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

Hossein Mohammad Khanlou, Ali Sadollah, Bee Chin Ang, Joong Hoon Kim, Sepehr Talebian, Azadeh Ghadimi

Research output: Contribution to journalArticle

39 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalNeural Computing and Applications
DOIs
Publication statusAccepted/In press - 2014 Jan 30

Keywords

  • Artificial neural networks
  • Electrospinning parameters
  • Nanofibers
  • Polymethyl methacrylate (PMMA)
  • Response surface methodology

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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