Nodular cast iron, often referred to as ductile iron, has emerged as an ideal material for wheels in rail transportation due to its superior comprehensive mechanical properties, excellent wear resistance, good machinability, and relatively low manufacturing cost. The versatility of nodular cast iron stems from its unique microstructure, characterized by graphite spheroids embedded in a metallic matrix, which can be tailored through controlled solidification and cooling processes. In particular, the cooling rate during the casting process plays a pivotal role in determining the as-cast microstructure, including graphite morphology (such as nodularity, size, and count) and matrix constituents (primarily ferrite and pearlite). For large-scale components like full-size wheels, which exhibit significant variations in wall thickness, non-uniform cooling rates across different sections lead to heterogeneous microstructures and, consequently, inconsistent mechanical properties. This inhomogeneity poses a major challenge in ensuring the reliability and performance of nodular cast iron wheels. Therefore, understanding and predicting the microstructure distribution in full-size nodular cast iron wheels is crucial for optimizing manufacturing processes, reducing development time and cost, and enhancing product quality.
In this study, we investigate the influence of cooling rate on the as-cast microstructure of sand-cooled nodular cast iron (SCDI) through designed mold dimensions, and we perform numerical simulations of the casting process for a full-size nodular cast iron wheel. By combining experimental data with simulation results, we establish predictive models for graphite characteristics and matrix phases. These models are then applied to forecast the microstructure distribution in the wheel, providing insights for process optimization. Our work underscores the importance of integrating thermal analysis with microstructural predictions to advance the application of nodular cast iron in critical engineering components.
The base material used in this research is nodular cast iron, with its chemical composition detailed in Table 1. The composition is carefully controlled to ensure proper graphite nodularization and matrix formation, which are essential for achieving the desired properties in nodular cast iron components.
| C | Si | P | S | Mn | Ni | Cu | Mg | Ceq |
|---|---|---|---|---|---|---|---|---|
| 3.68 | 2.16 | 0.05 | 0.02 | 0.13 | 0.63 | 0.61 | 0.05 | 4.40 |
To simulate the non-uniform cooling conditions encountered in a full-size nodular cast iron wheel, we designed SCDI specimens with varying mold thicknesses. Six different mold dimensions were employed, with thicknesses of 3 mm, 12.5 mm, 25 mm, 50 mm, 75 mm, and 100 mm, while other dimensions were kept constant to ensure one-dimensional heat transfer primarily through the thickness direction. The molds were made of high-strength furan resin sand to withstand the expansion pressures during solidification of nodular cast iron, minimizing the risk of shrinkage defects. Side risers were used for feeding to avoid internal porosity. During casting, K-type thermocouples recorded the temperature evolution, capturing cooling curves that reflect the local solidification time and cooling rate. Due to melting capacity constraints, the specimens were cast in two batches, labeled SCDI-1 and SCDI-2.
The full-size nodular cast iron wheel model has a diameter of 840 mm, a height of 170 mm, a maximum hot-spot circle diameter of approximately 100 mm, and a weight of about 350 kg. Considering the geometry—thicker hub and rim sections and thinner spokes—and the mushy solidification behavior of nodular cast iron, a simultaneous solidification casting process was designed. This includes a closed shower gating system with eight ingates evenly distributed along the thin spokes, chill placements around thermal nodes in the hub and rim, and small risers (Φ10 mm) on the top surfaces of the hub and rim. The mold material is furan resin sand with cast iron flasks to enhance rigidity.
For numerical simulation, we used ProCAST software. The thermophysical properties of nodular cast iron were derived from JMatPro, while the mold and chill materials were selected from the ProCAST database (SAND_Silica for the mold and Fe_Eutectic_Gray_Iron for the chills). The interfacial heat transfer coefficients were set as 700 W/(m²·℃) between the casting and mold, 700 W/(m²·℃) between the mold and chills, and 1000 W/(m²·℃) between the casting and chills. Boundary conditions included a pouring temperature of 1400 °C, a pouring rate of 19.29 kg/s, and air cooling for the entire mold with a heat transfer coefficient of 100 W/(m²·℃). Gravity was set at 9.8 m/s² in the negative normal direction of the gating plane, and the initial temperature for all components was 20 °C.
The cooling curves obtained from the SCDI specimens reveal that as mold thickness decreases, the local solidification time shortens and the actual eutectic temperature drops, indicating enhanced cooling capacity. For instance, at a mold thickness of 12.5 mm, the cooling was so rapid that the eutectic plateau was not detectable, but the cooling rate in the subsequent stage increased. These variations effectively mimic the non-uniform thermal histories expected in a full-size nodular cast iron wheel, making SCDI specimens suitable for studying cooling rate effects on microstructure in nodular cast iron.
