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Three-dimensional microprinting via two-photon absorption is the additive manufacturing technique of choice for complex micro-optical systems. Since post-processing of printed micro-optics is not possible in most cases, deviations between design and printed samples affect the intended function and therefore need to be minimized. This is a difficult task since important material properties such as shrinkage and refractive index depend on the cross-linking density and thus on the process parameters. We present first results towards a detailed prediction of 3D printed structures based on a modeling approach combined with machine learning to adjust the corresponding process parameters.
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Georg von Freymann, Julian Hering, Svenn Enns, Nicolas Lang, "A machine-learning approach for reliable process parameter prediction in three-dimensional additive microprinting," Proc. SPIE PC12939, High-Power Laser Ablation VIII, PC129390Z (11 April 2024); https://doi.org/10.1117/12.3014314