Alexander A. Rybanov, Victor F. Kablov

Volzhsky polytechnic institute (Branch) of Volgograd state technical university

The article presents the results of applying the Apriori association rule mining algorithm to analyze rubber compound formulations in order to identify technologically significant component combinations. A methodology based on data mining is proposed, which enables the automatic detection of stable ingredient combinations, reduces the search space for formulations, and formalizes expert knowledge. The study analyzed a database of 5065 industrial formulations using support, confidence, and lift metrics. Key associations between rubber compound ingredients were identified. The results demonstrate the potential of association analysis methods for optimizing formulations, reducing development time, and digitalizing rubber compound design.

association rules, rubber compounds, ingredients, machine learning, data mining, database, significance metrics, confidence, support, lift, digitalization of materials science, pattern discovery