Prediction and Optimization of Tensile Strength Values of 3D Printed PLA Components with RSM, ANOVA and ANN Analysis

Abstract

This study evaluates the comparative effectiveness of Response Surface Methodology (RSM), Analysis of Variance (ANOVA), and Artificial Neural Networks (ANN) in predicting and optimizing the tensile strength of 3D-printed PLA components. Key process parameters—including layer thickness, infill density, print speed, temperature, and build orientation—were systematically varied to analyze their impact on tensile strength. The results indicate that RSM and ANOVA offer higher prediction accuracy compared to ANN, with lower deviation rates (0.65%, 0.18%, and 3.43% for RSM; 0.20%, 0.12%, and 3.25% for ANOVA) versus ANN (5.93%, 3.88%, and 6.26%). The analysis revealed that layer thickness plays the most significant role in tensile strength, followed by temperature, infill density, build orientation, and print speed. The optimal combination of parameters—0.20 mm layer thickness, 50% infill density, 50 mm/s print speed, 220°C nozzle temperature, and 90° build orientation—yielded a maximum tensile strength of 55.506 MPa. These findings highlight the importance of parameter optimization in improving the mechanical properties of FDM-printed components. The study provides valuable insights for enhancing the reliability and efficiency of additive manufacturing processes, paving the way for future research on hybrid modeling techniques and alternative material applications.

Keywords

• Response surface methodology
• Analysis of variance
• Tensile strength
• Fused deposition modeling
• 3D printing
• PLA components
• Artifical neural networks
• Process optimization

References

1. [1] A.J. Sheoran, H. Kumar, “Fused Deposition modeling process parameters optimization and effect on mechanical properties and part quality: Review and reflection on present research”. Mater. Today Proc., 2019, 10, 14. https://doi.org/10.1016/j.matpr.2019.11.296
2. [2] A. Pandžić, D. Hodžić, E. Kadrić, “Experimental Investigation on Influence of Infill Density on Tensile Mechanical Properties of Different FDM 3D Printed Materials”. TEM Journal, 2021, 10(3). https://doi.org/10.18421/TEM103-25
3. [3] V. Wankhede, D. Jagetiya, A. Joshi, R. Chaudhari, “Experimental investigation of FDM process parameters using Taguchi analysis”. Mater. Today Proc., 2019, 27, 2117–2120. https://doi.org/10.1016/j.matpr.2019.09.078
4. [4] M. Algarni, S. Ghazali, “Comparative study of the sensitivity of PLA, ABS, PEEK, and PETG’s mechanical properties to FDM printing process parameters”. Crystals, 2021, 11.8: 995. https://doi.org/10.3390/cryst11080995
5. [5] A. Milovanović, A. Sedmak, A. Grbović, Z. Golubović, G. Mladenović, K. Čolić, M. Milošević, “Comparative analysis of printing parameters effect on mechanical properties of natural PLA and advanced PLA-X material”. Procedia Struct. Integr., 2020, 28, 1963–1968. https://doi.org/10.1016/j.prostr.2020.11.019
6. [6] G.C. Onwubolu, F. Rayegani, “Characterization and Optimization of Mechanical Properties of ABS Parts Manufactured by the Fused Deposition Modelling Process”. Hindawi, 2014, 1–13. http://dx.doi.org/10.1155/2014/598531
7. [7] H.B. Mamo, A.D. Tura, A.J. Santhos, N. Ashok, D.K. Rao, “Modeling and analysis of flexural strength with fuzzy logic technique for a fused deposition modeling ABS components”. Mater. Today Proc., 2022, 57, 768–774. https://doi.org/10.1016/j.matpr.2022.02.306
8. [8] S. Wang, Y. Ma, Z. Deng, S. Zhang, J. Cai, “Effects of fused deposition modeling process parameters on tensile, dynamic mechanical properties of 3D printed polylactic acid materials”. Polym. Test., 2020, 86, 106483. https://doi.org/10.1016/j.polymertesting.2020.106483

