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

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Abstract

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.

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Availability of data and material

Experimental data is included in Table 6.

Code availability

All software code is included in Weka release 3.8.

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Funding

This work was partially supported by the SMART-EASY project (Reference Number IDI-20191008) funded by the Spanish Centro para el Desarrollo Tecnológico e Industrial (CDTI) and by the Plan Propio de Investigación of the University of Cordoba.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Cordoba, Medina Azahara, 5, 14071, Cordoba, Spain

    Azahara Cerro & Pablo E. Romero

  2. Department of Mechanical Engineering, University of Karabuk, Kastamonu Yolu Demir Celik Kampusu, 78050, Karabuk, Turkey

    Okan Yiğit

  3. Departmento de Ingenieria Informatica, University of Burgos, Avda Cantabria s/n, 09006, Burgos, Spain

    Andres Bustillo

Authors
  1. Azahara Cerro
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  2. Pablo E. Romero
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  4. Andres Bustillo
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Contributions

Conceptualization: [Azahara Cerro]; methodology: [Azahara Cerro and Okan Yiğit]; formal analysis: [Pablo E. Romero]; funding acquisition: [Pablo E. Romero and Andres Bustillo]; investigation: [Azahara Cerro]; resources: [Azahara Cerro and Okan Yiğit]; validation: [Azahara Cerro and Okan Yiğit]; supervision: [Pablo E. Romero and Andres Bustillo]; writing and original draft: [Azahara Cerro and Okan Yiğit]; writing, review and editing: [Pablo E. Romero and Andres Bustillo]. All authors have read and agreed to the submitted version of the manuscript.

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Correspondence to Pablo E. Romero.

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Cerro, A., Romero, P.E., Yiğit, O. et al. Use of machine learning algorithms for surface roughness prediction of printed parts in polyvinyl butyral via fused deposition modeling. Int J Adv Manuf Technol 115, 2465–2475 (2021). https://doi.org/10.1007/s00170-021-07300-2

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