Abstract
Directed Energy Deposition (DED) is a highly localized process in which metal powders are added to a laser-induced molten pool. The shape and size of the melt pool ultimately determine the local cooling/solidification rate and, thus, the material's microstructure and properties. To study melt pool shape, in situ X-ray imaging techniques have been used. However, the data afterwards typically are manually analysed, which creates a bottleneck in understanding fundamental phenomena in DED. Here, a promising method to automatically extract melt pool shape and dimensions from in situ X-ray DED melt pool images using templates and Bayesian reasoning is proposed.
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