Skip to main content
SpringerLink
Log in

Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Since the fiber diameter determines the mechanical, electrical, and optical properties of electrospun nanofiber mats, the effect of material and process parameters on electrospun polymethyl methacrylate (PMMA) fiber diameter were studied. Accordingly, the prediction and optimization of input factors were performed using the response surface methodology (RSM) with the design of experiments technique and artificial neural networks (ANNs). A central composite design of RSM was employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the response factor, namely the PMMA fiber diameter (in nm). The parameters studied were polymer concentration (13–28 wt%), feed rate (1–5 mL/h), and tip-to-collector distance (10–23 cm). From the analysis of variance, the most significant factor that caused a remarkable impact on the experimental design response was identified. The predicted responses using the RSM and ANNs were compared in figures and tables. In general, the ANNs outperformed the RSM in terms of accuracy and prediction of obtained results.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Huang Z-M, Zhang YZ, Kotaki M, Ramakrishna S (2003) A review on polymer nanofibers by electrospinning and their applications in nanocomposites. Compos Sci Technol 63(15):2223–2253

    Article  Google Scholar 

  2. Persano L, Camposeo A, Tekmen C, Pisignano D (2013) Industrial upscaling of electrospinning and applications of polymer nanofibers: a review. Macromol Mater Eng 298(5):504–520

    Article  Google Scholar 

  3. Jia L, Prabhakaran MP, Qin X, Kai D, Ramakrishna S (2013) Biocompatibility evaluation of protein-incorporated electrospun polyurethane-based scaffolds with smooth muscle cells for vascular tissue engineering. J Mater Sci 48(15):5113–5124

    Article  Google Scholar 

  4. Kameoka J, Verbridge SS, Liu H, Czaplewski DA, Craighead H (2004) Fabrication of suspended silica glass nanofibers from polymeric materials using a scanned electrospinning source. Nano Lett 4(11):2105–2108

    Article  Google Scholar 

  5. Pawlowski K, Belvin H, Raney D, Su J, Harrison J, Siochi E (2003) Electrospinning of a micro-air vehicle wing skin. Polymer 44(4):1309–1314

    Article  Google Scholar 

  6. Ma M, Hill RM (2006) Superhydrophobic surfaces. Curr Opin Colloid Interface Sci 11(4):193–202

    Article  Google Scholar 

  7. Reneker DH, Chun I (1996) Nanometre diameter fibres of polymer, produced by electrospinning. Nanotechnology 7(3):216

    Article  Google Scholar 

  8. Agarwal S, Greiner A, Wendorff JH (2013) Functional materials by electrospinning of polymers. Prog Polym Sci 38(6):963–991

    Article  Google Scholar 

  9. Hohman MM, Shin M, Rutledge G, Brenner MP (2001) Electrospinning and electrically forced jets. I. Stability theory. Phys Fluids 13:2201

    MathSciNet  Google Scholar 

  10. Reneker DH, Yarin AL (2008) Electrospinning jets and polymer nanofibers. Polymer 49(10):2387–2425

    Article  Google Scholar 

  11. Thompson C, Chase G, Yarin A, Reneker D (2007) Effects of parameters on nanofiber diameter determined from electrospinning model. Polymer 48(23):6913–6922

    Article  Google Scholar 

  12. Tanio N, Koike Y (2000) What is the most transparent polymer? Fiber Optics Wkly Update

  13. Piperno S, Lozzi L, Rastelli R, Passacantando M, Santucci S (2006) PMMA nanofibers production by electrospinning. Appl Surf Sci 252(15):5583–5586

    Article  Google Scholar 

  14. Qian Y, Su Y, Li X, Wang H, He C (2010) Electrospinning of polymethyl methacrylate nanofibres in different solvents. Iran Polym J 19(2):123

    Google Scholar 

  15. Wang H, Liu Q, Yang Q, Li Y, Wang W, Sun L, Zhang C, Li Y (2010) Electrospun poly (methyl methacrylate) nanofibers and microparticles. J Mater Sci 45(4):1032–1038

    Article  Google Scholar 

  16. Sánchez N, Martínez M, Aracil J (1997) Selective esterification of glycerine to 1-glycerol monooleate. 2. Optimization studies. Ind Eng Chem Res 36(5):1529–1534

    Article  Google Scholar 

  17. Box GE, Draper NR (1987) Empirical model-building and response surfaces. Wiley, New Jersey

    MATH  Google Scholar 

  18. Indira V, Anjana R, George KE (2011) Preparation and characterization of PP/HDPE/NANOCLAY/SHORT fiber hybrid composites using response surface methodology. Global J of Engg Appl Sci 1(4):88–91

    Google Scholar 

  19. Low KL, Tan SH, Zein SHS, McPhail DS, Boccaccini AR (2011) Optimization of the mechanical properties of calcium phosphate/multi-walled carbon nanotubes/bovine serum albumin composites using response surface methodology. Mater Des 32(6):3312–3319

