Experimental and Simulation Research Progress on the Solidification Structure Evolution of High Chromium Cast Iron
Abstract
1. Introduction
2. Experimental Investigations on the Solidification Microstructure Evolution of HCCIs
2.1. The Role of Alloy Composition in Microstructure Evolution Control
2.1.1. Influence of Non-Carbide-Forming Elements
- (1)
- Si
- (2)
- B
- (3)
- Ni
- (4)
- N
- (5)
- Cu
- (6)
- Mn
2.1.2. Influence of Carbide-Forming Elements
- (1)
- Cr
- (2)
- Mo
- (3)
- W
- (4)
- Nb, V, and Ti
2.1.3. Synergistic Effects of Multiple Alloying Elements
2.2. Influence of Cooling Rate on Microstructure and Phase Distribution
2.3. Effect of Inoculation Treatment on Microstructural Evolution of HCCIs
3. Simulation of Solidification Microstructure Evolution in HCCIs
3.1. Thermodynamics and Phase Diagram Calculation
3.2. Solidification Dynamics Simulation
3.2.1. Phase-Field Method
3.2.2. Cellular Automaton
3.2.3. Finite Element Method
3.3. Coupling of Experimental and Simulation Studies
4. Conclusions and Outlook
4.1. Conclusions
- (1)
- The solidification microstructure of HCCIs—comprising primary austenite and eutectic carbides—is strongly influenced by the synergistic effects of alloy composition, cooling rate, and inoculation treatment. Non-carbide-forming elements enhance the matrix strength via solid solution strengthening and phase transformation control, while carbide-forming elements refine carbides and modify the interface, thereby improving wear resistance. Multicomponent alloy design enables simultaneous optimization of carbide toughening and matrix phase stabilization. An increased cooling rate significantly refines carbides and promotes the transformation of austenite to martensite, although it must be balanced against microcrack risks. Inoculation improves carbide refinement and uniformity through heterogeneous nucleation and adsorption mechanisms.
- (2)
- Numerical simulations have played a pivotal role in elucidating the mechanisms of solidification microstructure evolution. Thermodynamic calculations can accurately predict carbide precipitation pathways and phase stability. Phase-field and cellular automaton (CA) methods have successfully simulated dendritic growth, eutectic reactions, and carbide evolution. The combined phase-field and strain-gradient plasticity model provides insights into mechanical anisotropy under rapid solidification. Finite element methods (FEM) show excellent performance in predicting macro-defects and thermal–mechanical field distributions.
- (3)
- Despite remarkable progress at both experimental and simulation levels, several key challenges remain. These include the disconnect between experimental and simulation parameters, limited model generalizability (due to strong dependence on fitted data and poor cross-system predictability), oversimplified multiphysics coupling in complex processes (with insufficient capture of temperature–composition interactions), fragmented linkage between macro and microstructure (lacking end-to-end multiscale modeling), and underutilized high-throughput data techniques (with insufficient quantitative mapping of composition–property relationships). To address these challenges, future efforts could focus on developing a more universal parameterization framework, integrating dynamic multiscale correlation models, and utilizing high-throughput data platforms. These approaches may facilitate a deeper understanding of solidification mechanisms and support intelligent optimization of HCCI processing.
4.2. Outlook
- (1)
- Develop dedicated thermodynamic databases for HCCIs (e.g., interfacial energy, latent heat of transformation), and integrate machine learning algorithms to optimize nonequilibrium solidification path predictions. Construct a multiscale simulation platform combining CALPHAD, phase-field, CA, and FEM, enabling full-process modeling from atomic-level mechanisms to macroscopic properties.
- (2)
- Explore novel composite inoculants to break the carbide refinement limit. Design gradient-composition and multiphase composite microstructures tailored for extreme environments such as high temperature and corrosion.
- (3)
- Leverage advanced in situ techniques such as synchrotron radiation and high-temperature confocal scanning laser microscopy (HT-CSLM) to dynamically capture solidification processes and calibrate simulation parameters. Establish quantitative “composition–process–microstructure–property” maps to support inverse design of high-performance HCCIs.
- (4)
- Develop cross-scale defect prediction models for large castings (e.g., mill liners and work rolls), optimize gating and cooling system design, and integrate multiphysics simulations to improve service life under complex conditions.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Huang, L.; Liu, Y.; Fu, H. Experimental and Simulation Research Progress on the Solidification Structure Evolution of High Chromium Cast Iron. Metals 2025, 15, 663. https://doi.org/10.3390/met15060663
Huang L, Liu Y, Fu H. Experimental and Simulation Research Progress on the Solidification Structure Evolution of High Chromium Cast Iron. Metals. 2025; 15(6):663. https://doi.org/10.3390/met15060663
Chicago/Turabian StyleHuang, Longxiao, Yang Liu, and Hanguang Fu. 2025. "Experimental and Simulation Research Progress on the Solidification Structure Evolution of High Chromium Cast Iron" Metals 15, no. 6: 663. https://doi.org/10.3390/met15060663
APA StyleHuang, L., Liu, Y., & Fu, H. (2025). Experimental and Simulation Research Progress on the Solidification Structure Evolution of High Chromium Cast Iron. Metals, 15(6), 663. https://doi.org/10.3390/met15060663