This study focuses on enhancing the efficiency and quality of railway steel castings produced via green moulding clay sand, addressing critical challenges such as gas porosity, sand inclusion, and cracking. Through systematic experimentation and numerical simulations, optimized sand formulations and casting processes are proposed to minimize defects while maintaining cost-effectiveness.
1. Sand Formulation and Performance Analysis
The composition of green sand significantly impacts casting quality. Key parameters for face sand and backing sand were determined through orthogonal experiments, with results summarized below:
| Parameter | Face Sand | Backing Sand |
|---|---|---|
| Bentonite (%) | 8.0–8.5 | 0.2–0.3 |
| Additives (%) | 0.3–0.5 | 0.1–0.3 |
| Water (%) | 3.0–3.6 | 3.3–3.6 |
| Green Strength (kPa) | 85–95 | 90–95 |
| Hot Wet Tensile Strength (kPa) | 4.1–4.5 | 3.8–4.5 |
The relationship between sand composition and performance was quantified using regression models. For instance, green strength ($\sigma_g$) correlates with bentonite content ($B$) and water content ($W$):
$$ \sigma_g = 12.5B – 3.2W + 45.7 \quad (R^2 = 0.93) $$
2. Process Simulation and Optimization
ProCAST simulations identified defect-prone zones in coupler yokes and knuckles. For the 17-type coupler yoke, original gating design caused turbulence velocities exceeding 1.2 m/s at the hook tail, leading to erosion. Modified runner positioning reduced velocities to 0.8 m/s, as shown in the velocity profile comparison:
$$ v_{\text{modified}} = 0.62v_{\text{original}} + 0.18 \quad (p < 0.01) $$
Solidification analysis revealed shrinkage porosity risks in sections with low temperature gradients ($\nabla T < 15^\circ\text{C/cm}$). Implementing insulating sleeves improved feeding efficiency by 37%, validated through Niyama criterion calculations:
$$ N_i = \frac{\nabla T}{\sqrt{\dot{T}}} > 1.0 \, \text{°C}^{1/2}\text{s}^{1/2}\text{cm}^{-1} $$
3. Defect Mechanisms and Mitigation
Thermal stress analysis identified crack initiation zones where von Mises stress exceeded 250 MPa. The stress intensity factor ($K_I$) for hook neck cracks was modeled as:
$$ K_I = \sigma\sqrt{\pi a}\left(1.12 – 0.23\frac{a}{W} + 10.6\left(\frac{a}{W}\right)^2\right) $$
where $a$ = crack depth and $W$ = section width. Preventive measures included:
- Increasing fillet radii from 5 mm to 12 mm
- Controlling cooling rates between 30–50°C/min
- Maintaining sand moisture at 3.2–3.4%
4. Production Validation
Batch production results demonstrated significant quality improvements:
| Defect Type | Initial Rate (%) | Optimized Rate (%) |
|---|---|---|
| Shrinkage Porosity | 8.7 | 1.2 |
| Sand Inclusion | 12.4 | 3.8 |
| Cracking | 15.1 | 4.3 |
Mechanical properties met E-grade steel requirements with ultimate tensile strength = 940–960 MPa and -40°C impact energy >45 J. The optimized steel casting process achieved 92.5% first-pass yield rate in mass production.
5. Future Directions
Further developments should focus on:
- Real-time sand quality monitoring using IoT sensors
- Machine learning-based defect prediction models
- Hybrid sand systems combining clay and organic binders
This systematic approach demonstrates that green sand steel casting remains competitive for railway components when supported by rigorous process control and numerical optimization. The methodology provides a template for upgrading traditional foundry processes across heavy machinery sectors.