Journal of Materials Engineering and Performance

, Volume 28, Issue 1, pp 169–182 | Cite as

Experimental Investigations for Optimizing the Extrusion Parameters on FDM PLA Printed Parts

  • Leipeng Yang
  • Shujuan LiEmail author
  • Yan Li
  • Mingshun Yang
  • Qilong Yuan


Fused deposition modeling (FDM) has become one of the most extensively used additive manufacturing technologies in recent years because of its wide adaptability, simple mechanism and low cost. It is difficult, however, to achieve an equitable trade-off among mechanical properties, surface finish quality and production time, which is an area seldom explored. This paper concentrates on the optimization of the parameters to achieve higher tensile strength and lower surface roughness with less build time during the FDM process based on central composite design for the tensile specimen forming process. The effects of five extrusion parameters (nozzle diameter, liquefier temperature, extrusion velocity, filling velocity and layer thickness) on the three outputs of tensile strength (TS), surface roughness (SR) and build time (BT) are investigated. Response surface methodology combined with nondominated sorting genetic algorithm II is developed to optimize the process parameters to achieve the maximum TS, minimum SR and BT, as verified by subsequent experiments. The predicted results are found to be very close to the experimental data, illustrating that the presented approach in this paper is effective for improving mechanical properties, surface finish and efficiency of the FDM process.


build time fused deposition modeling multiobjective optimization response surface methodology surface roughness tensile strength 



The authors wish to acknowledge the financial support from the National Natural Science Foundation of China (Grant No. 51575442) and the National Natural Science Foundation of Shaanxi Province (Grant No. 2016JZ011).


