This study investigates the formation mechanisms of shrinkage cavity and porosity defects in sand-cast steering bridge components for construction machinery. A numerical simulation framework is established using InteCAST software to analyze filling patterns, solidification behavior, and defect evolution under varying process parameters. Orthogonal experimental design combined with sensitivity analysis reveals critical relationships between casting parameters and defect severity.
1. Methodology
The numerical simulation incorporates multiphase flow dynamics and heat transfer equations:
$$ \frac{\partial (\rho \phi)}{\partial t} + \nabla \cdot (\rho \mathbf{u} \phi) = \nabla \cdot (\Gamma_\phi \nabla \phi) + S_\phi $$
where $\phi$ represents conserved quantities (velocity, temperature), $\Gamma_\phi$ denotes diffusion coefficients, and $S_\phi$ contains source terms. The defect prediction model employs the Niyama criterion:
$$ Ny = \frac{G}{\sqrt{\dot{T}}} $$
where $G$ is temperature gradient and $\dot{T}$ is cooling rate.
2. Orthogonal Experimental Design
Six critical parameters were evaluated through an L18 orthogonal array:
| Parameter | Level 1 | Level 2 | Level 3 |
|---|---|---|---|
| Pouring Temp (°C) | 1370 | 1380 | 1390 |
| Mold Temp (°C) | 10 | 20 | 30 |
| Mold Density (g/cm³) | 1.45 | 1.55 | 1.65 |
| Mold Heat Capacity (cal/g·°C) | 0.21 | 0.26 | 0.31 |
| Filling Time (s) | 15 | 20 | 25 |
| Sprue Radius (mm) | 25 | 30 | 35 |
3. Defect Formation Analysis
The simulation results demonstrate significant correlations between process parameters and casting defects:
| Factor | Shrinkage Volume Sensitivity | Porosity Count Sensitivity |
|---|---|---|
| Pouring Temperature | 0.2005 | 1.65 |
| Mold Initial Temperature | 0.098 | 1.82 |
| Mold Density | 0.154 | 1.24 |
| Filling Time | 0.132 | 1.07 |
The sensitivity index $S$ is calculated as:
$$ S = \frac{1}{n} \sum_{i=1}^{n} \left( \frac{\Delta D}{\Delta P_i} \right) $$
where $\Delta D$ represents defect metric variation and $\Delta P_i$ denotes parameter variation.
4. Process Optimization
Optimal parameters reducing casting defects by 38.7% were identified:
- Pouring temperature: 1370°C
- Mold preheat temperature: 30°C
- Filling time: 20s
- Sprue radius: 25mm
Key findings demonstrate that:
- Lower pouring temperatures reduce thermal gradients, decreasing shrinkage cavity risks
- Higher mold temperatures improve feeding efficiency
- Optimal sprue design minimizes turbulence-induced porosity
5. Conclusion
This research establishes a systematic approach for predicting and mitigating casting defects in complex sand-cast components. The integration of numerical simulation with orthogonal experimentation provides actionable insights for process optimization, particularly in controlling shrinkage cavity and porosity formation. Future work will focus on machine learning-enhanced defect prediction models for real-time casting process control.
The methodology demonstrates significant potential for improving production quality in heavy machinery casting applications while maintaining cost efficiency. Particular attention to thermal management parameters proves crucial for defect minimization in large-scale castings.