Prediction of the mechanical properties for 3D printed rapid prototypes based on artificial neural network

• Autorzy: Al-Bdairy Ali Mohammed Jassem , Ghazi Safaa Kadhim , Abed Aseel Hamad

Identyfikatory

DOI

10.12913/22998624/197405

• Języki publikacji: EN

Abstrakty

EN

With development of Additive Manufacturing (AM) especially the 3D printing technology, make it the widespread application for 3D prototypes in industrials, engineering jawless, biomedical and others filed. In the present work focused on the 3D printing problems that associated to selecting proper printing parameters. Based on the experimental and ANN the effect of printing speed, printing temperature, layer height, and number of top shells on the produced mechanical properties of the 3D prototypes. Ultimate tensile strength, yield strength, and modulus of elasticity have been studied as the main mechanical properties. Design of experiment for specimens using a MINITAB software has been achieved based on Taguchi method based on the sixteen specimens with four levels values of printing parameters. CAD CAM software (Solid work) used to create 3D model of the testing specimens with the specific dimensions based on the ASTM E8M. ANICUBIC 3D printing machine used to fabricate the specimens under the studied 3D printing parameters. ANN has been used to validate the obtained experimental and DOE. The obtained results showed that increasing the printing temperature up to 220oC, and high number of top shells arriving to 4 shells will increase the ultimate tensile strength, yield strength, and modules of elasticity. While decreasing the printing speed lower than 100m/sec. and decreasing layer height lower than 0.3mm will produce a gaining in the mentioned mechanical properties. Comparison results of the experimental work and the predicted results obtained from suggested model of ANN provide the more compatibility between these values, the regression of the ANN observed that the learning of the network is proper and can be application to predict the Ultimate Tensile Stress, Yield Stress, and Modulus of Elasticity, where the validation, training, test and all of data are about (0.95592-1).

Słowa kluczowe

EN

3D printing; printing parameters; rapid prototyping; design of experiments; artificial neural network; ANN

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 3
• Strony: 96--107
• Opis fizyczny: Bibliogr. 21 poz., fig., tab.

Twórcy

autor

Al-Bdairy Ali Mohammed Jassem

• Production Engineering and Metallurgy Deptartament University of Technology Baghdad, Iraq

autor

Ghazi Safaa Kadhim

• Production Engineering and Metallurgy Deptartament University of Technology Baghdad, Iraq

• https://orcid.org/0000-0002-8257-2568

autor

Abed Aseel Hamad

• Production Engineering and Metallurgy Deptartament University of Technology Baghdad, Iraq

Bibliografia

• 1. Ian Gibson et al. Additive manufacturing technologies 3D printing, rapid prototyping, and direct digital manufacturing. Second Edition, Springer New York Heidelberg Dordrecht, London, 2010.
• 2. Mikell P. Groover. Fundamentals of modern manufacturing materials, processes. Fourth Edition, United States of America, 1976.
• 3. Wittbrodt B., Pearce J.M. The effects of PLA color on material properties of 3-D printed components. Additive Manufacturing Journal 2015; 110–116.
• 4. Leite M., João F., Deus A.M., et al. Study of the influence of 3D printing parameters on the mechanical properties of PLA. Journal of Physics, IOP Publisher, 2021.
• 5. Peng T. Analysis of energy utilization in 3D printing process. 13th Global Conference on Sustainable Manufacturing, Procedia CIRP 2016; 40, 62–67.
• 6. Ahmed A.A. Al-Duroobi, Ali M. Al-Bdairy, Laith Al-Juboori. Rapid prototyping of sculpture surfaces based on discrete algorithm using 3D printer technique. IEEE Xplore; 2024. https://doi.org/10.1109/ASET60340.2024.10708638.
• 7. Wu J. Study on optimization of 3D printing parameters. IOP Conf. Series: Materials Science and Engineering 2018; 392.
• 8. Arnold C., Monsees D., Hey J., Schweyen R. Surface quality of 3D-printed models as a function of various printing parameters. MDPI Journal of Materials, 2019; 12(12): 1970. https://doi.org/10.3390/ma12121970.
• 9. Abdullah, M.A., Abbas T.F. Numerical developing the internet of things to remotely monitor the performance of a three dimensions printer for free-form surface. J. Eng. Sci. Technol. 2023; 18(6): 2809–2822, https://doi.org/10.18517/ijaseit.14.2.19863.
• 10. Maldonado-García B., Pal A.K., Misra M., Gregori S., Mohanty A.K. Sustainable 3D printed composites from recycled ocean plastics and pyrolyzed soy-hulls, Composites Part C: Open Access 6, 2021.
• 11. Sanglae K., Andreu A., Kim I., Kim J.-H., Lee J., Yoonet Y.-J. Continuously varied infill pattern (ConVIP): improvement of mechanical properties and printing speed of fused filament fabrication (FFF) 3D printing. Journal of Materials Research and Technology, 2022; 18, 1055–1069.
• 12. Rahmatabadi A., Ghasemi I., Baniassadi M., Abrinia K., Baghani M. 3D printing of PLA-TPU with different component ratios: Fracture toughness, mechanical properties, and morphology. Journal of Materials Research and Technology, 2022; 21, 3970–3981. https://doi.org/10.1016/j.jmrt.2022.11.024.
• 13. Khosravani M.R., Berto F, Ayatollahi M.R., Reinicke T. Characterization of 3D-printed PLA parts with different raster orientations and printing speeds. Scientific Reports Journals, 2022; 12.
• 14. Hamed M.A., Abbas T.F. The impact of FDM process parameters on the compression strength of 3D printed PLA filaments for dental applications. Advances in Science and Technology Research Journal 2023; 17(4): 121–129. https://doi.org/10.12913/22998624/169468.
• 15. Geoffrey W. Rowe. Principles of industry metalworking process. Edward Arnold publishers, London, 1977.
• 16. Ahmed A., Duroobi, L.A.M. Prediction the effect of cutting parameters on surface roughness using Taguchi method. Eng. & Tech. Journal 2013; 31(17A): 2434–2442.
• 17. Abdullah, M.A., Ahmed, B.A., Ghazi, S.K. 2024. Enhancing of material removal rate and surface roughness in wire EDM process using grey relational analysis. Engineering, Technology & Applied Science Research 2024; 14(5): 17422–17427. https://doi.org/10.48084/etasr.8450.
• 18. Ghazi, S.K., Abdullah, M.A., Abdulridha, H.H. 2025. Investigating the impact of EDM parameters on surface roughness and electrode wear rate in 7024 aluminum alloy. Engineering, Technology & Applied Science Research 2025; 15(1): 19401–19407. https://doi.org/10.48084/etasr.9252.
• 19. Ghazi S.K., Maher Y.S., Aqeel S.B. Experimental evaluation of a system to control the incremental forming of aluminum alloy type 1050. Engineering, Technology & Applied Science Research 2024; 14(5): 16943–16949.
• 20. Abdulridha H.H., 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. Advances in Science and Technology Research Journal, 2023; 17(6): 49-62. https://doi.org/10.12913/22998624/173562.
• 21. Abdulridha Hind H., Abbas T.F., Bedan A.S. Investigate the effect of chemical post processing on the surface roughness of fused deposition modeling printed parts. Advances in Science and Technology Research Journal, 2024; 18(2): 47-60. https://doi.org/10.12913/22998624/183528.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).