Abstract
Machine learning is gaining more popularity in the FDM process in the way of performance enhancement. The multi-functionality of multi-material printing and its rising employment makes the Machine-Learning (ML) tool more attractive as the diversity of process parameters involves many fabrication combinations. This paper describes the implementation of ML techniques in the production of multi-material objects to achieve a high mechanical outcome. A nozzle temperature of PLA and ABS extruders was chosen as an input feature for ML, whereas UTS was the target. 125 samples with additional 6 pieces for deviation cases printed for 25 temperature combinations. The decision Tree model exhibited improper prediction values. Although the next Random Forest model had a fairly good R2-0.78, the 3D graph of UTS had a coarse curve. The highest R2-0.81 belonged to the 5th degree Polynomial Regression model. According to this model, to acquire the highest UTS value-41.171MPa, extruding temperatures should be 216 C, and 246 C for PLA and ABS respectively.
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Acknowledgments
This work was supported by the Faculty Development Competitive Research Grants, Ref. No. 021220FD1551, Nazarbayev University with providing experimental facilities. The authors highly express their gratitude to Nazarbayev University and Kennesaw State University for providing laboratory facilities and financial support.
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Ali, M., Sabyrov, N., Tanveer, M.H., Kurokawa, S., Shehab, E. (2023). Enhancing Mechanical Property of Multi-material Printed Object Through Machine-Learning. In: Kim, KY., Monplaisir, L., Rickli, J. (eds) Flexible Automation and Intelligent Manufacturing: The Human-Data-Technology Nexus. FAIM 2022. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-17629-6_3
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DOI: https://doi.org/10.1007/978-3-031-17629-6_3
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