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...

Full description

Saved in:
Bibliographic Details
Main Author: Lee, Anthony Kai Zhe
Other Authors: Li Hua
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2023
Subjects:
Online Access:https://hdl.handle.net/10356/167312
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
id sg-ntu-dr.10356-167312
record_format dspace
spelling sg-ntu-dr.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
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
Lee, Anthony Kai Zhe
DED process parameters optimization via experiments
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.
author2 Li Hua
author_facet Li Hua
Lee, Anthony Kai Zhe
format Final Year Project
author Lee, Anthony Kai Zhe
author_sort Lee, Anthony Kai Zhe
title DED process parameters optimization via experiments
title_short 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_sort ded process parameters optimization via experiments
publisher Nanyang Technological University
publishDate 2023
url https://hdl.handle.net/10356/167312
_version_ 1772827364260052992