MODELING OF WEAR AND RESTORATION PROCESSES OF HARD-ALLOY STEELS FOR PREDICTING THE DURABILITY OF MACHINE PARTS

Abstract

The relevance of this study is determined by the need to ensure the durability and reliability of agricultural machinery parts that operate under complex abrasive, corrosive, and dynamic loading conditions. It has been established that traditional approaches to service life assessment do not account for combined degradation mechanisms or the effectiveness of restoration technologies, which leads to higher operating costs and reduced equipment productivity. The aim of the article is to develop scientifically justified approaches to predicting the durability of agricultural machinery parts by analyzing the wear processes of hard-alloy steels and assessing the impact of restoration technologies on extending their service life. The research methodology is based on a systematic analysis of operating conditions that define material degradation, classification of wear mechanisms, comparison of the effectiveness of modern restoration technologies, and the development of an integrated model for predicting the service life of parts. The study incorporates the generalization of experimental data, analysis of repair practices, and elements of mathematical modeling that consider the multifactorial impact of loads. The results revealed the patterns of influence of abrasive, adhesive, fatigue, and corrosive mechanisms on the loss of part functionality. It was proven that isolated wear mechanisms are rare, while combined scenarios prevail and accelerate degradation. The effectiveness of modern restoration technologies, including surfacing, plasma spraying, laser alloying, and chemical-thermal treatment, was assessed, demonstrating that the service life of parts can be extended by 1.5 to 3 times. The conclusions confirm that the integrated approach enables a shift from reactive repairs to preventive maintenance, reduces emergency downtime, and improves the economic efficiency of agricultural production. The study also identified limitations related to insufficient data for model validation, lack of standardized methodologies, and challenges in integrating digital monitoring systems. Future research should focus on developing methods for integrating sensor monitoring into modeling schemes, creating large datasets for machine learning algorithms, and providing economic justification for the implementation of durability prediction technologies in industrial practice.