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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Bibliographic Details
Main Author: Hong, Weidong
Other Authors: Li Hua
Format: Final Year Project (FYP)
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167105