this work, the “Arithmetic mean transformation”, “individual correction transformation”, “iterative inverse distance weighting transformation” and a stitching approach denominated “Auto-stitching” are presented and demonstrated as methods for reducing inherent deviations in galvanometer scanning systems for large area processing.