Transformer-Based Mechanical Property Prediction for Polymer Matrix Composites

Abstract

Combinatorial nature of polymer matrix composites design requires a robust predictive model to accurately predict the mechanical properties of polymer composites, thereby reducing the need for extensive and costly trial-and-error approaches in their manufacturing. However, traditional prediction models have been either lacking in accuracy or too resource-intensive for practical use. This study proposes an advanced Transformer-based predictive model simultaneously considering various variables that can influence mechanical properties, while utilizing only a minimal amount of training data. In developing this model, we utilize an extensive dataset across 294 types of polymer composites, using a diverse range of polymers and reinforcements, providing a comprehensive basis for the model’s predictions. The model employs a Transformer-based transfer learning technique, known for its efficiency with small datasets, to predict essential mechanical properties such as tensile strength, tensile modulus, flexural strength, flexural modulus and density. It shows high predictive accuracy (R² = 92%) and makes reliable predictions for combinations of polymer composites that have not been trained on (R² = 82%). Additionally, the model’s effectiveness and learning process are validated through Explainable Artificial Intelligence analysis and latent space visualization.