Use of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modeling
Küçük Resim Yok
Tarih
2021
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer London Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Machine learning algorithms for classification are employed in this study to generate different models that can predict the surface roughness of parts manufactured from polyvinyl butyral by means of Fused Deposition Modeling (FDM). Five input variables are defined (layer height, print speed, number of perimeters, wall angle, and extruder temperature), and 16 parts are 3D printed, each with three different surfaces (48 surfaces in total). The print values used to print each part were defined by a fractionated orthogonal experimental design. Using a perthometer, the average value of surface roughness, Ra, on each surface was obtained. From these experimental values, 40 models were trained and validated. The model with the best prediction results was the one generated by bagging and Multilayer Perceptron (BMLP), with a Kappa statistic of 0.9143. The input variables with the highest influence on the surface finish are the wall angle and the layer height.
Açıklama
Anahtar Kelimeler
Machine learning, 3d printing, Fused deposition modeling, Fused filament fabrication, Surface roughness, WEKA
Kaynak
International Journal of Advanced Manufacturing Technology
WoS Q Değeri
Q2
Scopus Q Değeri
Q1
Cilt
115
Sayı
7-8