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

Omar A. Mohamed, Syed H. Masood, Jahar L. Bhowmik

Advances in Manufacturing ›› 2015, Vol. 3 ›› Issue (1) : 42-53.

Advances in Manufacturing ›› 2015, Vol. 3 ›› Issue (1) : 42-53. DOI: 10.1007/s40436-014-0097-7
Article

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

Author information +
History +

Abstract

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.

Keywords

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

Cite this article

Download citation ▾
Omar A. Mohamed, Syed H. Masood, Jahar L. Bhowmik. Optimization of fused deposition modeling process parameters: a review of current research and future prospects. Advances in Manufacturing, 2015, 3(1): 42‒53 https://doi.org/10.1007/s40436-014-0097-7

References

[1.]
Gebhardt A. Rapid prototyping, 2003, Munich: Hanser.
CrossRef Google scholar
[2.]
Gibson I, Rosen DW, Stucker B. Additive manufacturing technologies, 2010, Heidelberg: Springer.
CrossRef Google scholar
[3.]
Kai CC, Fai LK, Chu-Sing L. Rapid prototyping: principles and applications in manufacturing, 2003, Singapore: World Scientific Publishing Co. Pte. Ltd.
[4.]
Upcraft S, Fletcher R. The rapid prototyping technologies. Assem Autom, 2003, 23(4): 318-330.
CrossRef Google scholar
[5.]
Mansour S, Hague R. Impact of rapid manufacturing on design for manufacture for injection moulding. Proc Inst Mech Eng Part B, 2003, 217(4): 453-461.
CrossRef Google scholar
[6.]
Hopkinson N, Hague R, Dickens P. Rapid manufacturing: an industrial revolution for the digital age, 2006, New Jersey: Wiley.
CrossRef Google scholar
[7.]
Bernard A, Fischer A. New trends in rapid product development. CIRP Ann Manuf Technol, 2002, 51(2): 635-652.
CrossRef Google scholar
[8.]
Gebhardt A. Understanding additive manufacturing, 2012, Munich: Carl Hanser Verlag GmbH & Co. KG.
CrossRef Google scholar
[9.]
Kai CC, Fai LK, Chu-Sing L (2010) Rapid prototyping: principles and applications. World Scientific Publishing Co. Pte. Ltd., Singapore
[10.]
Noorani R. Rapid prototyping: principles and applications, 2006, New Jersey: Wiley
[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, USA
[12.]
Masood SH. Intelligent rapid prototyping with fused deposition modelling. Rapid Prototyp J, 1996, 2(1): 24-33.
CrossRef Google scholar
[13.]
Groza JR, Shackelford JF. Materials processing handbook, 2010, Boca Raton: CRC Press
[14.]
Anitha R, Arunachalam S, Radhakrishnan P. Critical parameters influencing the quality of prototypes in fused deposition modelling. J Mater Process Technol, 2001, 118(1–3): 385-388.
CrossRef Google scholar
[15.]
Nancharaiah T, Raju DR, Raju VR. An experimental investigation on surface quality and dimensional accuracy of FDM components. Int J Emerg Technol, 2010, 1(2): 106-111.
[16.]
Thrimurthulu K, Pandey PM, Reddy NV. Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf, 2004, 44(6): 585-594.
CrossRef Google scholar
[17.]
Horvath D, Noorani R, Mendelson M. Improvement of surface roughness on ABS 400 polymer using design of experiments (DOE). Mater Sci Forum, 2007, 561: 2389-2392.
CrossRef Google scholar
[18.]
Wang CC, Lin TW, Hu SS. Optimizing the rapid prototyping process by integrating the Taguchi method with the gray relational analysis. Rapid Prototyp J, 2007, 13(5): 304-315.
CrossRef Google scholar
[19.]
Sood AK, Ohdar R, Mahapatra S. Improving dimensional accuracy of fused deposition modelling processed part using grey Taguchi method. Mater Des, 2009, 30(10): 4243-4252.
