Advances in Manufacturing

, Volume 3, Issue 1, pp 42–53 | Cite as

Optimization of fused deposition modeling process parameters: a review of current research and future prospects

  • Omar A. MohamedEmail author
  • Syed H. Masood
  • Jahar L. Bhowmik


Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for various engineering applications. FDM process has been introduced commercially in early 1990s by Stratasys Inc., USA. The quality of FDM processed parts mainly depends on careful selection of process variables. Thus, identification of the FDM process parameters that significantly affect the quality of FDM processed parts is important. In recent years, researchers have explored a number of ways to improve the mechanical properties and part quality using various experimental design techniques and concepts. This article aims to review the research carried out so far in determining and optimizing the process parameters of the FDM process. Several statistical designs of experiments and optimization techniques used for the determination of optimum process parameters have been examined. The trends for future FDM research in this area are described.


Fused deposition modeling (FDM) Experimental design Additive manufacturing Process parameters Mechanical properties Part quality 


  1. 1.
    Gebhardt A (2003) Rapid prototyping. Hanser, MunichCrossRefGoogle Scholar
  2. 2.
    Gibson I, Rosen DW, Stucker B (2010) Additive manufacturing technologies. Springer, HeidelbergCrossRefGoogle Scholar
  3. 3.
    Kai CC, Fai LK, Chu-Sing L (2003) Rapid prototyping: principles and applications in manufacturing. World Scientific Publishing Co. Pte. Ltd., SingaporeGoogle Scholar
  4. 4.
    Upcraft S, Fletcher R (2003) The rapid prototyping technologies. Assem Autom 23(4):318–330CrossRefGoogle Scholar
  5. 5.
    Mansour S, Hague R (2003) Impact of rapid manufacturing on design for manufacture for injection moulding. Proc Inst Mech Eng Part B 217(4):453–461CrossRefGoogle Scholar
  6. 6.
    Hopkinson N, Hague R, Dickens P (eds) (2006) Rapid manufacturing: an industrial revolution for the digital age. Wiley, New JerseyCrossRefGoogle Scholar
  7. 7.
    Bernard A, Fischer A (2002) New trends in rapid product development. CIRP Ann Manuf Technol 51(2):635–652CrossRefGoogle Scholar
  8. 8.
    Gebhardt A (2012) Understanding additive manufacturing. Carl Hanser Verlag GmbH & Co. KG, MunichCrossRefGoogle Scholar
  9. 9.
    Kai CC, Fai LK, Chu-Sing L (2010) Rapid prototyping: principles and applications. World Scientific Publishing Co. Pte. Ltd., SingaporeGoogle Scholar
  10. 10.
    Noorani R (2006) Rapid prototyping: principles and applications. Wiley, New JerseyGoogle Scholar
  11. 11.
    Montero M, Roundy S, Odell D et al (2001) Material characterization of fused deposition modeling ABS by designed experiments. In: Proceedings of Rapid Prototyping and Manufacturing Conference. Cincinnati, OH, USAGoogle Scholar
  12. 12.
    Masood SH (1996) Intelligent rapid prototyping with fused deposition modelling. Rapid Prototyp J 2(1):24–33CrossRefGoogle Scholar
  13. 13.
    Groza JR, Shackelford JF (2010) Materials processing handbook. CRC Press, Boca RatonGoogle Scholar
  14. 14.
    Anitha R, Arunachalam S, Radhakrishnan P (2001) Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol 118(1–3):385–388CrossRefGoogle 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.
    Thrimurthulu K, Pandey PM, Reddy NV (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf 44(6):585–594CrossRefGoogle Scholar
  17. 17.
    Horvath D, Noorani R, Mendelson M (2007) Improvement of surface roughness on ABS 400 polymer using design of experiments (DOE). Mater Sci Forum 561:2389–2392CrossRefGoogle Scholar
  18. 18.
    Wang CC, Lin TW, Hu SS (2007) Optimizing the rapid prototyping process by integrating the Taguchi method with the gray relational analysis. Rapid Prototyp J 13(5):304–315CrossRefGoogle Scholar
  19. 19.
    Sood AK, Ohdar R, Mahapatra S (2009) Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater Des 30(10):4243–4252CrossRefGoogle Scholar
  20. 20.
    Zhang JW, Peng AH (2012) Process-parameter optimization for fused deposition modeling based on Taguchi method. Adv Mater Res 538:444–447CrossRefGoogle Scholar
