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<p>In this article, we develop a two-component polyurethane system with improved physical and mechanical characteristics using a beta version of a specialized database. The obtained polyurethane sample exceeded the planned values in terms of tensile strength, elasticity modulus, and density, while also demonstrating high values of adhesion, flexural strength, and Shore hardness. It was found that even minor catalyst concentrations (up to 0.02%) significantly influence the architecture of the polymer network within the polyurethane system. The use of the PostgreSQL database containing 115 raw material samples, each described by 9 parameters, enables a systematic approach to formulation design and facilitates the identification of hidden correlations between the properties of raw materials and the final product. This methodology supports a shift from empirical selection to predictable synthesis, thus reducing the development time required for new materials by 40–60%.</p>

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Development of a Two-component Polyurethane System Using a Database Beta Version

  • 期刊论文

  • 发表日期：

    2026年6月20日

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  • R. F. Khamadaliev,
  • G. M. Khamadalieva,
  • T. R. Prosochkina,
  • K. G. Kichatov

摘要

In this article, we develop a two-component polyurethane system with improved physical and mechanical characteristics using a beta version of a specialized database. The obtained polyurethane sample exceeded the planned values in terms of tensile strength, elasticity modulus, and density, while also demonstrating high values of adhesion, flexural strength, and Shore hardness. It was found that even minor catalyst concentrations (up to 0.02%) significantly influence the architecture of the polymer network within the polyurethane system. The use of the PostgreSQL database containing 115 raw material samples, each described by 9 parameters, enables a systematic approach to formulation design and facilitates the identification of hidden correlations between the properties of raw materials and the final product. This methodology supports a shift from empirical selection to predictable synthesis, thus reducing the development time required for new materials by 40–60%.