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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Main Author: Yeo, Choon Hao
Other Authors: Li Hua
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
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Online Access:https://hdl.handle.net/10356/167562
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Institution: Nanyang Technological University
Language: English
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spelling 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
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Yeo, Choon Hao
DED process parameters optimisation via simulation (C)
description 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.
author2 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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