# Effect of Artificial Neural Network Design Parameters for Prediction of PS/TiO2 Nanofiber Diameter

## TL;DR

This study uses artificial neural networks to predict the diameter of PS/TiO2 nanofibers fabricated through electrospinning.

## Contribution

The paper introduces optimized ANN models (MLP and RBF) for predicting nanofiber diameters with high accuracy.

## Key findings

- Optimal MLP configurations achieved low MSEs of 4.03 × 10−3 and 7.01 × 10−3 for two data classes.
- RBF models with 30 and 250 neurons achieved extremely low MSEs of 1.42 × 10−32 and 2.75 × 10−32.
- ANN frameworks can serve as powerful tools for predicting structural features in nanostructured materials.

## Abstract

In this study, polystyrene (PS) and PS/TiO2 nanofibers were fabricated through electrospinning and quantitatively characterized to analyze and predict fiber diameters. To advance predictive methodologies for materials design, artificial neural network (ANN) models based on multilayer perceptron (MLP) and radial basis function (RBF) architectures were developed using system- and process-level parameters as inputs and the fiber diameter as the output. Two data classes were constructed: Class 1, consisting of PS/TiO2 nanofibers, and Class 2, containing both PS and PS/TiO2 nanofibers. The architectural optimization of the ANN models, particularly the number of neurons in hidden layers, had a critical influence on the correlation between predicted and experimentally measured fiber diameters. The optimal MLP configuration employed 40 and 20 neurons in the hidden layers, achieving mean square errors (MSEs) of 4.03 × 10−3 (Class 1) and 7.01 × 10−3 (Class 2). The RBF model reached its highest accuracy with 30 and 250 neurons, yielding substantially lower MSE values of 1.42 × 10−32 and 2.75 × 10−32 for Class 1 and Class 2, respectively. These findings underline the importance of methodological rigor in data-driven modeling and demonstrate that carefully optimized ANN frameworks can serve as powerful tools for predicting structural features in nanostructured materials, thereby supporting rational materials design and synthesis.