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Optimization of surface roughness in FDM 3D printer using response surface methodology, particle swarm optimization, and symbiotic organism search algorithms

  • Mohd Sazli SaadEmail author
  • Azuwir Mohd Nor
  • Mohamad Ezral Baharudin
  • Mohd Zakimi Zakaria
  • A.F Aiman
ORIGINAL ARTICLE
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Abstract

Additive manufacturing (AM) technologies such as fused deposition modeling (FDM) have been widely used in various fields of today’s manufacturing industries such as transportation, aerospace, and medical because of its ability to produce parts of complex designs with less manufacturing time and cost. However, a proper selection of input process parameters is a vital aspect in order to obtain the best quality of the printed part. This paper presents several approaches, namely response surface methodology, particle swarm optimization, and symbiotic organism search, to find the optimal parameter settings for better surface quality, i.e., surface roughness of the FDM printed part. Layer height, print speed, print temperature, and outer shell speed were considered as the input parameters and surface roughness as the output response. The experimental design was carried out using response surface methodology (RSM) method. Then, the relationship between the input parameters and the surface roughness was established using regression model. Once the accuracy of the model had been validated, the model was then coupled with particle swarm optimization (PSO) and symbiotic organism search (SOS) to optimize the input parameters that would lead to minimum surface roughness. Experimental results showed that the surface roughness obtained using PSO and SOS have improved about 8.5% and 8.8%, respectively, compared with the conventional method, i.e., RSM. A good agreement between the predicted surface roughness and the experimental values was also observed.

Keywords

Fused deposition modeling (FDM) Surface roughness Particle swarm optimization Symbiotic organism search Response surface methodology 

Notes

Acknowledgments

This research study was supported by the researchers from University Malaysia Perlis. The authors would like to express their gratitude to University Malaysia Perlis for their guidance in order to complete this research study.