    Article  Google Scholar 

  20. Hassoun MH (1995) Fundamentals of artificial neural networks. MIT press, Cambridge

    MATH  Google Scholar 

  21. Aleksander I, Morton H (1990) An introduction to neural computing, vol 240. Chapman and Hall, London

    Google Scholar 

  22. Sha W, Edwards K (2007) The use of artificial neural networks in materials science based research. Mater Des 28(6):1747–1752

    Article  Google Scholar 

  23. Hassan AM, Alrashdan A, Hayajneh MT, Mayyas AT (2009) Prediction of density, porosity and hardness in aluminum–copper-based composite materials using artificial neural network. J Mater Process Tech 209(2):894–899

    Article  Google Scholar 

  24. Xiao G, Zhu Z (2010) Friction materials development by using DOE/RSM and artificial neural network. Tribol Int 43(1):218–227

    Article  Google Scholar 

  25. Singh R, Bhoopal R, Kumar S (2011) Prediction of effective thermal conductivity of moist porous materials using artificial neural network approach. Build Environ 46(12):2603–2608

    Article  Google Scholar 

  26. Sumpter BG, Noid DW (1996) On the design, analysis, and characterization of materials using computational neural networks. Annu Rev Mater Sci 26(1):223–277

    Article  Google Scholar 

  27. Giri Dev VR, Venugopal JR, Senthilkumar M, Gupta D, Ramakrishna S (2009) Prediction of water retention capacity of hydrolysed electrospun polyacrylonitrile fibers using statistical model and artificial neural network. J Appl Polym Sci 113(5):3397–3404

    Article  Google Scholar 

  28. Li Y, Bridgwater J (2000) Prediction of extrusion pressure using an artificial neural network. Powder Technol 108(1):65–73

    Article  Google Scholar 

  29. Hinton GE (1992) How neural networks learn from experience. Sci Am 267(3):145–151

    Article  Google Scholar 

  30. Widrow B, Lehr MA (1993) Adaptive neural networks and their applications. Int J Intell Syst 8(4):453–507

    Article  MATH  Google Scholar 

  31. Hecht-Nielsen R (1989) Theory of the backpropagation neural network. Neural Netw IEEE IJCNN 1:593–605

    Google Scholar 

  32. Basheer I, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Meth 43(1):3–31

    Article  Google Scholar 

  33. Demuth H, Beale M (2000) Neural network toolbox user’s guide. The MathWorks, Inc, Natick

    Google Scholar 

  34. Adeli H (2001) Neural networks in civil engineering: 1989–2000. Comput-Aided Civ Inf 16(2):126–142

    Article  Google Scholar 

  35. Yu J, Wu B (2009) The inverse of material properties of functionally graded pipes using the dispersion of guided waves and an artificial neural network. NDT and E Int 42(5):452–458

    Article  MathSciNet  Google Scholar 

  36. Hecht-Nielsen R (1988) Neurocomputer applications. Neural Comput 41:445–453

    Google Scholar 

  37. Lee T, Jeng DS (2002) Application of artificial neural networks in tide-forecasting. Ocean Eng 29(9):1003–1022

    Article  Google Scholar 

  38. Hagan MT, Demuth HB, Beale MH (1996) Neural network design. Pws Pub, Boston

    Google Scholar 

  39. Gupta P, Elkins C, Long TE, Wilkes GL (2005) Electrospinning of linear homopolymers of poly (methyl methacrylate): exploring relationships between fiber formation, viscosity, molecular weight and concentration in a good solvent. Polymer 46(13):4799–4810

    Article  Google Scholar 

  40. Macossay J, Marruffo A, Rincon R, Eubanks T, Kuang A (2007) Effect of needle diameter on nanofiber diameter and thermal properties of electrospun poly (methyl methacrylate). Polym Adv Technol 18(3):180–183

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2013R1A2A1A01013886) and the University of Malaya, grant No. RP022C-13AET.

Author information

Authors and Affiliations

  1. Center of Advanced Materials, Department of Mechanical Engineering, University of Malaya, Kuala Lumpur, 50603, Malaysia

    Hossein Mohammad Khanlou, Bee Chin Ang & Sepehr Talebian

  2. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 136-713, South Korea

    Ali Sadollah & Joong Hoon Kim

  3. Department of Mechanical Engineering, School of Engineering, Taylor’s University, Kuala Lumpur, Malaysia

    Azadeh Ghadimi

Authors
  1. Hossein Mohammad Khanlou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Sadollah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bee Chin Ang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Joong Hoon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sepehr Talebian
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Azadeh Ghadimi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joong Hoon Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Khanlou, H.M., Sadollah, A., Ang, B.C. et al. Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. Neural Comput & Applic 25, 767–777 (2014). https://doi.org/10.1007/s00521-014-1554-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1554-8

Keywords

Search

Navigation