ML-BASED PREDICTION OF MECHANICAL PROPERTIES OF ShKh15 BEARING STEEL OBTAINED FROM SECONDARY MATERIALS

Baymirzayev Akbarjon Rustamjan o'g'li

Authors

Baymirzayev Akbarjon Rustamjan o'g'li PhD Doctoral Researcher, Andijan State Technical Institute, Andijan, Uzbekistan

Keywords:

bearing steel, ShKh15, machine learning, mechanical properties prediction, secondary materials, artificial neural network, heat treatment optimization

Abstract

This study presents a machine learning-based approach for predicting the mechanical properties of ShKh15 (AISI 52100) bearing steel produced from secondary raw materials. A dataset comprising 180 experimental samples with varying chemical compositions, heat treatment parameters (austenitizing temperature 820–880°C, tempering temperature 150–250°C), and recycled material ratios (20–80%) was compiled from laboratory testing. Three ML models — Random Forest (RF), Artificial Neural Network (ANN), and Gradient Boosting Regression (GBR) — were trained and evaluated for predicting Rockwell hardness (HRC), tensile strength (σb), and impact toughness (KCU). The ANN model with architecture 9-64-32-3 achieved the best performance with R² = 0.9724 for hardness and RMSE = 0.87 HRC. Feature importance analysis identified recycled material ratio (31.4%), austenitizing temperature (22.8%), and carbon content deviation (18.6%) as the most influential parameters. The developed models enable rapid property estimation during bearing steel production from secondary materials, potentially reducing quality control testing by 45–55%.

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