Analytical modelling of residual stress in additive manufacturing

By O. Fergani, Filippo Berto, T. Welo, S. Liang. 2017

161 citations recorded by the source corpus.

Subfields: Automotive Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/a721d4249de6ae71682f005dc77ccf656be6acfd

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. T. Mukherjee
2. T. DebRoy
3. H. Wei
4. Amitava De
5. Wei Zhang
6. J. Elmer
7. J. Zuback
8. J. Milewski

In Mechanical Engineering

1. T. DebRoy
2. T. Mukherjee
3. H. Wei
4. Amitava De
5. J. Elmer
6. Wei Zhang
7. J. Zuback
8. J. Milewski

Citations in the corpus

• Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives (2019, unknown): “[8] proposed an analytical model to assess residual stress in the AM process of metallic materials; and Chen et al.”
• Review: The Impact of Metal Additive Manufacturing on the Aerospace Industry (2019, background): “With its ability to produce compressive stresses on the impacted substrate and lack of an HAZ, SPD has been shown to restore and mitigate material degradation in structural aircraft components that have been exposed to both corrosion and wear for parts such as load bearing wings, horizontal stabiliz”
• Additive manufacturing of metallic components – Process, structure and properties (2018, background): “developed an analytical assessment of residual stresses in a homogeneous semi-infinite medium [228].”