  21. 21.
    Sahu RK, Mahapatra S, Sood AK (2013) A study on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic. J Manuf Sci Prod 13(3):183–197Google Scholar
  22. 22.
    Lee B, Abdullah J, Khan Z (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169(1):54–61CrossRefGoogle Scholar
  23. 23.
    Laeng J, Khan ZA, Khu SY (2006) Optimizing flexible behaviour of bow prototype using Taguchi approach. J Appl Sci 6:622–630CrossRefGoogle Scholar
  24. 24.
    Zhang Y, Chou K (2008) A parametric study of part distortions in fused deposition modelling using three-dimensional finite element analysis. Proc Inst Mech Eng Part B 222(8):959–968CrossRefGoogle Scholar
  25. 25.
    Nancharaiah T (2011) Optimization of process parameters in FDM process using design of experiments. Int J Emerg Technol 2(1):100–102Google Scholar
  26. 26.
    Kumar GP, Regalla SP (2012) Optimization of support material and build time in fused deposition modeling (FDM). Appl Mech Mater 110:2245–2251Google Scholar
  27. 27.
    Ahn SH, Montero M, Odell D et al (2002) Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyp J 8(4):248–257CrossRefGoogle Scholar
  28. 28.
    Ang KC, Leong KF, Chua CK et al (2006) Investigation of the mechanical properties and porosity relationships in fused deposition modelling-fabricated porous structures. Rapid Prototyp J 12(2):100–105CrossRefGoogle Scholar
  29. 29.
    Sood AK, Ohdar RK, Mahapatra SS (2010) Parametric appraisal of mechanical property of fused deposition modelling processed parts. Mater Des 31(1):287–295CrossRefGoogle Scholar
  30. 30.
    Percoco G, Lavecchia F, Galantucci LM (2012) Compressive properties of FDM rapid prototypes treated with a low cost chemical finishing. Res J Appl Sci Eng Technol 4(19):3838–3842Google Scholar
  31. 31.
    Rayegani F, Onwubolu GC (2014) 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 73(1–4):509–519Google Scholar
  32. 32.
    Masood SH, Mau K, Song WQ (2010) Tensile properties of processed FDM polycarbonate material. Mater Sci Forum 654:2556–2559CrossRefGoogle Scholar
  33. 33.
    Arivazhagan A, Masood SH, Sbarski I (2011) Dynamic mechanical analysis of FDM rapid prototyping processed polycarbonate material. In: Proceedings of the 69th annual technical conference of the society of plastics engineers 2011 (ANTEC 2011), vol 1. Boston, Massachusetts, United States, 1–5 May 2011, pp 950–955Google Scholar
  34. 34.
    Arivazhagan A, Masood SH (2012) Dynamic mechanical properties of ABS material processed by fused deposition modelling. Int J Eng Res Appl 2(3):2009–2014Google Scholar
  35. 35.
    Jami H, Masood SH, Song WQ (2013) Dynamic response of FDM made ABS parts in different part orientations. Adv Mater Res 748:291–294CrossRefGoogle Scholar
  36. 36.
    Peace GS (1993) Taguchi methods, a hands-on approach. Addison-Wesley Publishing Company, Reading, MAGoogle Scholar
  37. 37.
    Roy RK (2010) A primer on the Taguchi method. Society of Manufacturing Engineers, DearbornGoogle Scholar
  38. 38.
    Montgomery DC (2008) Design and analysis of experiments. Wiley, New JerseyGoogle Scholar
  39. 39.
    Wu CJ, Hamada MS (2001) Experiments: planning, analysis, and parameter design optimization. Wiley, New JerseyGoogle Scholar
  40. 40.
    Medsker L, Jain LC (1999) Recurrent neural networks: design and applications. CRC Press, Boca RatonGoogle Scholar
  41. 41.
    Haykin S (1999) Neural networks: a comprehensive foundation. Prentice-Hall Inc., New JerseyzbMATHGoogle Scholar
  42. 42.
    Correia DS, Gonçalves CV (2005) Comparison between genetic algorithms and response surface methodology in GMAW welding optimization. J Mater Process Technol 160(1):70–76CrossRefGoogle Scholar

Copyright information

© Shanghai University and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Omar A. Mohamed
    • 1
    Email author
  • Syed H. Masood
    • 1
  • Jahar L. Bhowmik
    • 2
  1. 1.Department of Mechanical and Product Design Engineering, Faculty of Science, Engineering and TechnologySwinburne University of TechnologyHawthornAustralia
  2. 2.Department of Psychological Sciences and Statistics, Faculty of Health, Arts and DesignSwinburne University of TechnologyHawthornAustralia

Personalised recommendations