References

  1. 1.
    Yan Q, Dong H, Su J, Han J, Song B, Wei Q, Shi Y (2018) A review of 3D printing technology for medical applications. Engineering 4(5):729–742.  https://doi.org/10.1016/j.eng.2018.07.021 CrossRefGoogle Scholar
  2. 2.
    Singamneni S, Yifan L, Hewitt A, Chalk R, Thomas W (2019) Additive manufacturing for the aircraft industry: a review. J Aeronaut Aerospace Eng 8(214):2Google Scholar
  3. 3.
    Deswal S, Narang R, Chhabra D (2019) Modeling and parametric optimization of FDM 3D printing process using hybrid techniques for enhancing dimensional preciseness. Int J Interact Des Manuf (IJIDeM):1-18Google Scholar
  4. 4.
    Lyu J, Manoochehri S (2019) Multi-objective optimization based on machine reliability and process-dependent product quality for FDM system. Int J Adv Manuf Technol:1-10Google Scholar
  5. 5.
    Griffiths C, Howarth J, De Almeida-Rowbotham G, Rees A, Kerton R (2016) A design of experiments approach for the optimisation of energy and waste during the production of parts manufactured by 3D printing. J Clean Prod 139:74–85CrossRefGoogle Scholar
  6. 6.
    Boursier J-F, Fournet A, Bassanino J, Manassero M, Bedu A-S, Leperlier D (2018) Reproducibility, accuracy and effect of autoclave sterilization on a thermoplastic three-dimensional model printed by a desktop fused deposition modelling three-dimensional printer. Vet Comp Orthopaed Traumatol 31(06):422–430CrossRefGoogle Scholar
  7. 7.
    Leite M, Varanda A, Ribeiro AR, Silva A, Vaz MF (2018) Mechanical properties and water absorption of surface modified ABS 3D printed by fused deposition modelling. Rapid Prototyp J 24(1):195–203CrossRefGoogle Scholar
  8. 8.
    Srivastava M, Rathee S, Maheshwari S, Kundra T (2019) Estimating percentage contribution of process parameters towards build time of FDM process for components displaying spatial symmetry: a case study. Int J Mater Prod Technol 58(2-3):201–224CrossRefGoogle Scholar
  9. 9.
    Mohamed OA, Masood SH, Bhowmik JL (2015) Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Adv Manuf 3(1):42–53CrossRefGoogle Scholar
  10. 10.
    Boér J, Blaga P (2018) Reducing production costs by monitoring the roughness of raw product surfaces. Procedia Manuf 22:202–208.  https://doi.org/10.1016/j.promfg.2018.03.031 CrossRefGoogle Scholar
  11. 11.
    Alsoufi MS, Elsayed AE (2018) Surface roughness quality and dimensional accuracy-a comprehensive analysis of 100% infill printed parts fabricated by a personal/desktop cost-effective FDM 3D printer. Mater Sci Appl 9:11–40Google Scholar
  12. 12.
    Peng T, Yan F (2018) Dual-objective analysis for desktop FDM printers: energy consumption and surface roughness. Procedia CIRP 69:106–111CrossRefGoogle Scholar
  13. 13.
    Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol 118(1):385–388.  https://doi.org/10.1016/S0924-0136(01)00980-3 CrossRefGoogle Scholar
  14. 14.
    Reddy V, Flys O, Chaparala A, Berrimi CE, Amogh V, Rosén BG (2018) Study on surface texture of Fused Deposition Modeling. Procedia Manuf 25:389–396CrossRefGoogle Scholar
  15. 15.
    Nancharaiah T, Raju DR, Raju VR (2010) An experimental investigation on surface quality and dimensional accuracy of FDM components. Int J Emerg Technol 1(2):106–111Google Scholar
  16. 16.
    Horvath D, Noorani R, Mendelson M Improvement of surface roughness on ABS 400 polymer using design of experiments (DOE). In: Materials Science Forum, 2007. Trans Tech Publ, pp 2389-2392Google Scholar
  17. 17.
    Chung Wang C, Lin T-W, Hu S-S (2007) Optimizing the rapid prototyping process by integrating the Taguchi method with the Gray relational analysis. Rapid Prototyp J 13(5):304–315CrossRefGoogle Scholar
  18. 18.
    Jiang R, Kleer R, Piller FT (2017) Predicting the future of additive manufacturing: A Delphi study on economic and societal implications of 3D printing for 2030. Technol Forecast Soc 117:84–97CrossRefGoogle Scholar
  19. 19.
    Thrimurthulu K, Pandey PM, Venkata Reddy N (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tool Manuf 44(6):585–594.  https://doi.org/10.1016/j.ijmachtools.2003.12.004 CrossRefzbMATHGoogle Scholar
  20. 20.
    Asadollahi-Yazdi E, Gardan J, Lafon P (2018) Multi-objective optimization of additive manufacturing process. IFAC-PapersOnLine 51(11):152–157.  https://doi.org/10.1016/j.ifacol.2018.08.250 CrossRefGoogle Scholar
  21. 21.
    Rao RV, Rai DP (2016) Optimization of fused deposition modeling process using teaching-learning-based optimization algorithm. Eng Sci Technol Int J 19(1):587–603CrossRefGoogle Scholar
  22. 22.
    Pandey PM, Thrimurthulu K, Reddy* NV (2004) Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm. Int J Prod Res 42(19):4069–4089CrossRefGoogle Scholar
  23. 23.
    Byun HS, Lee* KH (2005) Determination of the optimal part orientation in layered manufacturing using a genetic algorithm. Int J Prod Res 43(13):2709–2724.  https://doi.org/10.1080/00207540500031857 CrossRefzbMATHGoogle Scholar
  24. 24.
    Khatir S, Wahab MA, Benaissa B, Köppen M Crack identification using eXtended IsoGeometric analysis and particle swarm optimization. In: Fracture, fatigue and wear, 2018. Springer, pp 210-222Google Scholar
  25. 25.
    Prasad D, Mukherjee V (2016) A novel symbiotic organisms search algorithm for optimal power flow of power system with FACTS devices. Eng Sci Technol Int J 19(1):79–89CrossRefGoogle Scholar
  26. 26.
    Tran D-H, Cheng M-Y, Prayogo D (2016) A novel Multiple Objective Symbiotic Organisms Search (MOSOS) for time–cost–labor utilization tradeoff problem. Knowl-Based Syst 94:132–145CrossRefGoogle Scholar
  27. 27.
    Yamada T, Febri Z (2015) Freight transport network design using particle swarm optimisation in supply chain–transport supernetwork equilibrium. Transport Res E Log 75:164–187CrossRefGoogle Scholar
  28. 28.
    Noordin MY, Venkatesh VC, Sharif S, Elting S, Abdullah A (2004) Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. J Mater Process Technol 145(1):46–58CrossRefGoogle Scholar
  29. 29.
    Nee CY, Saad MS, Mohd Nor A, Zakaria MZ, Baharudin ME (2018) Optimal process parameters for minimizing the surface roughness in CNC lathe machining of Co28Cr6Mo medical alloy using differential evolution. Int J Adv Manuf Technol. 97:1541–1555.  https://doi.org/10.1007/s00170-018-1817-0 CrossRefGoogle Scholar
  30. 30.
    Lujan-Moreno GA, Howard PR, Rojas OG, Montgomery DC (2018) Design of experiments and response surface methodology to tune machine learning hyperparameters, with a random forest case-study. Expert Syst Appl 109:195–205.  https://doi.org/10.1016/j.eswa.2018.05.024 CrossRefGoogle Scholar
  31. 31.
    Eberhart R, Kennedy J A new optimizer using particle swarm theory. In: MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, 1995. Ieee, pp 39-43Google Scholar
  32. 32.
    Cheng M-Y, Prayogo D (2014) Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput Struct 139:98–112CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Mohd Sazli Saad
    • 1
    Email author
  • Azuwir Mohd Nor
    • 1
  • Mohamad Ezral Baharudin
    • 1
  • Mohd Zakimi Zakaria
    • 1
  • A.F Aiman
    • 1
  1. 1.School of Manufacturing EngineeringUniversiti Malaysia PerlisArauMalaysia

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