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.
Scanning ultra-short pulse laser ablation is a very flexible technology that can be used for the subtractive manufacturing of complex three-dimensional structures with precision requirements on micrometer level. In our studies, inadvertent periodic deviations at the bottom of ablated cavities in silicon were observed after laser ablation with ultra-short laser pulses. We introduce the hypothesis of an interdependency between the ablation process and ultrasonic resonant acoustic waves, also known as standing waves, forming in the air within the ablated volume. Using basic acoustic wave equations, the corresponding periodicity of the deviations at the bottom surface of the cavities is described with good agreement to our experimental data.
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