Microstructural characterization of parts produced by the hybrid directed energy deposition (HDED) process based on surface type and additive parameters

• Main Author: Tang, Javer Tian Shun
• Other Authors: Li Hua
• Format: Final Year Project
• Language: English
• Published: 2019
• Subjects: DRNTU::Engineering::Manufacturing::CAD/CAM systems
• Online Access: http://hdl.handle.net/10356/77630
• Institution: Nanyang Technological University

Inconel is a Nickel based superalloy that is highly resistant to heat and corrosion. It has high creep resistance in high temperatures hence it was widely used as airplane turbine engine blades and power generation turbine blades due to their ability to remain stable under high operating temperatures. Additive manufacturing is gaining traction as the choice manufacturing technique for the production of Inconel. A great amount of research had been devoted to its manufacturing technique using additive manufacturing however little research focuses on the different surfaces that the Inconel was printed on. This project will examine the how different surface roughness of the base Inconel substrates would affect the mechanical properties of the printed Inconel samples using the hybrid directed energy deposition technique. Different surface roughness of the samples will be prepared using the machining capabilities of the hybrid directed energy deposition machine. Three different surface roughness values were investigated: Machined, Remelted and As Printed. The samples were cut in half to reveal their cross-section areas and hot mounted for post-processing. After polishing, their porosity values were investigated using imaging software imagej. Their hardness values were also investigated using the Vickers Hardness method. Lastly, the samples were viewed under the SEM. The results showed that Inconel printed on a machined surface produced the lowest porosity values, hardness values are fairly similar with differences noticeable only on the modified surface and there was not much difference in the SEM image analysis.