Integrating theoretical studies and computational modeling for predicting nanofiber diameter using artificial intelligence

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The purpose of this study was to predict the diameter of PVA-Averrhoa bilimbi nanofibers using artificial intelligence approaches, such as response surface methodology (RSM) and artificial neural networks (ANN). The RSM and ANN models incorporated three independent variables: voltage, distance, and A. bilimbi concentration, with the dependent variable being the diameter of the PVA-A. bilimbi nanofibers. The RSM approach resulted in an R-squared value of 65.06%, while the ANN approach achieved an R-squared value of 99.98%. The novelty of this research lies in the innovative application of RSM and ANN for predicting the diameter of nanofibers composed of polyvinyl alcohol (PVA) and A. bilimbi. It is expected that this model will assist technicians, researchers, and practitioners in optimizing the variables that are essential to the development of these nanofibers. The integration of artificial intelligence with electrospinning technology holds the potential to develop automated and efficient nanofiber manufacturing processes.