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Improvement of Surface Roughness and Hydrophobicity in PETG Parts Manufactured via Fused Deposition Modeling (FDM): An Application in 3D Printed Self–Cleaning Parts
Open AccessArticle

Decision Tree Methods for Predicting Surface Roughness in Fused Deposition Modeling Parts

Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 5–14071 Cordoba, Spain
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Materials 2019, 12(16), 2574; https://doi.org/10.3390/ma12162574
Received: 20 July 2019 / Revised: 8 August 2019 / Accepted: 12 August 2019 / Published: 12 August 2019
3D printing using fused deposition modeling (FDM) includes a multitude of control parameters. It is difficult to predict a priori what surface finish will be achieved when certain values are set for these parameters. The objective of this work is to compare the models generated by decision tree algorithms (C4.5, random forest, and random tree) and to analyze which makes the best prediction of the surface roughness in polyethylene terephthalate glycol (PETG) parts printed in 3D using the FDM technique. The models have been created using a dataset of 27 instances with the following attributes: layer height, extrusion temperature, print speed, print acceleration, and flow rate. In addition, a dataset has been created to evaluate the models, consisting of 15 additional instances. The models generated by the random tree algorithm achieve the best results for predicting the surface roughness in FDM parts. View Full-Text
Keywords: fused deposition modeling (FDM); PETG; surface roughness; data mining; decision tree; C4.5; random forest; random tree fused deposition modeling (FDM); PETG; surface roughness; data mining; decision tree; C4.5; random forest; random tree
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Barrios, J.M.; Romero, P.E. Decision Tree Methods for Predicting Surface Roughness in Fused Deposition Modeling Parts. Materials 2019, 12, 2574.

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