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%.
Keywords: FFF; decision tree; machine learning; mean roughness; random forest; short carbon fiber.
Copyright 2023, Mary Ann Liebert, Inc., publishers.