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

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2019
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Abstract
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.
Reference Key
barrios2019decisionmaterials Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Barrios, Juan M;Romero, Pablo E;
Journal Materials (Basel, Switzerland)
Year 2019
DOI E2574
URL
Keywords
Data mining random forest surface roughness decision tree c4.5 petg fused deposition modeling (fdm) random tree

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