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

Materials - Tập 12 Số 16 - Trang 2574
Barrios1, Pablo E. Romero1
1Department of Mechanical Engineering, University of Cordoba, Medina Azahara Avenue, 5–14071 Cordoba, Spain

Tóm tắt

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.

Từ khóa


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