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<p>To better understand the melting behavior of GCr15 bearing steel and the carbon redistribution mechanism during frozen shell formation, a 3D numerical model integrated with experimental validation was developed. This approach combined experimental thermal analysis with multiphysics computational modeling to study the scrap melting process. The model rigorously accounts for temperature-dependent thermophysical properties, carbon–chromium interactions, and their influence on melting dynamics. Validation against experimental data demonstrated excellent agreement. Results show that natural convection greatly influences the spatial distribution of carbon and the melting process of the steel bar. Macroscopic segregation during the solidification process leads to the formation of a distinct carbon concentration gradient on the frozen shell. Specifically, the melting rate at the top of the steel bar is significantly accelerated compared to the bottom, while the axial length remains nearly unchanged during the initial stages of melting. During the formation of the frozen shell, the low Peclet number indicates that natural convection is more dominant than diffusion. The redistribution of solutes leads to an uneven distribution of carbon, with the lowest carbon content at 3.47 wt&#xa0;pct observed 0.5 mm from the steel bar’s surface at its midpoint. Furthermore, carbon accumulates at the solid–liquid interface. At the base of the steel bar, natural convection influences the diffusion of carbon, thereby reducing the carburization rate. The relationship between the preheating temperature of GCr15 and the melting of steel scrap was determined through modeling. Simulation results indicate that increasing the scrap temperature from 300 K to 700 K can reduce the maximum frozen shell mass by 28 pct, with a maximum difference in frozen shell production of 50.42 pct. This work quantitatively correlates the melting rate of steel scrap with thermal temperature, offering a robust predictive model to optimize process control in metallurgical operations.</p>

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Mechanism of Carbon Redistribution in GCr15 Steel Scrap Melting Under Natural Convection: A Combined Numerical and Experimental Study

  • 期刊论文

  • 发表日期：

    2025年8月14日

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  • Yuan Cai,
  • Lingzhi Yang,
  • Guangsheng Wei,
  • Fuhai Liu,
  • Guanghui Li

摘要

To better understand the melting behavior of GCr15 bearing steel and the carbon redistribution mechanism during frozen shell formation, a 3D numerical model integrated with experimental validation was developed. This approach combined experimental thermal analysis with multiphysics computational modeling to study the scrap melting process. The model rigorously accounts for temperature-dependent thermophysical properties, carbon–chromium interactions, and their influence on melting dynamics. Validation against experimental data demonstrated excellent agreement. Results show that natural convection greatly influences the spatial distribution of carbon and the melting process of the steel bar. Macroscopic segregation during the solidification process leads to the formation of a distinct carbon concentration gradient on the frozen shell. Specifically, the melting rate at the top of the steel bar is significantly accelerated compared to the bottom, while the axial length remains nearly unchanged during the initial stages of melting. During the formation of the frozen shell, the low Peclet number indicates that natural convection is more dominant than diffusion. The redistribution of solutes leads to an uneven distribution of carbon, with the lowest carbon content at 3.47 wt pct observed 0.5 mm from the steel bar’s surface at its midpoint. Furthermore, carbon accumulates at the solid–liquid interface. At the base of the steel bar, natural convection influences the diffusion of carbon, thereby reducing the carburization rate. The relationship between the preheating temperature of GCr15 and the melting of steel scrap was determined through modeling. Simulation results indicate that increasing the scrap temperature from 300 K to 700 K can reduce the maximum frozen shell mass by 28 pct, with a maximum difference in frozen shell production of 50.42 pct. This work quantitatively correlates the melting rate of steel scrap with thermal temperature, offering a robust predictive model to optimize process control in metallurgical operations.