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Vat photopolymerization (VPP) is an effective additive manufacturing (AM) process known for its high dimensional accuracy and excellent surface finish. The combination of visible light with the use of LCD screens for 3D printing, allows for a faster, more efficient and economical manufacturing process. Despite these benefits, fabricating the end-use products still has some limitations related to the strength of the fabricated parts. For this purpose, the present paper provides a methodology to predict and optimize three critical process variables in AM, namely: layer height, build orientation, post-curing time. A neural-network model was developed for predicting the impact strength and hardness and optimizing the printing variables for highest responses. From the experiments using full-factorial design, it was revealed that improved parts strength and hardness are obtained at lower layer height, flat orientation, and moderate post-curing time. Based on the ANOVA analysis of, the most effective variable on the impact strength was post-curing time with (41.8%), while the orientation was higher contribution than the rest on the parts hardness with (47.5%). Comparisons between the experimental and the predicted values were illustrated. The mean error between experimental and neural network model was (1.13%) for impact strength and (0.82%) for hardness strength with correlation coefficient equal to 0.988 and 0.982 for the two responses respectively. The current proposed study demonstrates good agreement between the predicted model values and the experiments outcomes of impact strength and parts hardness.
Wydawca
Rocznik
Tom
Strony
373--383
Opis fizyczny
Bibliogr. 24 poz., fig., tab.
Twórcy
- Production Engineering and Metallurgy Department, University of Technology-Iraq, Alsina’a street, 10066, Baghdad, Iraq
autor
- Production Engineering and Metallurgy Department, University of Technology-Iraq, Alsina’a street, 10066, Baghdad, Iraq
autor
- Production Engineering and Metallurgy Department, University of Technology-Iraq, Alsina’a street, 10066, Baghdad, Iraq
Bibliografia
- 1. Paral S.K., Lin D.-Z., Cheng Y.-L., Lin S.-C., and Jeng J.-Y. A review of critical issues in high-speed vat photopolymerization, Polymers (Basel)., 2023; 15(12): 2716, https://doi.org/10.3390/polym15122716.
- 2. Abdulridha H.H. and Abbas T.F. Analysis and investigation the effect of the printing parameters on the mechanical and physical properties of PLA parts fabricated via FDM printing, Adv. Sci. Technol. Res. J., 2023; 17: 6. https://doi.org/10.12913/22998624/173562.
- 3. Quan H., Zhang T., Xu H., Luo S., Nie J., and Zhu X. Photo-curing 3D printing technique and its challenges, Bioact. Mater., 2020; 5(1): 110–115. https://doi.org/10.1016/j.bioactmat.2019.12.003.
- 4. Zhu Y. et al. Recent advancements and applications in 3D printing of functional optics, Addit. Manuf., 2022; 52: 102682. https://doi.org/10.1016/j.addma.2022.102682.
- 5. Caplins B.W. et al. Characterizing light engine uniformity and its influence on liquid crystal display based vat photopolymerization printing, Addit. Manuf., 2023; 62: 103381. https://doi.org/10.1016/j.addma.2022.103381.
- 6. Hu G. et al. Photopolymerisable liquid crystals for additive manufacturing, Addit. Manuf., 2022; 55: 102861. https://doi.org/10.1016/j.addma.2022.102861.
- 7. Yang T. and Gu F. Overview of modulation techniques for spatially structured-light 3D imaging, Opt. Laser Technol., 2024; 169: 110037. https://doi.org/10.1016/j.optlastec.2023.110037.
- 8. Tosto C. et al. Epoxy based blends for additive manufacturing by liquid crystal display (LCD) printing: The effect of blending and dual curing on daylight curable resins, Polymers (Basel)., 2020; 12(7), 1594. https://doi.org/10.3390/polym12071594.
- 9. Salih R.M., Kadauw A., Zeidler H., and Aliyev R. Investigation of LCD 3D Printing of Carbon Fiber Composites by Utilising Central Composite Design, J. Manuf. Mater. Process., 2023; 7(2): 58. https://doi.org/10.3390/jmmp7020058.
- 10. Dey A. and Yodo N. A systematic survey of FDM process parameter optimization and their influence on part characteristics, J. Manuf. Mater. Process., 2019; 3(3). https://doi.org/10.3390/jmmp3030064.
