Galvanometric scanners, widely used for beam deflection in laser material processing applications such as welding, cutting, and additive manufacturing, provide precise and rapid laser focal spot positioning. However, the growing demand for reduced cycle times and improved positioning accuracy poses significant challenges to their dynamic performance. Inertia-induced discrepancies between desired and actual geometries, especially at acute angles and at high operating speeds, can negatively impact the processing outcome. This study introduces innovative trajectory planning methodologies designed to enhance scanner dynamics without requiring hardware alterations. By optimizing or pre-calculating scanner input trajectories, these approaches effectively counteract inertia-induced deviations, thereby improving geometric accuracy and processing results.

The integration of galvanometer scanners and robotic manipulators in laser material processing poses challenges due to their independent hardware and software architectures. Synchronizing these systems requires precise coordination of robotic motion and galvanometer scanner control, particularly for complex path planning tasks. This paper introduces a ROS-based framework that leverages the Robot Operating System (ROS) and its open-source, modular, and widely-adopted capabilities in robotics to unify the control of robot and galvanometer scanner. A service-oriented, publish-subscribe wrapper is developed to manage scanner mirror positions and robotic joint movements, ensuring accurate laser beam positioning and process consistency. A case study in laser marking validates the framework's functionality, highlighting its adaptability to other applications such as welding and cutting. Future research may explore additional factors such as dynamic power control, deep learning, and vision-based system integration. This scalable and flexible approach demonstrates the potential of ROS to drive innovations in laser material processing.

The integration of collaborative robots (cobots) into laser-based material processing offers significant potential for flexible and cost-efficient automation in remanufacturing, a crucial part of circular economy. Recent advances in laser technology and the adaptability of cobots make them well suited for resource-efficient processes that require precision and flexibility.

This paper presents an approach in which additional sensors are integrated into a cobot to refine part positioning after the operator has roughly learned the initial position. Based on this improved positional accuracy, the robot autonomously performs precise path planning and executes automated laser cleaning tasks. This enables the effective removal of contaminants or coatings, a common requirement in reprocessing operations.

A laser cleaning case study illustrates how sensor fusion, adaptive path optimization and offline simulation improve accuracy and streamline workflows. The results show the potential of cobots to improve precision, simplify human-robot interaction and optimize remanufacturing processes through advanced automation strategies.

Over the past several years, the University of Dayton Research Institute and the US Air Force, have been developing advanced laser (300W fs) surface processing techniques, hollow-core fiber coupling methods, and in-situ process monitoring technologies to enable robotic implementation for large-scale components.

Focusing primarily on composites to date, a body of mechanical bond-strength, thermal, and laser quality data will be discussed. These applications are enabled by the developed in-situ process monitoring and feed-forward control which allow large complex structures to be processed while remaining in focus. Data was collected to measure resultant laser beam quality of the automated system during motion to ensure process stability.

Finally, process rate and cost considerations will be discussed and how those variables impact not only the applicability of advanced laser processes to current needs, but also how these considerations may drive the direction of research and commercial laser offerings into the future.

The integration of AI methods into photonic production chains, especially when evaluating data to assess the process result, is associated with high expectations. Machine learning should not only make processes and quality information more reliable, but also provide real, tangible results, physical quantities that describe the process result in µm or µOhm or kN.

Furthermore, shaped light has been shown to play a crucial role in improving the precision and versatility of laser material processing. By adjusting the intensity distribution of the laser beam, shaped light enables a more controlled energy input and thus a larger and significantly more robust process window.

It seems clear that the combination of these two innovative approaches marks a new path in industrial laser material processing and that both have a positive influence on each other. This is particularly beneficial for demanding welds in the context of e-mobility and the specific materials used.

The framework for characterizing laser beams according to ISO 11146 gives appropriate results for Gaussian to top-hat shaped beams in their focal planes for quantities such as 2nd-order moment beam width (dσ), Rayleigh length (zR) and beam propagation factor (M2).

These quantities are not ideally suited to describe laser beams created by a range of new technologies that generate ring-shaped beams. This deficit is compounded when “mixed” beams with a central beam surrounded by a ring beam are used.

A new set of parameters to describe ring-shaped beams more adequately and with a focus on the application is presented. Both for single planes and entire beam caustics, we describe how beam features are extracted. A comparison of the new method with the ISO 11146 approach is provided, based on real world examples. The presented method can serve as a framework for a future norm on ring-beam analysis.