9. [9] J. Lluch-Cerezo, R. Benavente, M.D. Meseguer, S.C. Gutiérrez, “Study of samples geometry to analyze mechanical properties in Fused Deposition Modeling process (FDM)”. Procedia Manuf., 2019, 41, 890–897. https://doi.org/10.1016/j.promfg.2019.10.012
10. [10] Y.G. Zhou, J.R. Zou, H.H. Wu, B.P. Xu, “Balance between bonding and deposition during fused deposition modeling of polycarbonate and acrylonitrile‐butadiene‐styrene composites”. Polymer Composites, 2019, 41(1), 60-72. https://doi.org/10.1002/pc.25345
11. [11] A.W. Gebisa, H.G. Lemu, “Influence of 3D printing FDM process parameters on tensile property of ULTEM 9085”. Procedia Manufacturing, 2019, 30, 331-338. https://doi.org/10.1016/j.promfg. 2019.02.047
12. [12] K.I. Byberg, A.W. Gebisa, H.G. Lemu, “Mechanical properties of ULTEM 9085 material processed by fused deposition modeling”. Polymer Testing, 2018, 72, 335-347. https://doi.org/10.1016/j.polymertesting.2018.10.040
13. [13] K.P. Motaparti, G. Taylor, M.C. Leu, K. Chandrashekhara, J. Castle, M. Matlack, “Experimental investigation of effects of build parameters on flexural properties in fused deposition modelling parts”. Virtual and Physical Prototyping, 2017, 12(3), 207-220. https://doi.org/10.1080/17452759.2017.1314117
14. [14] K.J. Christiyan, U. Chandrasekhar, K. Venkateswarlu, “A study on the influence of process parameters on the Mechanical roperties of 3D printed ABS composite”. In IOP conference series: materials science and engineering, IOP Publishing, 2016, 114, 1, 012109. https://doi.org/10.1088/1757-899X/114/1/012109
15. [15] X. Zhou, S.J. Hsieh, C.C. Ting, “Modelling and estimation of tensile behaviour of polylactic acid parts manufactured by fused deposition modelling using finite element analysis and knowledge-based library”. Virtual and Physical Prototyping, 2018, 13(3), 177-190.https://doi.org/10.1080/17452759.2018.1442681
16. [16] A.W. Gebisa, H.G. Lemu, “Investigating effects of fused-deposition modeling (FDM) processing parameters on flexural properties of ULTEM 9085 using designed experiment”. Materials, 2018, 11(4), 500. https://doi.org/10.3390/ma11040500
17. [17] M.H. Hsueh, C.J. Lai, S.H. Wang, Y.S. Zeng, C.H. Hsieh, C.Y. Pan, W.C. Huang, “Effect of printing parameters on the thermal and mechanical properties of 3d-printed PLA and PETG, using fused deposition modeling”. Polymers, 2021.a, 13(11), 1758.https://doi.org/10.3390/polym13111758
18. [18] E.U. Enemuoh, S. Duginski, C. Feyen, V.G. Menta, “Effect of process parameters on energy consumption, physical, and mechanical properties of fused deposition modeling”. Polymers, 2021, 13(15), 2406. https://doi.org/10.3390/polym13152406
19. [19] M.H. Hsueh, C.J. Lai, K.Y. Liu, C.F. Chung, S.H. Wang, C.Y. Pan, Y.S. Zeng, “Effects of printing temperature and filling percentage on the mechanical behavior of fused deposition molding technology components for 3D printing”. Polymers, 2021.b, 13(17), 2910. https://doi.org/10.3390/polym13172910
20. [20] C. Patil, P.D. Sonawane, M. Naik, D.G. Thakur, “Finite element analysis of flexural test of additively manufactured components fabricated by fused deposition modelling”. In AIP Conference Proceedings AIP Publishing, December, 2020, 2311, 1. https://doi.org/10.1063/ 5.0034306
21. [21] R. Srinivasan, T. Pridhar, L.S. Ramprasath, N.S. Charan, W. Ruban, “Prediction of tensile strength in FDM printed ABS parts using response surface methodology (RSM)”. Materials Today: Proceedings, 2020, 27, 1827-1832. https://doi.org/10.1016/j.matpr.2020.03.788
22. [22] S. Deshwal, A. Kumar, D. Chhabra, “Exercising hybrid statistical tools GA-RSM, GA-ANN and GA-ANFIS to optimize FDM process parameters for tensile strength improvement”. CIRP Journal of Manufacturing Science and Technology, 2020, 31, 189-199. https://doi.org/10.1016/j.cirpj.2020.05.009
23. [23] A.D. Tura, H.G. Lemu, H.B. Mamo, “Experimental investigation and prediction of mechanical properties in a fused deposition modeling process”. Crystals, 2022, 12(6), 844. https://doi.org/10.3390/ cryst12060844
24. [24] M.S. Saad, A. Mohd Nor, M.Z. Zakaria, M.E. Baharudin, W.S. Yusoff, “Modelling and evolutionary computation optimization on FDM process for flexural strength using integrated approach RSM and PSO”. Progress in Additive Manufacturing, 2021, 6, 143-154. https://doi.org/10.1007/s40964-020-00157-z
25. [25] J. Giri, P. Shahane, S. Jachak, R. Chadge, P. Giri, “Optimization of FDM process parameters for dual extruder 3d printer using artificial neural network”. Materials Today: Proceedings, 2021, 43, 3242-3249. https://doi.org/10.1016/j.matpr.2021.01.899
26. [26] Y.G. Zhou, B. Su, L.S. Turng, “Deposition-induced effects of isotactic polypropylene and polycarbonate composites during fused deposition modeling”. Rapid Prototyping Journal, 2017, 23(5), 869-880. https://doi.org/10.1108/RPJ-12-2015-0189