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Application of Machine Learning in Fused Deposition Modeling: A Review

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Digital Technologies and Applications (ICDTA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 668))

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Abstract

The market of modern industry is oriented towards the technology of additive manufacturing, which encompasses several processes, among which we find FDM (fused deposition modeling), which has become increasingly used due to its advantageous contribution compared to traditional techniques. Its advantages concern in particular the reduction of manufacturing time, the resistance to temperature and the limitation of human intervention, which also decreases the risks that the user of this technology may have. However, despite these advantages, FDM has some limitations, especially with regard to defects that influence the manufacturing and, of course, the quality of the final product. As a result, and because of the recognised effectiveness of machine learning algorithms in the field of FDM, we have prepared this summary paper to review recent research involving the application of these algorithms to the FDM manufacturing process. The purpose of this work is to show the applicability of ML in various tasks related to this type of process. These tasks include surface roughness prediction, part deviation detection, cost estimation and defect prediction. The results of this paper show that ML algorithms contribute effectively to achieving good prediction and classification accuracies for several tasks associated with FDM manufacturing. This work opens the door for further research to apply these ML algorithms in other tasks related to this type of process.

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Authors and Affiliations

  1. LISAC Laboratory, Faculty of Sciences Dhar EL Mehraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Mohmed Achraf El Youbi El Idrissi, Hamid Tairi & Moncef Zaki

  2. LISA Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Loubna Laaouina

  3. LISAC Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Adil Jeghal

Authors
  1. Mohmed Achraf El Youbi El Idrissi
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  2. Loubna Laaouina
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  3. Adil Jeghal
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  4. Hamid Tairi
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  5. Moncef Zaki
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Corresponding author

Correspondence to Mohmed Achraf El Youbi El Idrissi .

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Editors and Affiliations

  1. Ecole Nationale des Sciences Appliquées, Fez, Morocco

    Saad Motahhir

  2. Faculty of Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Badre Bossoufi

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El Youbi El Idrissi, M.A., Laaouina, L., Jeghal, A., Tairi, H., Zaki, M. (2023). Application of Machine Learning in Fused Deposition Modeling: A Review. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 668. Springer, Cham. https://doi.org/10.1007/978-3-031-29857-8_12

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