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.
Part quality and building time during selective laser melting strongly correlates with the quality of the melting tracks and the melting rate respectively. Conventional processes can be improved by changing their parameters. Increase of the building rate is achieved by modifying the laser beam properties, e.g. spot size and laser power, and process parameters, such as layer thickness and scanning speed. However, process acceleration often leads to appearance of balling, spatters, evaporation and undercuts with subsequent degradation of component quality. The present investigation introduces an innovative way towards increasing of melting rate without lowering part quality. In our approach, the laser beam energy is efficiently distributed on the powder bed by means of beam splitting. This leads to the generation of high volume melt pools. Finally, an outlook on further increase of the melting rate is given.
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