Studying the Effect of Short Carbon Fiber on Fused Filament Fabrication Parts Roughness via Machine Learning

  1. García-Collado, Alberto 1
  2. Romero-Carrillo, Pablo Eduardo 1
  3. Dorado-Vicente, Rubén 1
  4. Gupta, Munish Kumar 2
  1. 1 Department of Mechanical and Mining Engineering, University of Jaén, EPS de Jaén, Jaén, Spain.
  2. 2 Faculty of Mechanical Engineering, Opole University of Technology, Opole, Poland.
3D Printing and Additive Manufacturing

ISSN: 2329-7662 2329-7670

Year of publication: 2022

Volume: 0

Issue: 0

Type: Article

DOI: 10.1089/3DP.2021.0304 GOOGLE SCHOLAR lock_openOpen access editor

More publications in: 3D Printing and Additive Manufacturing


Cited by

  • Web of Science Cited by: 1 (15-10-2023)
  • Dimensions Cited by: 1 (26-03-2023)

JCR (Journal Impact Factor)

  • Year 2022
  • Journal Impact Factor: 3.1
  • Journal Impact Factor without self cites: 2.9
  • Article influence score: 0.768
  • Best Quartile: Q3
  • Area: MATERIALS SCIENCE, MULTIDISCIPLINARY Quartile: Q3 Rank in area: 188/344 (Ranking edition: SCIE)
  • Area: ENGINEERING, MANUFACTURING Quartile: Q3 Rank in area: 30/50 (Ranking edition: SCIE)

SCImago Journal Rank

  • Year 2022
  • SJR Journal Impact: 0.724
  • Best Quartile: Q1
  • Area: Industrial and Manufacturing Engineering Quartile: Q1 Rank in area: 84/364
  • Area: Materials Science (miscellaneous) Quartile: Q2 Rank in area: 170/597


  • Social Sciences: A

Scopus CiteScore

  • Year 2022
  • CiteScore of the Journal : 7.7
  • Area: Industrial and Manufacturing Engineering Percentile: 85
  • Area: Materials Science (miscellaneous) Percentile: 79

Journal Citation Indicator (JCI)

  • Year 2022
  • Journal Citation Indicator (JCI): 0.55
  • Best Quartile: Q3
  • Area: MATERIALS SCIENCE, MULTIDISCIPLINARY Quartile: Q3 Rank in area: 217/424
  • Area: ENGINEERING, MANUFACTURING Quartile: Q3 Rank in area: 37/66


(Data updated as of 26-03-2023)
  • Total citations: 1
  • Recent citations: 1


Along with the characteristic staircase effect, short carbon fibers, added to reinforce Fused Filament Fabrication parts, can significantly worsen the resulting surface finishing. Concerning this topic, the present work intends to improve the existing knowledge by analyzing 2400 measurements of arithmetic mean roughness Ra corresponding to different combinations of six process parameters: the content by weight of short carbon fibers in polyethylene terephthalate glycol (PETG) filaments f, layer height h, surface build angle θ, number of walls w, printing speed s, and extruder diameter d. The collected measurements were represented by dispersion and main effect plots. These representations indicate that the most critical parameters are θ, f, and h. Besides, up to a carbon fiber content of 12%, roughness is mainly affected by the staircase effect. Hence, it would be likely to obtain reinforced parts with similar roughness to unreinforced ones. Different machine learning methods were also tested to extract more information. The prediction model of Ra using the Random Forest algorithm showed a correlation coefficient equal to 0.94 and a mean absolute error equal to 2.026 μm. In contrast, the J48 algorithm identified a combination of parameters (h = 0.1 mm, d = 0.6 mm, and s = 30 mm/s) that, independent of the build angle, provides a Ra < 25 μm when using a 20% carbon fiber PETG filament. An example part was printed and measured to check the models. As a result, the J48 algorithm correctly classified surfaces with low roughness (Ra < 25 μm), and the Random Forest algorithm predicted the Ra value with an average relative error of less than 8%.

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