- 11. Abdulrazaq M.M., AL-Khafaji M.M.H., and Kadauw A. The influence of slicing parameters on mechanical strength of FDM printed parts: A review, in AIP Conference Proceedings, AIP Publishing, 2025. https://doi.org/10.1063/5.0254289.
- 12. Riyaz Ahmed A. and Mugendiran V. Effect of process parameters on mechanical properties of PLA resin through LCD 3D printing, Proc. Inst. Mech. Eng. Part E J. Process Mech. Eng., 2024; 09544089231225147. https://doi.org/10.1177/09544089231225.
- 13. Al‐Dulaijan Y.A. et al. Effect of printing orientation and postcuring time on the flexural strength of 3D‐printed resins, J. Prosthodont., 2023; 32(S1): 45–52. https://doi.org/10.1111/jopr.13572.
- 14. Seprianto D., Sugiantoro R., and Erwin M. The effect of rectangular parallel key manufacturing process parameters made with stereolithography DLP 3D printer technology against impact strength, in Journal of Physics: Conference Series, IOP Publishing, 2020, 12028. https://doi.org/10.1088/1742-6596/1500/1/012028.
- 15. Yang Y., Zhou Y., Lin X., Yang Q., and Yang G. Printability of external and internal structures based on digital light processing 3D printing technique, Pharmaceutics, 2020; 12(3), 207. https://doi.org/10.3390/pharmaceutics12030207.
- 16. Schittecatte L., Geertsen V., Bonamy D., Nguyen T., and Guenoun P. From resin formulation and process parameters to the final mechanical properties of 3D printed acrylate materials, MRS Commun., 2023; 13(3), 357–377. https://doi.org/10.1016/j.heliyon.2018.e00938.
- 17. Al-Wswasi M., Al-Khaleeli W.A. and Aufy S.A. Implement the artificial neural network concept for predicting the mechanical properties of printed polylactic acid parts, Adv. Sci. Technol. Res. J., 2025; 19(5): 73–83. https://doi.org/10.12913/22998624/201463.
- 18. Al-Bdairy A.M.J., Ghazi S.K. and Abed A.H. Prediction of the mechanical properties for 3D printed rapid prototypes based on artificial neural network, Adv. Sci. Technol. Res. J., 2025; 19(3), 96–107. https://doi.org/10.12913/22998624/197405.
- 19. Abiodun O.I., Jantan A., Omolara A.E., Dada K.V., Mohamed N.A. and Arshad H. State-of-the-art in artificial neural network applications: A survey, Heliyon, 2018; 4(11), e00938. https://doi.org/10.1016/j.heliyon.2018.e00938.
- 20. Mahmood M.A., Visan A.I., Ristoscu C. and Mihailescu I.N. Artificial neural network algorithms for 3D printing, Materials (Basel)., 2020; 14(1), 163. https://doi.org/10.3390/ma14010163.
- 21. Al-Shathr A., Shakor Z.M., Majdi H.S., Abdul Razak A.A. and Albayati T.M. Comparison between artificial neural network and rigorous mathematical model in simulation of industrial heavy naphtha reforming process, Catalysts, 2021; 11(9), 1034. https://doi.org/10.3390/catal11091034.
- 22. Shirmohammadi M., Goushchi S.J., and Keshtiban P.M. Optimization of 3D printing process parameters to minimize surface roughness with hybrid artificial neural network model and particle swarm algorithm, Prog. Addit. Manuf., 2021; 6: 199–215. https://doi.org/10.1007/s40964-021-00166-6.
- 23. Abdulrazaq M.M., Al-Khafaji M.M.H., Kadauw A., Krinke S., and Zeidler H. Out-of-position bead geometry prediction in wire arc additive manufacturing (WAAM) using fuzzy logic-based system, Manag. Syst. Prod. Eng., 2025. https://doi.org/10.2478/mspe-2025-0005.
- 24. Abdullah M.A., Abed A.H., and Mansor K.K. Comparison between low-carbon steel and galvanized steel by deep drawing under the influence of different parameters, Manag. Syst. Prod. Eng., 2025. https://doi.org/10.2478/MSPE-2025-0016.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-6d1e1e56-2afa-4599-b676-37e2b0c2e4cc
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