Analysis of the Effect of the Surface Inclination Angle on the Roughness of Polymeric Parts Obtained with Fused Filament Fabrication Technology

Abstract

The aim of this work was to conduct a dimensional study, in terms of microgeometry, using parts from an additive manufacturing process with fused filament fabrication (FFF) technology. As in most cases of additive manufacturing processes, curved surfaces were obtained via approximation of planes with different inclinations. The focus of this experimental study was to analyze the surface roughness of curve geometry from surface-roughness measurements of the plane surfaces that generate it. Three relevant manufacturing parameters were considered: layer height, nozzle diameter and material. Taguchi’s experimental design based on the Latin square was applied to optimize the set of specimens used. For the manufactured samples, the surface-roughness parameters Ra (roughness average), Rq (root mean square roughness) and Rz (maximum height) were obtained in eight planes of different inclinations (0° to 90°). The results were analyzed using both a graphical model and an analysis of variance study (ANOVA), demonstrating the dependency relationships among the parameters considered and surface finish. The best surface roughness was reached at 85°, with a global average Ra value of 8.66 µm, increasing the average Ra value from 6.39 µm to 11.57 µm according to the layer height increase or decreasing it slightly, from 8.91 µm to 8.41 µm, in relation to the nozzle diameter increase. On the contrary, the worst surface roughness occurred at 20°, with a global average Ra value of 19.05 µm. Additionally, the theoretical profiles and those from the surface-roughness measurement were found to coincide greatly. Eventually, the eight regression curves from the ANOVA allowed prediction of outputs from future specimens tested under different conditions.

Bridging Effect of Carbon Nitride with More Negative Conduction Potential and Halogens Promotes the Liquid-Phase Oxidation of Aromatic C–H Bonds

“…However, the transverse dimensions of CN and Br-CN were 150 and 500 nm, respectively, indicating that the doping of Ni and Br significantly extended the transverse aromatic conjugation system of CN, 55 which also explained the strong optical absorption of Br-CN. Moreover, the roughness (R a and R q ) 56 of the Br-CN samples was significantly increased compared to pure CN, a phenomenon that explained the high specific surface area of Br-CN.…”

Application of Artificial Intelligence for Surface Roughness Prediction of Additively Manufactured Components

“…Different methods were used to predict the surface roughness of additively manufactured components, i.e., Taguchi-based regression models [ 87 , 88 ], statistical regression models [ 89 ], computational modeling (e.g., the FEM and Discrete Element Method (DEM)), [ 90 ] and machine learning methods [ 91 ]. There are several limitations of conventional (statistical) methods for predicting the surface roughness of additively manufactured components.…”