Effect of 3D Printing Parameters on the Flexural Performance of Laminated Structures: A Taguchi and ANOVA-Based Approach

Abstract

The influence of important fused deposition modelling (FDM) parameters, including infill pattern, printing speed and wall thickness, on the flexural behaviors of multi-layered laminated polymer specimens, was investigated in this study. However, the literature remains limited regarding data on the flexural behavior of multi-material laminated FDM structures. To address this gap, the study develops a new three-layer laminated structure (PLA-ABS-CF/PLA) and provides statistical validation using Taguchi design and analysis of variance (ANOVA). The samples were manufactured in a three-layer structure consisting of PLA on the top, ABS in the core, and carbon fiber-reinforced PLA (CF/PLA) on the bottom. Nine experimental runs were designed according to a Taguchi L9 orthogonal array to evaluate the effect of process parameters on flexural stress. The experimental results showed that the highest flexural stress of 78.66 MPa was achieved with a ZigZag infill pattern, a printing speed of 40 mm/s, and a wall thickness of 2 mm. In the analysis of the signal-to-noise (S/N) ratio, the printing speed was found to be the most important parameter, followed by the infill pattern, with the wall thickness having a relatively minor effect. The ANOVA results showed that the contribution of printing speed to the total variation in flexural stress was 91.8% (p = 0.011) and wall thickness was statistically insignificant (p = 0.075). Three-dimensional surface response plots were used to visualize interaction effects between the parameters. Overall, results show that optimized printing speed and infill design significantly improve the flexural performance of laminated multi-material structures manufactured by FDM.

Keywords

• Fused deposition modeling
• Laminated polymer composites
• Flexural stress
• Taguchi design of experiments
• Multi-material 3D printing

Ethical Statement

Ethical approval was not required for this study since no experiments involving human participants or animals were conducted.

References

1. Alatefi, M., Al-Ahmari, A. M., Saleh, M., Elgawad, A. E. E. A., & AlFaify, A. Y. (2025). Integrated FDM optimization with multivariate capability analysis for dimensional and compressive mechanical properties. Scientific Reports, 15(1), Article 8234. https://doi.org/10.1038/s41598-025-30098-y
2. Ammar, S., Ben Fraj, B., Hentati, H., Saouab, A., Ben Amar, M., & Haddar, M. (2024). Mechanical performances of printed carbon fiber-reinforced PLA and PETG composites. Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications, 238(7), 1488–1499. https://doi.org/10.1177/14644207231225761
3. Balasrinivasan, M., Varun Kumar, A., & Sathickbasha, K. (2024). A comparative study on mechanical properties of PLA, PETG, and carbon fiber prepared by fused deposition modeling. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. Advance online publication. https://doi.org/10.1177/09544089241301961
4. Dağlı, S. (2025). Mechanical characterization and interface evaluation of multi-material composites manufactured by hybrid fused deposition modeling. Polymers, 17(12), Article 1631. https://doi.org/10.3390/polym17121631
5. Demir, S. (2025). Optimization of mechanical properties in FDM 3D-printed PLA parts: Evaluating the interaction of key parameters. Journal of Materials Engineering and Performance, 34(13), 28566–28580. https://doi.org/10.1007/s11665-025-11320-4
6. Ekşi, S., & Karakaya, C. (2025). Effects of process parameters on tensile properties of 3D-printed PLA parts fabricated with the FDM method. Polymers, 17(14), Article 1934. https://doi.org/10.3390/polym17141934
7. Girimurugan, R., Arunraja, K. M., Shanmugam, A., Saranya, S., & Vigneshwaran, M. (2023). The effects of nano-alumina particles on the enrichment of tensile, flexural and impact properties of carbon fiber-reinforced epoxy composites. Materials Today: Proceedings, 80(3), 2235–2239. https://doi.org/10.1016/j.matpr.2023.04.053
8. Le, A.-T., & Le Hoang, A. (2023). Comparisons of flexural, split tensile, double punch, and direct tension tests on high-performance concrete reinforced with different fiber types. Case Studies in Construction Materials, 19, Article e02413. https://doi.org/10.1016/j.cscm.2023.e02413

9. Liu, Q., Zhang, Z., Yavas, D., Shen, W., & Wu, D. (2023). Multi-material additive manufacturing: Effect of process parameters on flexural behavior of soft-hard sandwich beams. Rapid Prototyping Journal, 29(5), 885–896. https://doi.org/10.1108/RPJ-07-2022-0231
10. Maguluri, N., Suresh, G., & Rao, K. V. (2023). Assessing the effect of FDM processing parameters on mechanical properties of PLA parts using Taguchi method. Journal of Thermoplastic Composite Materials, 36(6), 1472–1488. https://doi.org/10.1177/08927057211053036
11. Moradi, M., Rezayat, M., Rozhbiany, F. A. R., Meiabadi, S., Casalino, G., Shamsborhan, M., Bijoy, A., Chakkingal, S., Lawrence, M., Mohammed, N., & Karamimoghadam, M. (2023). Correlation between infill percentages, layer width, and mechanical properties in fused deposition modelling of poly-lactic acid 3D printing. Machines, 11(10), Article 950. https://doi.org/10.3390/machines11100950
12. Patel, R., Jani, S., & Joshi, A. (2023). Review on multi-objective optimization of FDM process parameters for composite materials. International Journal on Interactive Design and Manufacturing, 17(4), 2115–2125. https://doi.org/10.1007/s12008-022-01111-9
13. Popović, M., Pjević, M., Milovanović, A., Mladenović, G., & Milošević, M. (2023). Printing parameter optimization of PLA material concerning geometrical accuracy and tensile properties relative to FDM process productivity. Journal of Mechanical Science and Technology, 37(2), 697–706. https://doi.org/10.1007/s12206-023-0113-6
14. Rahim, T. N. A. T., Abdullah, A. M., & Md Akil, H. (2019). Recent developments in fused deposition modeling-based 3D printing of polymers and their composites. Polymer Reviews, 59(4), 589–624. https://doi.org/10.1080/15583724.2019.1597883
15. Tebassi, H., Yallese, M. A., & Belhadi, S. (2023). Optimization and machinability assessment at the optimal solutions across Taguchi OA, GRA, and BBD: An overall view. Arabian Journal for Science and Engineering, 48(9), 12455–12483. https://doi.org/10.1007/s13369-023-07825-6
16. Zhou, L., Miller, J., Vezza, J., Mayster, M., Raffay, M., Justice, Q., Al Tamimi, Z., Hansotte, G., Sunkara, L. D., & Bernat, J. (2024). Additive manufacturing: A comprehensive review. Sensors, 24(8), Article 2668. https://doi.org/10.3390/s24092668