Microstructural analysis was conducted on samples taken from the center of each SCDI specimen. The graphite phase appeared as spheroids uniformly distributed in the matrix, which consisted of ferrite (bright regions) and pearlite (dark regions). Quantitative measurements using Image-Pro Plus software yielded data on graphite nodularity, average diameter, graphite nodule count per unit area, and the volume fractions of ferrite and pearlite. The results are summarized in Table 2, highlighting the impact of mold thickness on these microstructural features in nodular cast iron.
| Mold Thickness (mm) | Graphite Nodularity (%) | Average Graphite Diameter (μm) | Graphite Nodule Count (per mm²) | Ferrite Content (%) | Pearlite Content (%) |
|---|---|---|---|---|---|
| 3 | 93.75 | 12.29 | 549.51 | 11.61 | 88.39 |
| 12.5 | 91.42 | 15.87 | 320.18 | 15.33 | 84.67 |
| 25 | 88.16 | 19.45 | 180.92 | 22.47 | 77.53 |
| 50 | 85.94 | 24.33 | 120.75 | 31.25 | 68.75 |
| 75 | 82.51 | 28.91 | 90.64 | 36.89 | 63.11 |
| 100 | 80.71 | 30.67 | 74.77 | 39.08 | 60.92 |
As mold thickness decreases, graphite nodularity and nodule count increase, while the average graphite diameter decreases. This is attributed to higher undercooling during eutectic reaction, which promotes nucleation and limits growth time for graphite nodules in nodular cast iron. For matrix constituents, ferrite content initially remains relatively stable but decreases significantly at smaller thicknesses, whereas pearlite content shows the opposite trend. This behavior is linked to cooling rate effects on carbon diffusion during eutectoid transformation; faster cooling suppresses ferrite formation, favoring pearlite in nodular cast iron.
To establish predictive models, we first simulated the thermal history of SCDI specimens using ProCAST. The simulated cooling curves align well with experimental data, allowing extraction of two key parameters: local solidification time (t_s) during eutectic solidification and average cooling rate (R_c) in the temperature range of 800–900 °C during cooling. These parameters correlate with microstructural features. For graphite characteristics, we derived relationships with local solidification time, as shown in Figure 1. The data were fitted using exponential functions via least squares method, with adjusted coefficients of determination close to 1, indicating good fit quality. The predictive equations for graphite in nodular cast iron are:
Graphite nodularity (%) = 81.38 + 14.84 × e^{-t_s / 95.63} \quad (1)
Average graphite diameter (μm) = 30.29 – 18.20 × e^{(15.27 – t_s) / 143.99} \quad (2)
Graphite nodule count (per mm²) = 78.50 + 628.12 × e^{-t_s / 60.40} \quad (3)
For matrix phases, ferrite content correlates with cooling rate. The fitted equation, including equilibrium conditions, is:
Ferrite content (%) = 12.66 + 86.44 × e^{-R_c / 0.095} \quad (4)
where R_c is in °C/s. Pearlite content is then calculated as 100% minus ferrite content. The adjusted coefficient of determination for ferrite content is 0.926, reflecting a moderate fit due to data clustering at higher mold thicknesses, but it adequately captures the trend for nodular cast iron.
These models enable microstructure prediction based on thermal parameters. We applied them to the full-size nodular cast iron wheel by simulating its casting process. The temperature field results provide local solidification times and cooling rates along the wheel’s central cross-section. The distributions are plotted in Figure 2, revealing that regions with chills (e.g., hub and rim surfaces, spokes) exhibit lower local solidification times (75–150 s), while thermal centers (e.g., hub and rim cores, ingate-adjacent areas) show peaks. Cooling rates increase radially from the center outward during the cooling stage.
