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Machine Learning for Optimizing Technological Properties of Wood Composite Filament-Timberfill Fabricated by Fused Deposition Modeling

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Applied Technologies (ICAT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1194))

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Abstract

This work evaluates the applicability of machine learning (ML) tools in additive manufacturing (AM) processes. One of the most employed AM techniques is fused deposition modeling (FDM), where a part is created from a computer-aided design (CAD) model using layer-by-layer deposition of a feedstock plastic filament material extruded through a nozzle. Owing to the large number of parameters involved in the manufacturing process, it is necessary to identify printing parameters ranges to improve mechanical properties as yield and ultimate strength. In that sense, ML has proven to be a reliable tool in engineering and materials processing, where hybrid ML algorithms are the best alternative since one-part acts as a forecaster, and the other part acts as an optimizer. To evaluate the performance of wood composite filament fabricated by FDM a uniaxial tensile test was performed at room temperature. The experimental procedure was carried out with a design of experiments of four factors at three levels, where the statistical significance of layer thickness, fill density, printing speed and raster angle was obtained as well as their interactions. Furthermore, ML’s algorithm accuracy was explored, where a neuro-fuzzy system (ANFIS) was trained and tested with the experimental data. Through the development of the present work, it is concluded that layer thickness and raster angle play a significant role in FDM of a wood composite filament where fibers presence increases the layer thickness accelerating the FDM process.

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Acknowledgements

This study has been completed under the financial support of the State Secretariat for higher education, science, technology and innovation (SENESCYT) grant number ARSEQ-BEC-000329-2017 and the Research Center for Nanotechnology and Advanced Materials (CIEN-UC).

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Authors and Affiliations

  1. Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Av. Vicuña Mackenna, 4860, Macul, Santiago, Chile

    Germán O. Barrrionuevo & Jorge A. Ramos-Grez

  2. Research Center for Nanotechnology and Advanced Materials (CIEN-UC), Av. Vicuña Mackenna, 4860, Macul, Santiago, Chile

    Jorge A. Ramos-Grez

Authors
  1. Germán O. Barrrionuevo
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  2. Jorge A. Ramos-Grez
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Correspondence to Germán O. Barrrionuevo .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica del Norte, Ibarra, Ecuador

    Marcelo Zambrano Vizuete

  3. Universidad Técnica Particular de Loja, Loja, Ecuador

    Pablo Torres-Carrión

  4. Universidad de las Fuerzas Armadas (ESPE), Quito, Ecuador

    Sergio Montes León

  5. Universidad Politécnica Salesiana, Guayaquil, Ecuador

    Guillermo Pizarro Vásquez

  6. International University of Sarajevo, Sarajevo, Bosnia and Herzegovina

    Benjamin Durakovic

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Barrrionuevo, G.O., Ramos-Grez, J.A. (2020). Machine Learning for Optimizing Technological Properties of Wood Composite Filament-Timberfill Fabricated by Fused Deposition Modeling. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_10

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  • DOI: https://doi.org/10.1007/978-3-030-42520-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-42519-7

  • Online ISBN: 978-3-030-42520-3

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