Surface roughness prediction using measured data and interpolation in layered manufacturing

By Daekeon Ahn, Ho-Chan Kim, Seok-Hee Lee. 2009

188 citations recorded by the source corpus.

Subfields: Computational Mechanics, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/29a230501ca1cae999f57be29346d20be26f49e2

How researchers see this paper

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

In Computational Mechanics

1. Ahmed Hussein
2. C. Yan
3. Liang Hao

In Mechanical Engineering

1. C. Yan
2. Liang Hao
3. Philippe G. Young
4. Ahmed Hussein
5. Matthijs Langelaar
6. Christopher John Tuck
7. Yaoyao Fiona Zhao
8. Ian Ashcroft

In Mechanics of Materials

1. C. Yan

Citations in the corpus

• Prediction of surface roughness in extrusion-based additive manufacturing with machine learning (2019, methodology): “[6] developed a method to predict the surface roughness of 3D printed parts based on the geometric data in an STL file.”
• Metallic additive manufacturing: state-of-the-art review and prospects (2012, background): “When producing parts, to minimize stair effect [11] on complex parts, hence limiting the finishing operations, smaller layer thickness should be chosen.”
• Advanced lightweight 316L stainless steel cellular lattice structures fabricated via selective laser melting (2014, background): “9 can be diminished by decreasing the layer thickness, but this increases the time required to complete the fabrication [17].”