Abstract
This study focuses on the design of a Neural Network (NN) model for the prediction of interpolated values of polyvinylacetate (PVAc) nanofiber diameters produced by the electrospinning process and it supposes to be a preliminary work for future and industrial applications. The experimental data gathered from the literature form the basis for generating a more consistent sample through standard interpolation. The inputs of the NN are the polymer concentration, the applied voltage, the nozzle-collector distance and the flow rate parameters of the process, whereas the average diameter acts as the unique output of the network. The generated model is able to approximate the mapping between process parameters and fiber morphology, which is of practical importance to help prepare homogeneous nano-fibers. The reliability of the model was tested by 7-fold cross validation as well as leave-one-out method, showing good performance in terms of both average RMSE (0.109, corresponding to 138.51 nm) and correlation coefficient (0.905) between the desired and the predicted diameters when a White Gaussian Noise with 2% power (WGN2%) is applied to the interpolations.
Keywords
- Neural networks
- Electrospinning
- PVAc
- Nanomaterials
- Material informatics
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Huang, Z.-M., et al.: A review on polymer nanofibers by electrospinning and their applications in nanocomposites. Compos. Sci. Technol. 63(15), 2223–2253 (2003)
Persano, L., et al.: Industrial upscaling of electrospinning and applications of polymer nanofibers: a review. Macromol. Mater. Eng. 298(5), 504–520 (2013)
Pantò, F., Fan, Y., Frontera, P., Stelitano, S., Fazio, E., Patanè, S., Santangelo, S.: Are electrospun carbon/metal oxide composite fibers relevant electrodematerials for li-ion batteries? J. Electrochem. Soc. 163(14), A2930–A2937 (2016)
Haykin, S.: Neural networks: a comprehensive foundation. Neural Netw. 2(2004), 41 (2004)
Carrera, D., et al.: Defect detection in SEM images of nanofibrous materials. IEEE Trans. Ind. Inform. 13(2), 551–561 (2017)
Borrotti, M., et al.: Defect minimization and feature control in electrospinning through design of experiments. J. Appl. Polym. Sci. 134(17), 44740(1 of 10), 44740(2 of 10), .., 44740(10 of 10) (2017)
Sarkar, K., et al.: A neural network model for the numerical prediction of the diameter of electro-spun polyethylene oxide nanofibers. J. Mater. Process. Technol. 209(7), 3156–3165 (2009)
Mirzaei, E., et al.: Artificial neural networks modeling of electrospinning of polyethylene oxide from aqueous acid acetic solution. J. Appl. Polym. Sci. 125(3), 1910–1921 (2012)
Faridi-Majidi, R., et al.: Use of artificial neural networks to determine parameters controlling the nanofibers diameter in electrospinning of nylon-6, 6. J. Appl. Polym. Sci. 124(2), 1589–1597 (2012)
Naghibzadeh, M., Adabi, M.: Evaluation of effective electrospinning parameters controlling gelatin nanofibers diameter via modelling artificial neural networks. Fibers Polym. 15(4), 767–777 (2014)
Vatankhah, E., et al.: Artificial neural network for modeling the elastic modulus of electrospun polycaprolactone/gelatin scaffolds. Acta biomaterialia 10(2), 709–721 (2014)
Pham, Q.P., Sharma, U., Mikos, A.G.: Electrospinning of polymeric nanofibers for tissue engineering applications: a review. Tissue Eng. 12(5), 1197–1211 (2006)
Karimi, M.A., et al.: Using an artificial neural network for the evaluation of the parameters controlling PVA/chitosan electrospun nanofibers diameter. e-Polym. 15(2), 127–138 (2015)
Ketabchi, N., et al.: Preparation and optimization of chitosan/polyethylene oxide nanofiber diameter using artificial neural networks. Neural Comput. Appl. 1–13 (2016). https://link.springer.com/article/10.1007/s00521-016-2212-0
Brooks, H., Tucker, N.: Electrospinning predictions using artificial neural networks. Polymer 58, 22–29 (2015)
Nasouri, K., Shoushtari, A.M., Khamforoush, M.: Comparison between artificial neural network and response surface methodology in the prediction of the production rate of polyacrylonitrile electrospun nanofibers. Fibers Polym. 14(11), 1849–1856 (2013)
Nasouri, K., et al.: Modeling and optimization of electrospun PAN nanofiber diameter using response surface methodology and artificial neural networks. J. Appl. Polym. Sci. 126(1), 127–135 (2012)
Khanlou, H.M., et al.: Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks. Neural Comput. Appl. 25(3–4), 767–777 (2014)
Rabbi, A., et al.: RSM and ANN approaches for modeling and optimizing of electrospun polyurethane nanofibers morphology. Fibers Polym. 13(8), 1007–1014 (2012)
Nateri, A.S., Hasanzadeh, M.: Using fuzzy-logic and neural network techniques to evaluating polyacrylonitrile nanofiber diameter. J. Comput. Theor. Nanosci. 6(7), 1542–1545 (2009)
Son, W.K., et al.: The effects of solution properties and polyelectrolyte on electrospinning of ultrafine poly (ethylene oxide) fibers. Polymer 45(9), 2959–2966 (2004)
Yördem, O.S., Papila, M., Menceloğlu, Y.Z.: Effects of electrospinning parameters on polyacrylonitrile nanofiber diameter: an investigation by response surface methodology. Mater. Des. 29(1), 34–44 (2008)
Ojha, S.S., et al.: Morphology of electrospun nylon-6 nanofibers as a function of molecular weight and processing parameters. J. Appl. Polym. Sci. 108(1), 308–319 (2008)
Park, J.Y., Lee, I.H., Bea, G.N.: Optimization of the electrospinning conditions for preparation of nanofibers from polyvinylacetate (PVAc) in ethanol solvent. J. Ind. Eng. Chem. 14(6), 707–713 (2008)
Garg, K., Bowlin, G.L.: Electrospinning jets and nanofibrous structures. Biomicrofluidics 5(1), 013403 (2011)
Ramakrishna, S.: An Introduction to Electrospinning and Nanofibers. World Scientific, Singapore (2005)
Chattopadhyay, R., Guha, A.: Artificialneural networks: applications to textiles. Textile Progress 35(1), 1–46 (2004)
Morabito, F.C.: Independent component analysis and feature extraction techniques for NDT data. Mater. Eval. 58(1), 85–92 (2000)
Refaeilzadeh, P., Tang, L., Liu, H.: Cross-validation. In: Liu, L., Özsu, M.T. (eds.) Encyclopedia of Database Systems, pp. 532–538. Springer, Heidelberg (2009). doi:10.1007/978-0-387-39940-9_565
Steyerberg, E.W., et al.: Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J. Clin. Epidemiol. 54(8), 774–781 (2001)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ieracitano, C., Pantò, F., Frontera, P., Morabito, F.C. (2017). A Neural Network Approach for Predicting the Diameters of Electrospun Polyvinylacetate (PVAc) Nanofibers. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-65172-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65171-2
Online ISBN: 978-3-319-65172-9
eBook Packages: Computer ScienceComputer Science (R0)