Predictive Modeling of Mechanical Properties in Polymer Nanocomposites Using Artificial Intelligence

EasyChair Preprint 14554

Abstract

The integration of artificial intelligence (AI) in materials science has revolutionized the field of polymer nanocomposites. This study explores the application of AI techniques in predictive modeling of mechanical properties in polymer nanocomposites. By leveraging machine learning algorithms and deep learning neural networks, we developed a predictive model that accurately forecasts the mechanical behavior of polymer nanocomposites based on their composition and structural characteristics.

Our model utilizes a comprehensive dataset of experimental results, incorporating parameters such as nanoparticle size, dispersion, and polymer matrix properties. The AI-driven approach enables the identification of complex relationships between these factors and the resulting mechanical properties, including tensile strength, elastic modulus, and toughness.

The predictive model demonstrated high accuracy and robustness, outperforming traditional analytical methods. This innovative approach enables materials scientists and engineers to design and optimize polymer nanocomposites with tailored mechanical properties, streamlining the development process and reducing experimental costs.

Keyphrases: Artificial, Nanocomposites, intelligence, mechanical, polymer

@booklet{EasyChair:14554,
  author    = {Abey Litty},
  title     = {Predictive Modeling of Mechanical Properties in Polymer Nanocomposites Using Artificial Intelligence},
  howpublished = {EasyChair Preprint 14554},
  year      = {EasyChair, 2024}}