Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

다국어 초록 (Multilingual Abstract)

In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand‐alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables.






We developed an artificial neural networks (ANN) model to predict and analyze 3D melt electrospun polycaprolactone fiber diameter as a function of process parameters such as collector speed, tip‐to‐nozzle distance, applied pressure, and voltage. The predictions and analysis agree with experimental values and the electrospinning mechanism. A user‐friendly ANN software was created for easy use of the model without programming knowledge or artificial neural networks.