DED process parameters optimisation via simulation (C)
As Additive Manufacturing (AM) gains popularity, there is a growing emphasis on refining its processes to fully capitalise on the benefits it offers. Among the various AM techniques used, Directed Energy Deposition (DED) is the most popular, and significant efforts are being made to understand and i...
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2023
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sg-ntu-dr.10356-1675622023-06-03T16:50:25Z DED process parameters optimisation via simulation (C) Yeo, Choon Hao Li Hua School of Mechanical and Aerospace Engineering LiHua@ntu.edu.sg Engineering::Mechanical engineering As Additive Manufacturing (AM) gains popularity, there is a growing emphasis on refining its processes to fully capitalise on the benefits it offers. Among the various AM techniques used, Directed Energy Deposition (DED) is the most popular, and significant efforts are being made to understand and improve its operational parameters, to improve the efficiency and quality of the final product. This Final Year Project (FYP) aims to investigate the optimal input parameters for a DED process. ANSYS Fluent was used to simulate a DED nozzle model and analyse the convergence point of the powder flow. This FYP also investigates the effect turbulence models have on DED simulations. The focal point height obtained from these simulations, using different turbulence models, will be compared with the experimental results. Comparison between simulation and experimental results suggest that there are significant differences when using different turbulence models. The results obtained show that there is potential for new directions towards further refinement of turbulence modelling for DED processes, which can aid in progressing the understanding of DED processes. Bachelor of Engineering (Mechanical Engineering) 2023-05-30T02:32:52Z 2023-05-30T02:32:52Z 2023 Final Year Project (FYP) Yeo, C. H. (2023). DED process parameters optimisation via simulation (C). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167562 https://hdl.handle.net/10356/167562 en B323 application/pdf Nanyang Technological University |
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Engineering::Mechanical engineering Yeo, Choon Hao DED process parameters optimisation via simulation (C) |
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As Additive Manufacturing (AM) gains popularity, there is a growing emphasis on refining its processes to fully capitalise on the benefits it offers. Among the various AM techniques used, Directed Energy Deposition (DED) is the most popular, and significant efforts are being made to understand and improve its operational parameters, to improve the efficiency and quality of the final product.
This Final Year Project (FYP) aims to investigate the optimal input parameters for a DED process. ANSYS Fluent was used to simulate a DED nozzle model and analyse the convergence point of the powder flow. This FYP also investigates the effect turbulence models have on DED simulations. The focal point height obtained from these simulations, using different turbulence models, will be compared with the experimental results.
Comparison between simulation and experimental results suggest that there are significant differences when using different turbulence models. The results obtained show that there is potential for new directions towards further refinement of turbulence modelling for DED processes, which can aid in progressing the understanding of DED processes. |
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Li Hua |
author_facet |
Li Hua Yeo, Choon Hao |
format |
Final Year Project |
author |
Yeo, Choon Hao |
author_sort |
Yeo, Choon Hao |
title |
DED process parameters optimisation via simulation (C) |
title_short |
DED process parameters optimisation via simulation (C) |
title_full |
DED process parameters optimisation via simulation (C) |
title_fullStr |
DED process parameters optimisation via simulation (C) |
title_full_unstemmed |
DED process parameters optimisation via simulation (C) |
title_sort |
ded process parameters optimisation via simulation (c) |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/167562 |
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1772826435680993280 |