Modelling of geometry for directed energy deposition via machine learning

Predicting the geometry of bead in multi-track and multi-layer Directed Energy Deposition (DED) presents a challenge due to variations in process parameters, resulting in changes in geometry from one layer or track to another. To address this issue, a Long Short-Term Memory (LSTM) model is applied t...

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Bibliografiska uppgifter
Huvudupphovsman: Hong, Weidong
Övriga upphovsmän: Li Hua
Materialtyp: Final Year Project (FYP)
Språk:English
Publicerad: Nanyang Technological University 2023
Ämnen:
Länkar:https://hdl.handle.net/10356/167105