Advertisement

PCA-based desirability method for dimensional improvement of part extruded by fused deposition modelling technology

  • Azhar Equbal
  • Md. Israr Equbal
  • Anoop Kumar Sood
Full Research Article
  • 18 Downloads

Abstract

Fused deposition modelling (FDM) is an extrusion-based additive manufacturing technique that has the ability to build complicated geometry of parts in least possible time without any tooling problem. Extrusion-based methods offer various advantages but the part quality of fabricated part is poorer when measured in terms of dimensional accuracy. Quality of its fabricated part primarily depends on processing parameters like raster angle, air gap, layer thickness, etc. For improving the part accuracy, present work is aimed at optimization of FDM processing parameters. Response surface methodology-based face-centered central composite design is used for designing the experimental matrix and also to reduce the number of experimental runs. Analysis of variance is used to study the effects of processing parameters on responses. Empirical model relating the parameters and responses is also developed. The suitability of developed model is tested using Anderson–darling normality test. Dimensional measurement shows that measured dimensions of fabricated part are always more than CAD model. Restriction of shrinkage during part fabrication causes oversize dimension of part. Besides, chosen processing parameters is the major reason for dimensional inaccuracy of the fabricated part. Weighted principal component analysis (WPCA)-based desirability function method is used as a hybrid approach to find the optimal parameter setting for part fabrication with minimum overall deviations in dimension. Optimization process suggests that part fabrication should be done at 30° raster angle, − 0.004 mm air gap and 0.4064 mm raster width for minimal relative changes in length (∆L), width (∆W) and thickness (∆T).

Keywords

FDM Face centered Central composite design Analysis of variance Raster angle Air gap 

Notes

References

  1. 1.
    Peng A, Xiao X, Yue R (2014) Process parameter optimization for fused deposition modeling using response surface methodology combined with fuzzy inference system. Int J Adv Manuf Technol 73:87–100CrossRefGoogle Scholar
  2. 2.
    Piotr K (2016) A Review of fused deposition modeling process models, In: Rusiński E., Pietrusiak D. (eds) Proceedings of the 13th International scientific conference. RESRB 2016. Lecture notes in mechanical engineering. Springer, ChamGoogle Scholar
  3. 3.
    Mohamed O, 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
  4. 4.
    Kai C, Fai LK (1997) Rapid prototyping: principles and applications in manufacturing. Wiley, SingaporeGoogle Scholar
  5. 5.
    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
  6. 6.
    Onwubolu GC, Rayegani F (2014) Characterization and optimization of mechanical properties of ABS parts manufactured by the fused deposition modelling process, Int J Manuf Eng 5:1–13Google Scholar
  7. 7.
    IDurgun, Ertan R (2014) Experimental investigation of FDM process for improvement of mechanical properties and production cost. Rapid Prototyp J 20:228–235CrossRefGoogle Scholar
  8. 8.
    Ahn SH, Odell D, Roundy S, Wright PK (2002) Anisotropic material properties of fused deposition modeling ABS. Rapid Prototyp J 8(4):248–257CrossRefGoogle Scholar
  9. 9.
    Pandey PM, Thrimurthulu K, Venkata N, Reddy (2004) Optimal part deposition orientation in FDM by using a multicriteria genetic algorithm. Int J Prod Res 42(19):4069–4089CrossRefzbMATHGoogle Scholar
  10. 10.
    Thrimurthulu K, Pandey PM, Venkata N, Reddy (2004) Optimum part deposition orientation in fused deposition modeling. Int J Mach Tools Manuf 44(6):585–594CrossRefzbMATHGoogle Scholar
  11. 11.
    Sood AK, Ohdar RK, Mahapatra SS (2009) Improving dimensional accuracy of fused deposition modeling processed part using grey Taguchi method. Mater Des 30:4243–4252CrossRefGoogle Scholar
  12. 12.
    Sahu RK, Mahapatra SS, Sood AK (2013) A study on dimensional accuracy of fused deposition modelling (FDM) processed parts using fuzzy logic. J Manuf Sci Prod 13(3):183–197Google Scholar
  13. 13.
    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):509–519CrossRefGoogle Scholar
  14. 14.
    Salmi M, Ituarte IF, Chekurov S, Huotilainen E (2016) Effect of build orientation in 3D printing production for material extrusion, material jetting, binder jetting, sheet object lamination, vat photopolymerisation, and powder bed fusion. Int J Collab Enterp 5(3–4):218–231CrossRefGoogle Scholar
  15. 15.
    Basavaraj CK, Vishwas M (2016) Studies on effect of fused deposition modelling process parameters on ultimate tensile strength and dimensional accuracy of Nylon, IOP Conf. Mater Sci Eng 149:1–11Google Scholar
  16. 16.
    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
  17. 17.
    Lee BH, Abdullah J, Khan ZA (2005) Optimization of rapid prototyping parameters for production of flexible ABS object. J Mater Process Technol 169:54–61CrossRefGoogle Scholar
  18. 18.
    Equbal A, Sood AK, Ansari AR, Equbal MdA (2017) Optimization of process parameters of FDM part for minimiizing its dimensional inaccuracy. Int J Mech Prod Eng Res Dev 7(2):57–66Google Scholar
  19. 19.
    Panda S, Padhee S, Sood AK, Mahapatra SS (2009) Optimization of fused deposition modelling (FDM) process parameters using bacterial foraging technique. Intell Inf Manag 2:89–97Google Scholar
  20. 20.
    Routara BC, Mohanty SD, Datta S, Bandyopadhyay A, Mahapatra SS (2010) Combined quality loss (CQL) concept in WPCA-based Taguchi philosophy for optimization of multiple surface quality characteristics of UNS C34000 brass in cylindrical grinding. Int J Adv Manuf Technol 51:135–143CrossRefGoogle Scholar
  21. 21.
    Costa NR, Lourenço J, Pereira ZL (2011) Desirability function approach: a review and performance evaluation in adverse conditions. Chemom Intell Lab Syst 107:234–244CrossRefGoogle Scholar
  22. 22.
    Hattiangadi A, Bandyopadhyay A (2000) Modeling of multiple pore ceramic materials fabricated via fused deposition process. Scr Mater 42:581–588CrossRefGoogle Scholar
  23. 23.
    Jeff Wu CF, Hamada M (2002) Experiments: planning, analysis, and parameter design optimization. Wiley, New DelhizbMATHGoogle Scholar
  24. 24.
    Montgomery DC (2003) Design and analysis of experiments. Wiley, SingaporeGoogle Scholar
  25. 25.
    Turner BN, Strong R, Gold SA (2014) A review of melt extrusion additive manufacturing processes: I. Process design and modelling. Rapid Prototyp J 20(3):192–204CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Azhar Equbal
    • 1
  • Md. Israr Equbal
    • 2
  • Anoop Kumar Sood
    • 3
  1. 1.Department of Mechanical EngineeringRTC Institute of TechnologyRanchiIndia
  2. 2.Department of Mechanical EngineeringJB Institute of Engineering and TechnologyHyderabadIndia
  3. 3.Department of Manufacturing EngineeringNational Institute of Foundry and Forge TechnologyRanchiIndia

Personalised recommendations