Data-driven optimization of biaxial shrinkage and stability in electrospun membranes via machine learning and Monte Carlo simulation

Controlling shrinkage behavior in electrospun membranes is critical for applications that require precise dimensional or mechanical performance. However, experimental variability and limited datasets often hinder the development of robust process models. This study introduces a data-driven framework that combines machine learning with Monte Carlo simulation to enable both accurate and stable shrinkage control in electrospinning using a small experimental dataset. Multiple regression models were trained to predict biaxial shrinkage ratios and their variability, with support vector regression and extreme gradient boosting showing the best performance for accuracy and stability prediction, respectively. Feature importance analysis revealed applied voltage and thermoplastic polyurethane concentration as the dominant parameters. A Monte Carlo-based optimization strategy was employed to identify process parameter sets that achieve target shrinkage ratios while minimizing output variability. The proposed approach enables multi-objective optimization in low-data, high-variability manufacturing environments, offering practical insights into precision fabrication of stimulus-responsive membranes.