Data-Driven Prediction and Uncertainty Quantification of Process Parameters for Directed Energy Deposition
Florian Hermann,
Andreas Michalowski,
Tim Brünnette
et al.
Abstract:Laser-based directed energy deposition using metal powder (DED-LB/M) offers great potential for a flexible production mainly defined by software. To exploit this potential, knowledge of the process parameters required to achieve a specific track geometry is essential. Existing analytical, numerical, and machine-learning approaches, however, are not yet able to predict the process parameters in a satisfactory way. A trial-&-error approach is therefore usually applied to find the best process parameters. Thi… Show more
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