Abstract
This work deals with the planning and fabrication of a functionally gradient copper-nickel composition via Laser Metal Deposition (LMD). Various compositions of copper and nickel were made by blending different weight percentages which were then sequentially deposited to fabricate functionally gradient copper-nickel thin-wall structures. Analyses were performed by sectioning the thin-wall samples for metallographic, hardness, X-ray diffraction (XRD) and Energy Dispersive X-ray Spectroscopy (EDS) studies. The fabrication was studied for identifying and corroborating the deposited compositions and their corresponding gradients. XRD analyses were performed to identify the crystal structure of the deposit. EDS analysis was instrumental in identifying the variation in composition and realizing the gradient in between compositions. Consequences of using different laser beam intensity profiles and varying laser power duty cycles were realized by analyzing the copper-nickel concentration trends obtained from EDS analyses. Hardness testing was successful in capturing the decreasing trends in strength with decreasing nickel concentration.
Recommended Citation
S. Karnati et al., "Laser Metal Deposition of Functionally Gradient Materials from Elemental Copper and Nickel Powders," Proceedings of the 26th Annual International Solid Freeform Fabrication Symposium (2015, Austin, TX), pp. 789 - 802, University of Texas at Austin, Aug 2015.
Meeting Name
26th Annual International Solid Freeform Fabrication Symposium -- An Additive Manufacturing Conference, SFF 2015 (2015: Aug. 10-12, Austin, TX)
Department(s)
Mechanical and Aerospace Engineering
Second Department
Materials Science and Engineering
Research Center/Lab(s)
Intelligent Systems Center
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Publication Date
12 Aug 2015
Comments
The authors would like to express their sincere gratitude to the support from National Aeronautics and Space Administration EPSCoR program (Grant# NNX13AM99A), and Intelligent Systems Center at Missouri University of Science and Technology.