Influence of energy density on metallurgy and properties in metal additive manufacturing

By S. M. Yusuf, N. Gao. 2017

147 citations recorded by the source corpus.

Subfields: Automotive Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/2f6f67a26acb9e6afd9ac10aa13bbcd3154ed2cb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. Shengmin Guo
2. A. H. Ettefagh
3. J. Raush
4. Jun Wei
5. Zhiheng Hu
6. Xu Song
7. Wei Zhai
8. B. Nagarajan

In Mechanical Engineering

1. Shengmin Guo
2. A. H. Ettefagh
3. J. Raush
4. N. Gao
5. S. M. Yusuf
6. Zhiheng Hu
7. Yifei Chen
8. Jun Wei

Citations in the corpus

• Development of Micro Selective Laser Melting: The State of the Art and Future Perspectives (2019, background): “The motivation for heat treatment is to reduce or eliminate defects, control the microstructure, improve the properties, and relieve residual stresses [21,56,58].”
• Corrosion performance of additively manufactured stainless steel parts: A review (2020, methodology): “A more systematic optimization method for AM processing parameters to attain the densest possible AM SS parts is via laser energy density (LED), also known as volumetric energy density (VED), calculated by dividing laser energy by the product of scanning rate, hatch spacing and layer thickness [36,3”
• Investigation on Porosity and Microhardness of 316L Stainless Steel Fabricated by Selective Laser Melting (2017, background): “In addition, the additive nature of this process allows components to be manufactured with much less raw material wastage which could reduce material costs and the environmental footprint [1,2].”
• An overview of powder granulometry on feedstock and part performance in the selective laser melting process (2017, unknown): “Furthermore, part optimisation in SLM often requires the control of key process parameters including (1) Laser Power, (2) Scan Speed, (3) Hatch Spacing and (4) Layer thickness which generates an energy density, E (J/mm) governed by Equation 1 [21]:”
• Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, background): “For example, energy density has been shown to have an obvious influence on solidification and metallurgy during AM processing, as well as on the resulting microstructures and mechanical properties of the fabricated parts [65].”