TOPSIS-Driven Optimization of FFF Process Parameters for Mechanical Strength Enhancement

Open Access

Year : 2024 | Volume :11 | Special Issue : 12 | Page : 246-255
By

Sourabh Anand*

Manoj Kumar Satyarthi

  1. Research Scholar University School of Information New Delhi India
  2. Assistant Professor University School of Information, Communication and Technology New Delhi India

Abstract

In this study, the application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for optimizing Fused Filament Fabrication (FFF) process parameters to enhance the tensile and flexural strength of Polylactic Acid (PLA) material-based 3D printed components is explored. This investigation delves into the intricate relationship between key parameters, such as layer height, print speed, infill density, print temperature, and nozzle diameter, and their impact on material strength. The experimental results reveal a significant improvement in both tensile and flexural strength, establishing PLA as an exemplary choice for manufacturing robust components suitable for diverse applications, ranging from prototyping to customized product development. The identified optimal settings, including a layer height of 0.23 mm, a print speed of 55 mm/s, print orientation at 45°, 100% infill density, and a print temperature of 220°C, contribute to the enhanced performance of the FFF technique. These findings contribute valuable insights for practitioners seeking to achieve superior mechanical properties in PLA-based 3D printed components.

Keywords: Fused Filament Fabrication process, TOPSIS, Multi Objective Optimization, Poly Lactic Acid

[This article belongs to Special Issue under section in Journal of Polymer and Composites(jopc)]

How to cite this article: Sourabh Anand*, Manoj Kumar Satyarthi. TOPSIS-Driven Optimization of FFF Process Parameters for Mechanical Strength Enhancement. Journal of Polymer and Composites. 2024; 11(12):246-255.
How to cite this URL: Sourabh Anand*, Manoj Kumar Satyarthi. TOPSIS-Driven Optimization of FFF Process Parameters for Mechanical Strength Enhancement. Journal of Polymer and Composites. 2024; 11(12):246-255. Available from: https://journals.stmjournals.com/jopc/article=2024/view=137990

Full Text PDF Download

Browse Figures

References

  1. Chatham CA, Long TE, Williams CB. A review of the process physics and material screening methods for polymer powder bed fusion additive manufacturing. Progress in Polymer Science. 2019 Jun 1; 93:68–
  2. Steenhuis HJ, Pretorius L. The additive manufacturing innovation: a range of implications. Journal of Manufacturing Technology Management. 2017 Feb 6;28(1):122–
  3. Huang Y, Leu MC, Mazumder J, Donmez A. Additive manufacturing: current state, future potential, gaps and needs, and recommendations. Journal of Manufacturing Science and Engineering. 2015 Feb 1;137(1):014001.
  4. Szymczyk-Ziółkowska P, Łabowska MB, Detyna J, Michalak I, Gruber P. A review of fabrication polymer scaffolds for biomedical applications using additive manufacturing techniques. Biocybernetics and Biomedical Engineering. 2020 Apr 1;40(2):624–
  5. Singh S, Ramakrishna S, Singh R. Material issues in additive manufacturing: A review. Journal of Manufacturing Processes. 2017 Jan 1; 25:185–
  6. Gao G, Xu F, Xu J, Tang G, Liu Z. A survey of the influence of process parameters on mechanical properties of fused deposition modeling parts. Micromachines. 2022 Mar 30;13(4):553.
  7. Shaikh SE. Holistic support for cross-organisational workflow negotiation: Requirements and an approach. The University of Manchester (United Kingdom); 2006.
  8. Kamaal M, Anas M, Rastogi H, Bhardwaj N, Rahaman A. Effect of FDM process parameters on mechanical properties of 3D-printed carbon fibre–PLA composite. Progress in Additive Manufacturing. 2021 Feb; 6:63–
  9. Vishwas M, Basavaraj CK, Vinyas M. Experimental investigation using taguchi method to optimize process parameters of fused deposition Modeling for ABS and nylon materials. Materials Today: Proceedings. 2018 Jan 1;5(2):7106–
  10. Chacón JM, Caminero MA, García-Plaza E, Núnez PJ. Additive manufacturing of PLA structures using fused deposition modelling: Effect of process parameters on mechanical properties and their optimal selection. Materials & Design. 2017 Jun 15; 124:143–
  11. Rao VD, Rajiv P, Geethika VN. Effect of fused deposition modelling (FDM) process parameters on tensile strength of carbon fibre PLA. Materials Today: Proceedings. 2019 Jan 1; 18:2012–
  12. Rinanto A, Nugroho A, Prasetyo H, Pujiyanto E. Simultaneous Optimization of TensileStrength, Energy Consumption and Processing Time on FDM Process Using Taguchi and PCR-TOPSIS. In 2018 4th International Conference on Science and Technology (ICST) 2018 Aug 7 (pp. 1–5). IEEE.
  13. Durgun I, Ertan R. Experimental investigation of FDM process for improvement of mechanical properties and production cost. Rapid Prototyping Journal. 2014 Apr 14;20(3):228–
  14. Vinodh S. Parametric optimization of fused deposition modelling process using Grey based Taguchi and TOPSIS methods for an automotive component. Rapid Prototyping Journal. 2020 Nov 24;27(1):155–
  15. Alsoufi MS, Elsayed AE. How surface roughness performance of printed parts manufactured by desktop FDM 3D printer with PLA+ is influenced by measuring direction. Am. J. Mech. Eng. 2017;5(5):211–
  16. Hsueh MH, Lai CJ, Wang SH, Zeng YS, Hsieh CH, Pan CY, Huang WC. Effect of printing parameters on the thermal and mechanical properties of 3d-printed pla and petg, using fused deposition modeling. Polymers. 2021 May 27;13(11):1758.
  17. Chohan JS, Kumar R, Singh TB, Singh S, Sharma S, Singh J, Mia M, Pimenov DY, Chattopadhyaya S, Dwivedi SP, Kapłonek W. Taguchi s/n and topsis based optimization of fused deposition modelling and vapor finishing process for manufacturing of ABS plastic parts. Materials. 2020 Nov 17;13(22):5176.
  18. Pavić Z, Novoselac V. Notes on TOPSIS method. International Journal of Research in Engineering and Science. 2013 Jan;1(2):5–
  19. Anand MB, Vinodh S. Application of fuzzy AHP–TOPSIS for ranking additive manufacturing processes for microfabrication. Rapid Prototyping Journal. 2018 Mar 12;24(2):424–
  20. Tzeng GH, Huang JJ. Multiple attribute decision making: methods and applications. CRC press; 2011 Jun 22.

Special Issue Open Access Original Research
Volume 11
Special Issue 12
Received December 19, 2023
Accepted January 1, 2024
Published April 2, 2024