The keyhole produced during deep penetration laser welding emits a plume of hot metal vapor and particles. The interaction between the plume and the incident laser beam results in beam scattering, absorption, and phase front deformation. The combination of scattering and absorption leads to a partial extinction of the laser beam, while the phase front deformation adversely effects the beam quality. In this study we present a measurement setup which allows for diagnostics of the beam characteristics after interaction with the plume. This is achieved by utilizing an additional measurement beam, which is coaxially aligned to the high-power laser beam used for welding. The experimental procedure presented here enables high-frequency measurements of the caustic changes and relative power losses of the measurement beam. The measurements obtained provide a quantification of the various interaction mechanisms between the laser beam and vapor plume. This knowledge is crucial to prevent weld defects, which result from the adverse effects of the vapor plume on the laser beam.
The flexibility of new laser sources and process-monitoring enables new possibilities in laser-based production technology, for instance the combination of different laser processes with many adjustable parameters. The fusion of domain knowledge and probabilistic models in the form of hybrid models allows an efficient optimization of these processes with machine learning. This can be a key technology to realize self-learning laser-based universal machines in the future. The article discusses some examples where algorithm-based optimization, partly supported by hybrid models, can already greatly reduce the effort required to find suitable process parameters.
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