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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| Materialtyp: | Final Year Project (FYP) |
| Språk: | English |
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Nanyang Technological University
2023
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| Länkar: | https://hdl.handle.net/10356/167105 |