Revolutionizing Rubber Engineering: Neural Network Calculator Enhances Material Performance

Introduction

The integration of Artificial Neural Networks (ANN) in material science is opening new possibilities for predictive modeling and performance optimization. In a groundbreaking study, researchers at Chuvash State University have developed a Neural Network Calculator designed to accurately predict and enhance the physical, mechanical, and dynamic properties of rubber compounds. This innovation could redefine how rubber formulations are engineered for industrial applications such as rail fasteners, reducing vibration and improving durability.
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Advancing Rubber Material Design with Neural Networks

The study introduces a data-driven approach for analyzing the effects of trans-polynorbornene (TPNB) and basalt fiber on rubber compounds. Traditionally, optimizing such formulations required extensive experimentation. However, by leveraging ANN, the researchers created a calculator capable of interpolating complex experimental data and even solving inverse problems determining the ideal composition to achieve desired material properties.

The rubber compound examined included general-purpose rubbers such as isoprene (SKI-3), butadiene-methylstyrene (SKMS-30ARK), and butadiene (SKD), along with vulcanizing agents, accelerators, and antioxidants. Basalt fiber was added in varying proportions to study its influence on tensile strength, elasticity, hardness, and dynamic damping behavior.

A detailed analysis can be found in the main journal article: https://doi.org/10.29328/journal.aac.1001045.

Findings and Implications

The ANN-based calculator successfully demonstrated the ability to model relationships between rubber composition and performance, even with limited experimental data.
Key findings include:

• Enhanced tensile strength and hardness with increased basalt fiber content.
• Improved vibration and noise absorption characteristics.
• Minimal deviation between experimental and ANN-predicted results, with interpolation errors as low as 1%.
• The ability to predict optimal fiber content for desired elasticity and strength.

This neural approach significantly reduces the need for repetitive testing, allowing faster innovation in polymer research and industrial rubber design.

Broader Impact and Medical-Grade Applications

The World Federation of Engineering Organizations (WFEO) emphasizes the importance of adopting AI-driven technologies to improve the performance and sustainability of industrial materials. The present study aligns with this vision by using ANN modeling to optimize eco-friendly rubber components that could be adapted in transportation, construction, and biomedical devices requiring vibration-damping properties.

Furthermore, this AI-based modeling strategy could be extended to medical elastomers used in prosthetics and protective medical gear, improving precision and durability through computational material design.

Integration with Related Research

This research builds upon prior developments in nanomaterials, propellant modeling, and elastomer dynamics, highlighting how ANN tools bridge experimental and computational materials science.
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