DED process parameters optimization via experiments
The additive manufacturing (AM) technique Directed Energy Deposition (DED) specializes in mending, quick prototyping, and low-volume part manufacturing. Due to its tremendous advantages, it is commonly employed in various sectors, such as aerospace, healthcare, and the military. A common material to...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2023
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| Online Access: | https://hdl.handle.net/10356/167312 |
| _version_ | 1811690089140453376 |
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| author | Lee, Anthony Kai Zhe |
| author2 | Li Hua |
| author_facet | Li Hua Lee, Anthony Kai Zhe |
| author_sort | Lee, Anthony Kai Zhe |
| collection | NTU |
| description | The additive manufacturing (AM) technique Directed Energy Deposition (DED) specializes in mending, quick prototyping, and low-volume part manufacturing. Due to its tremendous advantages, it is commonly employed in various sectors, such as aerospace, healthcare, and the military. A common material to work with is 316L stainless steel because of its excellent tensile and corrosion-resistant qualities.
For this project, 5 process parameters (Laser Power, Scanning Speed, Powder Mass flow rate, XY-Incremental ratio, and Z Incremental ratio) were varied with different values to get a correlation with the microstructure parameters (Grain Area, Grain Ellipse aspect ratio, and Grain angle) and the mechanical property (Ultimate Tensile Strength) of a multi-layer multi-track deposition.
The microstructure was analysed using Electron Backscatter Diffraction (EBSD) method and a relationship between the process parameters, microstructure parameters, and the ultimate tensile strength was concluded. |
| first_indexed | 2024-10-01T05:58:27Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/167312 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:58:27Z |
| publishDate | 2023 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1673122023-05-27T16:50:54Z DED process parameters optimization via experiments Lee, Anthony Kai Zhe Li Hua School of Mechanical and Aerospace Engineering LiHua@ntu.edu.sg Engineering::Mechanical engineering The additive manufacturing (AM) technique Directed Energy Deposition (DED) specializes in mending, quick prototyping, and low-volume part manufacturing. Due to its tremendous advantages, it is commonly employed in various sectors, such as aerospace, healthcare, and the military. A common material to work with is 316L stainless steel because of its excellent tensile and corrosion-resistant qualities. For this project, 5 process parameters (Laser Power, Scanning Speed, Powder Mass flow rate, XY-Incremental ratio, and Z Incremental ratio) were varied with different values to get a correlation with the microstructure parameters (Grain Area, Grain Ellipse aspect ratio, and Grain angle) and the mechanical property (Ultimate Tensile Strength) of a multi-layer multi-track deposition. The microstructure was analysed using Electron Backscatter Diffraction (EBSD) method and a relationship between the process parameters, microstructure parameters, and the ultimate tensile strength was concluded. Bachelor of Engineering (Mechanical Engineering) 2023-05-25T08:44:39Z 2023-05-25T08:44:39Z 2023 Final Year Project (FYP) Lee, A. K. Z. (2023). DED process parameters optimization via experiments. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/167312 https://hdl.handle.net/10356/167312 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Mechanical engineering Lee, Anthony Kai Zhe DED process parameters optimization via experiments |
| title | DED process parameters optimization via experiments |
| title_full | DED process parameters optimization via experiments |
| title_fullStr | DED process parameters optimization via experiments |
| title_full_unstemmed | DED process parameters optimization via experiments |
| title_short | DED process parameters optimization via experiments |
| title_sort | ded process parameters optimization via experiments |
| topic | Engineering::Mechanical engineering |
| url | https://hdl.handle.net/10356/167312 |
| work_keys_str_mv | AT leeanthonykaizhe dedprocessparametersoptimizationviaexperiments |