Using equations (1) to (4), we predicted the as-cast microstructure distribution along the wheel’s central line. The results are summarized in Table 3. Graphite nodularity ranges from 80% to 90%, indicating excellent spheroidization typical of high-quality nodular cast iron. Average graphite diameter varies between 20 μm and 30 μm, and nodule count ranges from 75 to 300 per mm², with slight coarsening and lower counts at thermal centers. Overall, the simultaneous solidification design ensures relatively uniform graphite distribution in the nodular cast iron wheel. For matrix phases, ferrite content ranges from 13% to 41%, and pearlite from 59% to 87%, with a trend of decreasing ferrite and increasing pearlite near the rim surface due to higher cooling rates.
| Position (Radial Distance from Center) | Local Solidification Time, t_s (s) | Cooling Rate, R_c (°C/s) | Graphite Nodularity (%) | Average Graphite Diameter (μm) | Graphite Nodule Count (per mm²) | Ferrite Content (%) | Pearlite Content (%) |
|---|---|---|---|---|---|---|---|
| Hub Core | 145 | 0.12 | 80.5 | 29.8 | 78.2 | 40.8 | 59.2 |
| Hub Surface | 85 | 0.35 | 87.2 | 23.1 | 185.4 | 25.3 | 74.7 |
| Spoke Region | 95 | 0.28 | 85.9 | 24.5 | 150.7 | 28.9 | 71.1 |
| Rim Core | 150 | 0.10 | 80.1 | 30.2 | 75.0 | 41.2 | 58.8 |
| Rim Surface | 75 | 0.40 | 89.5 | 20.3 | 295.6 | 13.5 | 86.5 |
The integration of experimental data and numerical simulation provides a robust framework for predicting microstructure in nodular cast iron components. The models developed here are based on fundamental thermal parameters, making them adaptable to other geometries and casting conditions for nodular cast iron. However, it’s important to note that factors such as alloy composition, inoculation practice, and mold material can influence microstructure in nodular cast iron. Future work could incorporate these variables to enhance prediction accuracy. Additionally, the mechanical properties derived from microstructure, such as tensile strength and hardness, could be correlated using empirical relationships, further extending the utility of this approach for nodular cast iron applications.
In summary, this study demonstrates that cooling rate, controlled by mold design, significantly affects the as-cast microstructure of nodular cast iron. Through systematic experiments and simulation, we established predictive models for graphite and matrix phases in nodular cast iron. Applying these to a full-size wheel reveals that a simultaneous solidification process yields uniform graphite distribution, while matrix phases vary radially, with pearlite enrichment at high-cooling-rate surfaces. This methodology offers a valuable tool for optimizing casting processes and ensuring quality in nodular cast iron products, particularly for large-scale components like wheels in transportation systems.
To further elaborate on the theoretical underpinnings, the solidification kinetics of nodular cast iron can be described using classical nucleation and growth theory. The rate of graphite nucleation (N) is often expressed as a function of undercooling (ΔT):
N = N_0 \cdot e^{-A / (\Delta T)^2} \quad (5)
where N_0 and A are material constants. For nodular cast iron, higher cooling rates increase ΔT, leading to enhanced nucleation and finer graphite nodules. Similarly, graphite growth is diffusion-controlled, with the growth velocity (v) given by:
v = D \cdot (C_e – C_i) / r \quad (6)
where D is the diffusion coefficient, C_e and C_i are equilibrium and interface carbon concentrations, and r is the graphite radius. Shorter local solidification times limit growth, reducing graphite size in nodular cast iron. These principles align with our empirical models.
For matrix formation, the eutectoid transformation in nodular cast iron involves competitive growth of ferrite and pearlite. The cooling rate affects the driving force for diffusion, with higher rates favoring pearlite due to reduced time for carbon partitioning. This can be modeled using Avrami-type equations, but our simplified exponential fit suffices for engineering predictions in nodular cast iron.
In practice, the casting of nodular cast iron wheels requires careful control of process parameters to avoid defects like shrinkage porosity or degenerate graphite. Our simulation approach helps identify thermal hotspots and optimize chill placement. For instance, the predicted microstructure uniformity validates the simultaneous solidification design, but minor adjustments in chill size or riser design could further homogenize properties in nodular cast iron wheels.
Moreover, the economic and environmental benefits of using nodular cast iron in wheels are noteworthy. Compared to steel, nodular cast iron offers lower density and better damping capacity, reducing noise and vibration in rail systems. The predictability of microstructure through models like ours enhances sustainability by minimizing trial-and-error production, saving energy and materials in manufacturing nodular cast iron components.
In conclusion, this work highlights the synergy between experimental investigation and computational modeling for advancing nodular cast iron technology. By focusing on cooling rate effects and developing predictive tools, we contribute to the reliable production of high-performance nodular cast iron wheels, supporting their expanding role in modern transportation. As industries seek lightweight and durable materials, nodular cast iron stands out, and methods for microstructure prediction will be increasingly vital for innovation in nodular cast iron applications.