Additive manufacturing: DED process parameters optimization via experiments (E)

Directed Energy Deposition (DED), a prominent technique within additive manufacturing, is particularly valued for its efficiency in repairing and fabricating components with precision, which is integral in sectors like aerospace, healthcare, and defense. This investigation centers on 316L stainle...

Full description

Saved in:
Bibliographic Details
Main Author: Tong, Gao Rui
Other Authors: Li Hua
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
DED
Online Access:https://hdl.handle.net/10356/177814
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary:Directed Energy Deposition (DED), a prominent technique within additive manufacturing, is particularly valued for its efficiency in repairing and fabricating components with precision, which is integral in sectors like aerospace, healthcare, and defense. This investigation centers on 316L stainless steel, chosen for its robust tensile strength and resistance to corrosion. The study explores the impact of five DED process parameters: Laser Power, Scanning Speed, Powder Mass Flow Rate, XY-Incremental Ratio, and Z-Incremental Ratio. Adjustments to these parameters are scrutinized for their correlation with key microstructural aspects, namely Grain Area, Grain Ellipse Aspect Ratio, and Grain Angle, as well as their collective influence on the Ultimate Tensile Strength of multi-layer multitrack depositions. A meticulous analysis was conducted utilizing Electron Backscatter Diffraction (EBSD) to assess the microstructure. The investigation established a discernible relationship between the selected process parameters and the resulting microstructural features. The findings contribute valuable insights into the optimization of DED settings to enhance material properties, with potential implications for the future of material engineering in additive manufacturing.