Use of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modeling

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...

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Bibliographic Details
Published in:International journal of advanced manufacturing technology 2021-08, Vol.115 (7-8), p.2465-2475
Main Authors: Cerro, Azahara, Romero, Pablo E., Yiğit, Okan, Bustillo, Andres
Format: Article
Language:English
Subjects:
Algorithms
CAE) and Design
Computer-Aided Engineering (CAD
Deposition
Design of experiments
Engineering
Fused deposition modeling
Industrial and Production Engineering
Machine learning
Mechanical Engineering
Media Management
Multilayer perceptrons
Original Article
Polyvinyl acetal resins
Polyvinyl butyral
Rapid prototyping
Surface finish
Surface roughness
Three dimensional printing
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Summary: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.
ISSN:0268-3768
1433-3015
DOI:10.1007/s00170-021-07300-2