TY - JOUR
T1 - Prediction and optimization of electrospinning parameters for polymethyl methacrylate nanofiber fabrication using response surface methodology and artificial neural networks
AU - Khanlou, Hossein Mohammad
AU - Sadollah, Ali
AU - Ang, Bee Chin
AU - Kim, Joong Hoon
AU - Talebian, Sepehr
AU - Ghadimi, Azadeh
N1 - Funding Information:
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.
PY - 2014/9
Y1 - 2014/9
N2 - 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.
AB - 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.
KW - Artificial neural networks
KW - Electrospinning parameters
KW - Nanofibers
KW - Polymethyl methacrylate (PMMA)
KW - Response surface methodology
UR - http://www.scopus.com/inward/record.url?scp=84892942251&partnerID=8YFLogxK
U2 - 10.1007/s00521-014-1554-8
DO - 10.1007/s00521-014-1554-8
M3 - Article
AN - SCOPUS:84892942251
SN - 0941-0643
VL - 25
SP - 767
EP - 777
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 3-4
ER -