Results in Engineering (Dec 2024)
Data-driven prediction of electrospun nanofiber diameter using machine learning: A comprehensive study and web-based tool development
Abstract
Electrospinning has emerged as a versatile technique for fabricating polymer nanofibers with diameters ranging from tens to hundreds of nanometers. Precisely controlling and predicting the nanofiber diameter is crucial for various applications. The complex interplay of multiple electrospinning parameters presents a significant challenge in this endeavor. This study introduces a novel data-driven approach using machine learning (ML) to predict and optimize the diameter of electrospun nanofibers. A comprehensive dataset of approximately 430 data points was compiled from literature sources, including polymer properties and electrospinning process parameters. Six ML algorithms were evaluated: Decision Tree (DT), Extra Trees Regression (ETR), Kernel Ridge Regression (KRR), Random Forest (RF), Support Vector Regression (SVR), and eXtreme Gradient Boosting (XGB). A 75/25 train-test split was selected for optimal model evaluation, with 10-fold cross-validation employed for hyperparameter tuning and estimating the variability of performance metrics. RF and ETR demonstrated the best predictive accuracy, with R2 values of 0.9468 and 0.9421, and RMSE values of 92.3 nm and 96.1 nm, respectively, on the test set. Feature importance analysis and SHapley Additive exPlanations (SHAP) values revealed that polymer concentration, applied voltage, and feed rate significantly influence the nanofiber diameter, which was further elucidated by partial dependence plots. To facilitate accessibility and collaboration, an interactive web server called ENDP was developed, allowing users to input polymer properties and process parameters to predict the nanofiber diameter. This study showcases the potential of ML in guiding the design and optimization of electrospun nanofibers, providing valuable insights for tailoring their properties into specific applications.
