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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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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