Representation of surface roughness in fused deposition modeling

By Daekeon Ahn, J. Kweon, Soon-man Kwon, Jung‐il Song, Seok-Hee Lee. 2009

432 citations recorded by the source corpus.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/bb74c3429c84a5f21b7f2d2b19c9e408151ae5f6

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. Yan Wang
2. Haixi Wu
3. Zhonghua Yu
4. A. Townsend
5. F. Chabert
6. Tzu‐Liang Bill Tseng
7. M. Alsoufi
8. Hoejin Kim

In Industrial and Manufacturing Engineering

1. A. Townsend
2. F. Chabert
3. Tzu‐Liang Bill Tseng
4. M. Alsoufi
5. Hoejin Kim
6. Shahriar Bakrani Balani
7. Yan Wang
8. Richard Leach

In Mechanical Engineering

1. Yan Wang
2. Haixi Wu
3. Zhonghua Yu
4. Nicola Senin
5. A. Townsend
6. F. Chabert
7. Tzu‐Liang Bill Tseng
8. M. Alsoufi

Citations in the corpus

• Influence of printing parameters on the stability of deposited beads in fused filament fabrication of poly(lactic) acid (2019, background): “Meanwhile, numerous studies have been conducted to measure and decrease the surface roughness of parts manufactured via the FFF process [14].”
• Numerical modeling of the strand deposition flow in extrusion-based additive manufacturing (2018, neutral): “However, these parameters are rather used as fitting parameters of the model, as they are difficult to estimate [23], without CFD simulations.”
• Surface Roughness Quality and Dimensional Accuracy—A Comprehensive Analysis of 100% Infill Printed Parts Fabricated by a Personal/Desktop Cost-Effective FDM 3D Printer (2018, background): “Fundamentally, FDM 3D technology is mainly based on the layer manufacturing process [14] and the fundamental principle is quite straightforward [15].”
• Experimental validation of a numerical model for the strand shape in material extrusion additive manufacturing (2018, neutral): “Therefore, the processing conditions must be well controlled for an optimal rendering of the surfaces [11, 12, 13].”
• In situ monitoring of FDM machine condition via acoustic emission (2015, methodology): “Again, the mean and standard deviation of ABS-Energy for each segment were calculated and linearly scaled to the range of [0,1].”
• A Systematic Survey of FDM Process Parameter Optimization and Their Influence on Part Characteristics (2019, neutral): “The following is a list of commonly used FDM machines: Fortus FDM 400 mc machine (Stratasys, Eden Prairie, MN, USA) [10,21,22], Fortus 360 mc machine (Stratasys, Eden Prairie, MN, USA) [23], FDM 200 mc machine (Stratasys, Eden Prairie, MN, USA) [24], FDM 1650 machine (Stratasys, Eden Prairie, MN, US”