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Surface Roughness Reduction in A Fused Filament Fabrication (FFF) Process using Central Composite Design Method

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The objective of this study is to optimize the fabrication factors of a consumer-grade fused filament fabrication (FFF) system. The input factors were nozzle temperature, bed temperature, printing speed, and layer thickness. The optimization aims to minimize average surface roughness (Ra) indicating the surface quality of benchmarks. In this study, Ra was measured at two positions, the bottom and top surface of benchmarks. For the fabrication, the material used was the Polylactic acid (PLA) filament. A response surface method (RSM), central composite design (CCD), was utilized to carry out the optimization. The analysis of variance (ANOVA) was calculated to explore the significant factors, interactions, quadratic effect, and lack of fit, while the regression analysis was performed to determine the prediction equation of Ra. The model adequacy checking was conducted to check whether the residual assumption still held. The total number of thirty benchmarks was fabricated and measured using a surface roughness tester. For the bottom surface, the analysis results indicated that there was the main effect from only one factor, printing speed. However, for the top surface, the ANOVA signified an interaction between the printing speed and layer thickness. The optimal setting of these factors was also recommended, while the empirical models of Ra at both surface positions were also presented. Finally, an extra benchmark was fabricated to validate the empirical model.
Rocznik
Strony
157--163
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
  • Valaya Alongkorn Rajabhat University, 1 Moo 20, Paholyothin Rd., Pathum Thani, Thailand
Bibliografia
  • 1. Ahn, D., Kweon, J.H., Kwon, S., Song, J., Lee, S., 2009. Representation of surface roughness in fused deposition modelling. Journal of Materials Processing Technology, 209(15–16), 5593-5600, DOI: 10.1016/j.jmatprotec.2009.05.01610.1016/j.jmatprotec.2009.05.016
  • 2. Armillotta, A., 2006. Assessment of surface quality on textured FDM prototypes. Rapid Prototyping Journal, 12(1), 35-41, DOI: 10.1108/1355254061063725510.1108/13552540610637255
  • 3. Box, G.E.P., Wilson, K. B., 1951. On the experimental attainment of optimum conditions. Journal of the Royal Statistical Society. Series B, 13(1), 1-45.10.1111/j.2517-6161.1951.tb00067.x
  • 4. Chohan, J.S., Singh, R., 2017. Pre and post processing techniques to improve surface characteristics of FDM parts: a state of art review and future applications. Rapid Prototyping Journal, 23(3), 495-513, DOI: 10.1108/RPJ-05-2015-005910.1108/RPJ-05-2015-0059
  • 5. Dewey, M.P., Ulutan D., 2017. Development of laser polishing as an auxiliary post-process to improve surface quality in fused deposition modeling parts. Additive Manufacturing, 2, DOI: 10.1115/MSEC2017-302410.1115/MSEC2017-3024
  • 6. Gurrala, P.L., Regalla, S.P., 2014. Multi-objective optimisation of strength and volumetric shrinkage of FDM parts. Virtual and Physical Prototyping, 9(2), 127-138, DOI: 10.1080/17452759.2014.89885110.1080/17452759.2014.898851
  • 7. Kiefer, J., Wolfowitz, J., 1959. Optimum designs in regression problems. Annals of Mathematical Statistics, 30, 271–294.10.1214/aoms/1177706252
  • 8. Kim, M.K., Lee, I.H., Kim, H.C., 2018. Effect of fabrication parameters on surface roughness of FDM parts. International Journal of Precision Engineering and Manufacturing, 19(1), 137–142, DOI: 10.1007/s12541-018-0016-010.1007/s12541-018-0016-0
  • 9. Krolczyk, G., Raos, P., Legutko, S., 2014. Experimental Analysis of Surface Roughness and Surface Texture of Machined and Fused Deposition Modelled Parts. Tehnički vjesnik, 21(1).10.2478/mms-2014-0060
  • 10. Li, Y., Linke, B. S., Voet, H., Falk, B., Schmitt, R., Lam, M., 2017. Cost, sustainability and surface roughness quality – A comprehensive analysis of products made with personal 3D printers. CIRP Journal of Manufacturing Science and Technology, 16, 1-11, DOI: 10.1016/j.cirpj.2016.10.00110.1016/j.cirpj.2016.10.001
