Long-short term memory networks for modeling track geometry in laser metal deposition.

ABSTRACT: Modeling metal additive manufacturing processes is of great importance because it allows for the production of objects that are closer to the desired geometry and mechanical properties. Over-deposition often takes place during laser metal deposition, especially when the deposition head changes its direction and results in more material being melted onto the substrate. Modeling over-deposition is one of the necessary steps toward online process control, as a good model can be used in a closed-loop system to adjust the deposition parameters in real-time to reduce this phenomenon. In this study, we present a long-short memory neural network to model over-deposition. The model has been trained on simple geometries such as straight tracks, spiral and V-tracks made of Inconel 718. The model show

SUBMITTER: Perani M 

PROVIDER: S-EPMC10320393 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature