A study on theoretical predictive model and experimental findings of melt-electrospinning process

The current study extends a previous work published by the authors in which an analytical predictive model is proposed to simulate the melt-electrospinning process. The analytical model is specifically designed to predict the various behaviors of the melt-electrospun fiber under different material and processing conditions. A brief discussion of this model is presented here to establish context and help the reader capture the modeling philosophy employed. The current study complements the previous work by focusing on the experimental aspects of the research. Correlations between the independent process parameters and the topological attributes of the melt-electrospun fibers are investigated and compared with findings from the theoretical model. The effects of changes in the process parameters on average fiber diameters and the collection diameter are experimentally analyzed using the design of experiments (DOE) techniques. Toward this end, polylactic acid (PLA) is melt-electrospun at different treatment levels of the processing parameters in a controlled environment. Two regression-based models—one for predicting the collection diameter and the other for the fiber diameter—are derived from the DOE data for benchmarking and quantitative evaluation of the predictive performance of the theoretical model. The theoretical model is run based on the same treatment levels as the experiment. The elastic parameter values used in the theoretical simulation are extracted from rheological tests. Comparison between the simulated and the observed fiber characteristics revealed that the collector diameter predictions by the theoretical model exhibited approximately a 16.7% difference compared to 24.2% for the average fiber diameter. Finally, a discussion is presented on the challenges and potential factors contributing to the observed differences. Overall, given the identified challenges and gaps in material characterization, the results of the theoretical predictive model are encouraging.