  • 11. Medellin-Castillo, H.I., Zaragoza-Siqueiros, J., 2019. Design and manufacturing strategies for fused deposition modelling in additive manufacturing: a review. Chinese Journal of Mechanical Engineering, 32(53), DOI: 10.1186/s10033-019-0368-010.1186/s10033-019-0368-0
  • 12. Mohamed, O.A., Masood, S.H., Bhowmik, J.L., 2016. Mathematical modeling and FDM process parameters optimisation using response surface methodology based on Q-optimal design. Applied Mathematical Modelling, 40(23-24), 10052-10073, DOI: 10.1016/j.apm.2016.06.05510.1016/j.apm.2016.06.055
  • 13. Pandey, P.M., Reddy, N.V., Dhande, S.G., 2003. Improvement of surface finish by staircase machining in fused deposition modelling. Journal of Materials Processing Technology, 132(1–3), 323-331, DOI: 10.1016/S0924-0136(02)00953-610.1016/S0924-0136(02)00953-6
  • 14. Pandey, P.M., Reddy, N.V., 2007. Virtual hybrid-FDM system to enhance surface finish. Virtual and Physical Prototyping, 1(2), 101-116, DOI: 10.1080/1745275060076390510.1080/17452750600763905
  • 15. Peng, A., Xiao, X., Yue, R., 2014. Process parameter optimisation for fused deposition modeling using response surface methodology combined with fuzzy inference system. International Journal of Advanced Manufacturing Technology, 73 (1-4), 87-100, DOI: 10.1007/s00170-014-5796-510.1007/s00170-014-5796-5
  • 16. Pérez, M., Medina-Sánchez, G., García-Collado, A., Gupta, M., Carou, D., 2018. Surface quality enhancement of fused deposition modeling (FDM) printed samples based on the selection of critical printing parameters. Materials, 11(8), 1382, DOI: 10.3390/ma1108138210.3390/ma11081382612005030096826
  • 17. Rahmati, S., Vahabli, E., 2015. Evaluation of analytical modeling for improvement of surface roughness of FDM test part using measurement results. International Journal of Advanced Manufacturing Technology, 79(5–8), 823–829, DOI: 10.1007/s00170-015-6879-710.1007/s00170-015-6879-7
  • 18. Shirmohammadi, M., Goushchi, S.J., Keshtiban, P.M., 2021. Optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm. Progress in Additive Manufacturing, 6, 199-215, DOI: 10.1007/s40964-021-00166-610.1007/s40964-021-00166-6
  • 19. Singh, R., Singh, S., Singh, I. P., Fabbrocino, F., Fraternali, F., 2017. Investigation for surface finish improvement of FDM parts by vapor smoothing process. Composites Part B, 111, 228-234, DOI: 10.1016/j.compositesb.2016.11.06210.1016/j.compositesb.2016.11.062
  • 20. Taufik, M., Jain, P., 2016. A study of build edge profile for prediction of surface roughness in fused deposition modelling. Journal of Manufacturing Science and Engineering, 138(6), DOI: 10.1115/1.403219310.1115/1.4032193
  • 21. Tiwari, K., Kumar, S., 2018. Analysis of the factors affecting the dimensional accuracy of 3D printed products. Materials Today, 5(9), 18674-18680, DOI: 10.1016/j.matpr.2018.06.21310.1016/j.matpr.2018.06.213
  • 22. Turner, B., Gold, S., 2015. A review of melt extrusion additive manufacturing processes: II. Materials, dimensional accuracy, and surface roughness. Rapid Prototyping Journal, 21(3), 250-261, DOI: 10.1108/RPJ-02-2013-001710.1108/RPJ-02-2013-0017
  • 23. Vahabli, E., Rahmati, S., 2016. Application of an RBF neural network for FDM parts’ surface roughness prediction for enhancing surface quality. International Journal of Precision Engineering and Manufacturing, 17, 1589–1603, DOI: 10.1007/s12541-016-0185-710.1007/s12541-016-0185-7
  • 24. Wu, D., Wei, Y., Terpenny, J., 2018. Predictive modeling of surface roughness in fused deposition modeling using data fusion. International Journal of Production Research, 57(3), 3992-4006, DOI: 10.1080/00207543.2018.150505810.1080/00207543.2018.1505058
  • 25. Yodo, N., Dey, A., 2021. Multi-objective optimization for FDM process parameters with evolutionary algorithms. Fused Deposition Modeling Based 3D Printing (Editors: Dave, H. K., Davim, J. P.), Springer International Publishing, Basel, Switzerland.10.1007/978-3-030-68024-4_22
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2e9f0509-a19d-4a9e-aa0b-8aa4dab0fcae
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