Open Access

Tobias Störk, Tobias Seyler, Markus Fratz, Alexander Bertz, Stefan Hensel, Daniel Carl

• Digital holographic multiwavelength sensor systems integrated in the production line on multi-axis systems such as robots or machine tools are exposed to unknown, complex vibrations that affect the measurement quality. To detect vibrations during the early steps of hologram reconstruction, we propose a deep learning approach using a deep neural network trained to predict the standard deviation ofDigital holographic multiwavelength sensor systems integrated in the production line on multi-axis systems such as robots or machine tools are exposed to unknown, complex vibrations that affect the measurement quality. To detect vibrations during the early steps of hologram reconstruction, we propose a deep learning approach using a deep neural network trained to predict the standard deviation of the hologram phase. The neural network achieves 96.0% accuracy when confronted with training-like data while it achieves 97.3% accuracy when tested with data simulating a typical production environment. It performs similar to or even better than comparable classical machine learning algorithms. A single prediction of the neural network takes 35 µs on the GPU.…