Electrospun nanofiber membrane diameter prediction using a combined response surface methodology and machine learning approach.

ABSTRACT: Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R²). In addition to principal component regression (PCR), locally weighted partial least squares

SUBMITTER: Pervez MN 

PROVIDER: S-EPMC10272235 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature