Abstract
With increasing attention on fused filament fabrication (FFF) applications for high-performance polymers, understanding the energy characteristics of FFF is critical to pursue a sustainable additive manufacturing system. In this regard, this study proposes a two-phased energy prediction approach to FFF. A full factorial design of experiments for FFF is planned to collect data for build time, filament volume, and energy consumption by varying major process parameters (i.e., layer thickness, infill density, and printing speed). Carbon fiber reinforced polyether-ether-ketone (CFR-PEEK), which is an emerging high-performance polymer for advanced engineering applications, is used for a material of the experiments. In the first phase, two machine learning methods (i.e., decision tree and random forest) are employed to estimate the material addition rate (MAR) of FFF, which indicates the amount of deposited material volume per unit time. Then, a statistical regression model of energy consumption on the estimated MAR is built to predict the energy consumption of FFF for CFR-PEEK. The results show that the decision tree model has better prediction performance to predict the MAR of FFF than the random forest model. In addition, the energy consumption model through the predicted MAR shows that an increase in the MAR reduces energy consumption. The two-phased prediction approach based on the MAR provides better performance than the direct energy prediction from process parameters through a machine learning technique. The findings of this study provide a basis to characterize energy dynamics in FFF for CFR-PEEK.
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References
Lunetto, V., Priarone, P.C., Galati, M., Minetola, P.: On the correlation between process parameters and specific energy consumption in fused deposition modelling. J. Manuf. Process. 56, 1039–1049 (2020). https://doi.org/10.1016/j.jmapro.2020.06.002
Park, K., Kim, G., No, H., Jeon, H.W., Okudan Kremer, G.E.: Identification of optimal process parameter settings based on manufacturing performance for fused filament fabrication of CFR-PEEK. Appl. Sci. 10(13), 4630 (2020). https://doi.org/10.3390/app10134630
Kim, K., Noh, H., Park, K., Jeon, H.W., Lim, S.: Characterization of power demand and energy consumption for fused filament fabrication using CFR-PEEK. Rapid Prototyp. J. 28(7), 1394–1406 (2022). https://doi.org/10.1108/rpj-07-2021-0188
Mognol, P., Lepicart, D., Perry, N.: Rapid prototyping: energy and environment in the spotlight. Rapid Prototyp. J. 12(1), 26–34 (2006). https://doi.org/10.1108/13552540610637246
Al-Ghamdi, K.A.: Sustainable FDM additive manufacturing of ABS components with emphasis on energy minimized and time efficient lightweight construction. Int. J. Lightweight Mater. Manuf. 2(4), 338–345 (2019). https://doi.org/10.1016/j.ijlmm.2019.05.004
Elkaseer, A., Schneider, S., Scholz, S.G.: Experiment-based process modeling and optimization for high-quality and resource-efficient FFF 3D printing. Appl. Sci. 10(8) (2020). https://doi.org/10.3390/app10082899. Article 2899
Galetto, M., Verna, E., Genta, G.: Effect of process parameters on parts quality and process efficiency of fused deposition modeling. Comput. Ind. Eng. 156, 107238 (2021). https://doi.org/10.1016/j.cie.2021.107238
Peng, T., Yan, F.: Dual-objective analysis for desktop FDM printers: energy consumption and surface roughness. Procedia CIRP 69, 106–111 (2018). https://doi.org/10.1016/j.procir.2017.11.084
Liu, Z., et al.: Investigation of energy requirements and environmental performance for additive manufacturing processes. Sustainability 10(10), 3606 (2018). https://doi.org/10.3390/su10103606
Thingiverse. https://www.thingiverse.com
Simplify 3D. www.simplify3d.com/software/release-notes/version-4-1-2
Apium. https://apiumtec.com/download/apium-cfr-peek-datasheet
Adpower. https://bit.ly/3LOZm3S
Wei, Y., et al.: A review of data-driven approaches for prediction and classification of building energy consumption. Renew. Sustain. Energy Rev. 82, 1027–1047 (2018). https://doi.org/10.1016/j.rser.2017.09.108
Zhang, W., Wu, C., Li, Y., Wang, L., Samui, P.: Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk Assessment Manage. Risk Eng. Syst. Geohaz. 15(1), 27–40 (2021). https://doi.org/10.1080/17499518.2019.1674340
Scikit-learn. https://scikit-learn.org/stable/supervised_learning.html#supervised-learning
Gutowski, T.G., Branham, M.S., Dahmus, J.B., Jones, A.J., Thiriez, A., Sekulic, D.P.: Thermodynamic analysis of resources used in manufacturing processes. Environ. Sci. Technol. 43(5), 1584–1590 (2009). https://doi.org/10.1021/es8016655
Acknowledgement
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1C1C1012140) for Kijung Park.
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Noh, H., Park, K., Kremer, G.E. (2022). A Two-Phased Approach to Energy Consumption Prediction for Fused Filament Fabrication of CFR-PEEK. In: Kim, D.Y., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Smart Manufacturing and Logistics Systems: Turning Ideas into Action. APMS 2022. IFIP Advances in Information and Communication Technology, vol 663. Springer, Cham. https://doi.org/10.1007/978-3-031-16407-1_33
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