CrossRef Google scholar
[20.]
Zhang JW, Peng AH. Process-parameter optimization for fused deposition modeling based on Taguchi method. Adv Mater Res, 2012, 538: 444-447.
CrossRef Google scholar
[21.]
Sahu RK, Mahapatra S, Sood AK. A study on dimensional accuracy of fused deposition modeling (FDM) processed parts using fuzzy logic. J Manuf Sci Prod, 2013, 13(3): 183-197.
[22.]
Lee B, Abdullah J, Khan Z. Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol, 2005, 169(1): 54-61.
CrossRef Google scholar
[23.]
Laeng J, Khan ZA, Khu SY. Optimizing flexible behaviour of bow prototype using Taguchi approach. J Appl Sci, 2006, 6: 622-630.
CrossRef Google scholar
[24.]
Zhang Y, Chou K. A parametric study of part distortions in fused deposition modelling using three-dimensional finite element analysis. Proc Inst Mech Eng Part B, 2008, 222(8): 959-968.
CrossRef Google scholar
[25.]
Nancharaiah T (2011) Optimization of process parameters in FDM process using design of experiments. Int J Emerg Technol 2(1):100–102
[26.]
Kumar GP, Regalla SP. Optimization of support material and build time in fused deposition modeling (FDM). Appl Mech Mater, 2012, 110: 2245-2251.
[27.]
Ahn SH, Montero M, Odell D, et al. Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyp J, 2002, 8(4): 248-257.
CrossRef Google scholar
[28.]
Ang KC, Leong KF, Chua CK, et al. Investigation of the mechanical properties and porosity relationships in fused deposition modelling-fabricated porous structures. Rapid Prototyp J, 2006, 12(2): 100-105.
CrossRef Google scholar
[29.]
Sood AK, Ohdar RK, Mahapatra S. Parametric appraisal of mechanical property of fused deposition modelling processed parts. Mater Des, 2010, 31(1): 287-295.
CrossRef Google scholar
[30.]
Percoco G, Lavecchia F, Galantucci LM. Compressive properties of FDM rapid prototypes treated with a low cost chemical finishing. Res J Appl Sci Eng Technol, 2012, 4(19): 3838-3842.
[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):509–519
[32.]
Masood SH, Mau K, Song WQ. Tensile properties of processed FDM polycarbonate material. Mater Sci Forum, 2010, 654: 2556-2559.
CrossRef Google scholar
[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 950–955
[34.]
Arivazhagan A, Masood SH. Dynamic mechanical properties of ABS material processed by fused deposition modelling. Int J Eng Res Appl, 2012, 2(3): 2009-2014.
[35.]
Jami H, Masood SH, Song WQ. Dynamic response of FDM made ABS parts in different part orientations. Adv Mater Res, 2013, 748: 291-294.
CrossRef Google scholar
[36.]
Peace GS. Taguchi methods, a hands-on approach, 1993, Reading, MA: Addison-Wesley Publishing Company
[37.]
Roy RK. A primer on the Taguchi method, 2010, Dearborn: Society of Manufacturing Engineers
[38.]
Montgomery DC. Design and analysis of experiments, 2008, New Jersey: Wiley
[39.]
Wu CJ, Hamada MS. Experiments: planning, analysis, and parameter design optimization, 2001, New Jersey: Wiley
[40.]
Medsker L, Jain LC. Recurrent neural networks: design and applications, 1999, Boca Raton: CRC Press
[41.]
Haykin S. Neural networks: a comprehensive foundation, 1999, New Jersey: Prentice-Hall Inc..
[42.]
Correia DS, Gonçalves CV. Comparison between genetic algorithms and response surface methodology in GMAW welding optimization. J Mater Process Technol, 2005, 160(1): 70-76.
CrossRef Google scholar

Accesses

Citations

Detail

Sections
Recommended

/