  1. 1.
    S. Ford and M. Despeisse, Additive Manufacturing and Sustainability: An Exploratory Study of the Advantages and Challenges, J. Clean. Prod., 2016, 137, p 1573–1587CrossRefGoogle Scholar
  2. 2.
    J.W. Comb, W.R. Priedeman, and P.W. Turley, FDM Technology Process Improvements, University of Texas, Austin, 1994, p 42–49Google Scholar
  3. 3.
    P.F. Jacobs and D.T. Reid, Rapid Prototyping & Manufacturing: Fundamentals of Stereolithography, SME Publication, Dearborn, 1992Google Scholar
  4. 4.
    J.J. Beaman, J.W. Barlow, D.L. Bourell, J.W. Barlow, R.H. Crawford, and K.P. McAlea, Solid Freeform Fabrication: A New Direction in Manufacturing, Springer, New York, 1997, p 25–49CrossRefGoogle Scholar
  5. 5.
    M.E. Sachs, J.S. Haggerty, M.J. Cima, and P.A. Williams, Three dimensional printing techniques, US Patent, 5204055, 1993.Google Scholar
  6. 6.
    M. Feygin and B. Hsieh, Laminated object manufacturing (LOM): a simpler process. In: Proceedings of Solid Freeform Fabrication Symposium, Austin, TX, p 123–130, 1991Google Scholar
  7. 7.
    O.A. Mohamed, S.H. Masood, J.L. Bhowmik et al., Effect of Process Parameters on Dynamic Mechanical Performance of FDM PC/ABS Printed Parts Through Design of Experiment, J. Mater. Eng. Perform., 2016, 25(7), p 1–14CrossRefGoogle Scholar
  8. 8.
    W.W. Yu, J. Zhang, J.R. Wu et al., Incorporation of Graphitic Nano-filler and Poly(lactic acid) in Fused Deposition Modeling, J. Appl. Polym. Sci., 2017, 134(15), p 44703CrossRefGoogle Scholar
  9. 9.
    C.C. Wang, T. Lin, and S. Hu, Optimizing the Rapid Prototyping Process by Integrating the Taguchi Method with the Gray Relational Analysis, Rapid Prototyp. J., 2007, 13(13), p 304–315CrossRefGoogle Scholar
  10. 10.
    J.W. Zhang and A.H. Peng, Process-Parameter Optimization for Fused Deposition Modeling Based on Taguchi Method, Adv. Mater. Res., 2012, 538-541, p 444–447CrossRefGoogle Scholar
  11. 11.
    A.K. Sood, R.K. Ohdar, and S.S. Mahapatra, Parametric Appraisal of Mechanical Property of Fused Deposition Modelling Processed Parts, Mater. Des., 2010, 31(1), p 287–295CrossRefGoogle Scholar
  12. 12.
    F. Rayegani and G.C. Onwubolu, Fused Deposition Modelling (FDM) Process Parameter Prediction and Optimization Using Group Method for Data Handling (GMDH) and Differential Evolution (DE), Int. J. Adv. Manuf. Technol., 2014, 73(1-4), p 509–519CrossRefGoogle Scholar
  13. 13.
    M.S. Hossain, D. Espalin, J. Ramos et al., Improved Mechanical Properties of Fused Deposition Modeling-Manufactured Parts Through Build Parameter Modifications, J. Manuf. Sci. E-T ASME, 2014, 136(6), p 12Google Scholar
  14. 14.
    A. Peng, X. Xiao, and R. Yue, Process Parameter Optimization for Fused Deposition Modeling Using Response Surface Methodology Combined with Fuzzy Inference System, Int. J. Adv. Manuf. Technol., 2014, 73(1-4), p 87–100CrossRefGoogle Scholar
  15. 15.
    M. Dawoud, I. Taha, and S.J. Ebeid, Mechanical Behaviour of ABS: An Experimental Study Using FDM and Injection Moulding Techniques, J. Manuf. Process., 2016, 21, p 39–45CrossRefGoogle Scholar
  16. 16.
    J. Torres, M. Cole, A. Owji et al., An Approach for Mechanical Property Optimization of Fused Deposition Modeling with Polylactic Acid via Design of Experiments, Rapid Prototyp. J., 2016, 22(2), p 387–404CrossRefGoogle Scholar
  17. 17.
    B.N. Panda, K. Shankhwar, A. Garg et al., Performance Evaluation of Warping Characteristic of Fused Deposition Modelling Process, Int. J. Adv. Manuf. Technol., 2016, 88(5-8), p 1–13Google Scholar
  18. 18.
    R. Singh, S. Singh, and K. Mankotia, Development of ABS Based Wire as Feedstock Filament of FDM for Industrial Applications, Rapid Prototyp. J., 2016, 22(2), p 300–310CrossRefGoogle Scholar
  19. 19.
    O.A. Mohamed, S.H. Masood, and J.L. Bhowmik, Mathematical Modeling and FDM Process Parameters Optimization Using Response Surface Methodology Based on Q-Optimal Design, Appl. Math. Model., 2016, 40(23-24), p 10052–10073CrossRefGoogle Scholar
  20. 20.
    E. Vahabli and S. Rahmati, Application of an RBF Neural Network for FDM Parts’ Surface Roughness Prediction for Enhancing Surface Quality, Int. J. Precis. Eng. Manag., 2016, 17(12), p 1589–1603CrossRefGoogle Scholar
  21. 21.
    B. Standards, Plastics—Determination of Tensile Properties—Part 1: General Principles (ISO 527-1:2012). CEN/TC 249—PlasticsGoogle Scholar
  22. 22.
    The technical data sheet of the PolyPlus-PLA characteristics., 2017. Accessed 26 June 2017
  23. 23.
    Ö. Bayraktar, G. Uzun, R.C. Akiroğlu et al., Experimental Study on the 3D-Printed Plastic Parts and Predicting the Mechanical Properties Using Artificial Neural Networks, Polym. Adv. Technol., 2017, 28, p 1044–1051CrossRefGoogle Scholar
  24. 24.
    N.S.A. Bakar, M.R. Alkahari, and H. Boejang, Analysis on Fused Deposition Modelling Performance, J. Zhejiang Univ. Sci. A, 2010, 11(12), p 972–977CrossRefGoogle Scholar
  25. 25.
    K. Deb, A. Pratap, S. Agarwal et al., A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II, IEEE Trans. Evol. Comput., 2002, 6(2), p 182–197CrossRefGoogle Scholar

Copyright information

© ASM International 2018

Authors and Affiliations

  • Leipeng Yang
    • 1
  • Shujuan Li
    • 1
    Email author
  • Yan Li
    • 1
  • Mingshun Yang
    • 1
  • Qilong Yuan
    • 1
  1. 1.School of Mechanical and Precision Instrument EngineeringXi’an University of TechnologyXi’anChina

Personalised recommendations