Skip to main content
SpringerLink
Log in

Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Fused deposition modelling (FDM) is the most extended additive manufacturing technique up to date. In FDM, a thermoplastic material is extruded through a nozzle to form layers, and the final geometry is the result of consecutive superimposed layers. However, it is difficult to obtain an adequate dimensional accuracy for some applications due to the characteristics of the process. This paper proposes a method for increasing accuracy of the distance between parallel faces on FDM manufactured prismatic parts, consisting in replacing the theoretical values of CAD model dimensions by new optimized values. For this purpose, a model has been developed for predicting the dimensions of the manufactured parts, based on design characteristics. Particularly, this work has used an artificial neural network combined with an optimization algorithm, to determine the optimal dimensional values for the CAD model. Subsequently, CAD model is redesigned according to the dimensions provided by the optimization algorithm, and the part is manufactured. The results show that the application of this methodology allows for a reduction in manufacturing error of approximately 50 % for external dimensions and 30 % for internal dimensions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. ASTM Standard F2792-12a (2012) Standard terminology for additive manufacturing technologies, ASTM International, West Conshohocken, PA, doi:10.1520/F2792-12A, www.astm.org

  2. Wholers TT (2011) Wohlers report 2011: additive manufacturing and 3D printing state of the Industry Annual Worldwide Progress Report. Wohlers Associates, Fort Collins, CO

    Google Scholar 

  3. Campbell T, Williams C, Ivanova O, Garrett B (2011) Could 3D printing change the world? Atlantic Council of the United States. http://www.acus.org/files/publication_pdfs/403/101711_ACUS_3DPrinting.PDF. Accessed 13 June 2012

  4. Too MH, Leong KF, Chua CK, Du ZH, Yang SF, Cheah CM, Ho SL (2002) Investigation of 3D non-random porous structures by fused deposition modelling. Int J Adv Manuf Technol 19(3):217–223

    Google Scholar 

  5. 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):385–388

    Article  Google Scholar 

  6. Lee CS, Kim SG, Kim HJ, Ahn SH (2007) Measurement of anisotropic compressive strength of rapid prototyping parts. J Mater Process Technol 187–188:627–630

    Article  Google Scholar 

  7. Chang DY, Huang BH (2011) Studies on profile error and extruding aperture for the RP parts using the fused deposition modeling process. Int J Adv Manuf Technol 53(9–12):1027–1037

    Article  Google Scholar 

  8. Brajlih T, Valentan B, Balic J, Drstvensek I (2011) Speed and accuracy evaluation of additive manufacturing machines. Rapid Prototyp J 17(1):64–75

    Article  Google Scholar 

  9. El-Katatny I, Masood SH, Morsi YS (2010) Error analysis of FDM fabricated medical replicas. Rapid Prototyp J 16(1):36–43

    Article  Google Scholar 

  10. Masood SH, Rattanawong W, Iovenitti P (2000) Part build orientations based on volumetric error in fused deposition modeling. Int J Adv Manuf Technol 16(3):162–168

    Article  Google Scholar 

  11. Rattanawong W, Masood SH, Iovenitti P (2001) A volumetric approach to part-build orientations in rapid prototyping. J Mater Process Technol 119(1–3):348–353

    Article  Google Scholar 

  12. Thrimurthulu K, Pandey PM, Reddy NV (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf 44(6):585–594

    Article  Google Scholar 

  13. Lee BH, Abdullah J, Khan ZA (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169(1):54–61

    Article  Google Scholar 

  14. Kumar VV, Tagore GRN, Venugopal A (2011) Some investigations on geometric conformity analysis of a 3-D freeform objects produced by rapid prototyping (FDM) process. Int J Appl Res Mech Eng 1(2):82–86

    Google Scholar 

  15. Tong K, Joshi S, Lehtinet EA (2008) Error compensation for fused deposition modeling (FDM) machine by correcting slice files. Rapid Prototyp J 14(1):4–14

    Article  Google Scholar 

  16. Sood AK (2011) Study on parametric optimization of used deposition modelling (FDM) process. Ph.D. Thesis. National Institute of Technology, Rourkela, India. http://ethesis.nitrkl.ac.in/3004/1/Thesis_anoop_kumar_sood_-_507me012.pdf. Accessed 9 July 2012

  17. Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modeling processed part using grey Taguchi method. Mater & Des 30(10):4243–4252

    Article  Google Scholar 

  18. Sood AK, Ohdar RK, Mahapatra SS (2012) Experimental investigation and empirical modeling of FDM process for compressive strength improvement. J Adv Res 3(1):81–90

    Article  Google Scholar 

  19. ASME Y14.5–2009 (2009) Dimensioning and tolerancing. The American Society of Mechanical Engineers National Standard. The American Society of Mechanical Engineers, New York

    Google Scholar 

  20. ISO Standard 10360–2 (2009) Geometrical product specifications (GPS)—acceptance and reverification tests for coordinate measuring machines (CMM)—part 2: CMMs used for measuring linear dimensions. International Organization for Standardization, Geneva, Switzerland

    Google Scholar 

  21. Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7(4):308–313

    Article  MATH  Google Scholar 

  22. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the Nelder-Mead simplex method in low dimensions. SIAM J Optim 9(1):112–147

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Construction and Manufacturing Department, University of Oviedo, 33203, Gijon, Asturias, Spain

    A. Noriega, D. Blanco, B. J. Alvarez & A. Garcia

Authors
  1. A. Noriega
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. Blanco
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. J. Alvarez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. A. Garcia
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. Blanco.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Noriega, A., Blanco, D., Alvarez, B.J. et al. Dimensional accuracy improvement of FDM square cross-section parts using artificial neural networks and an optimization algorithm. Int J Adv Manuf Technol 69, 2301–2313 (2013). https://doi.org/10.1007/s00170-013-5196-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-013-5196-2

Keywords

Search

Navigation