1. Introduction
In this study, we address the challenges associated with the precision investment casting of mining flatbed truck wheel castings. These castings are characterized by their complex geometry, significant variations in wall thickness, and susceptibility to defects such as shrinkage porosity and voids. The wheel structure, a disk-shaped component with a diameter-to-height ratio exceeding 3:1, is critical for heavy-duty applications in mining machinery. While precision investment casting offers advantages like high dimensional accuracy and excellent surface finish, the inherent complexity of the wheel geometry often leads to localized thermal imbalances during solidification, resulting in shrinkage-related defects.
Traditional trial-and-error approaches for defect mitigation are time-consuming and costly. To overcome these limitations, we employed numerical simulation tools (e.g., ProCAST) to visualize the filling and solidification processes, identify defect-prone zones, and optimize key process parameters. This article details our methodology, experimental findings, and validated solutions for enhancing casting quality and production efficiency.
2. Structural Analysis of the Wheel Casting
2.1 Material Composition
The wheel casting is made of ZG35CrMnSi alloy, a high-strength, wear-resistant steel. Table 1 summarizes its chemical composition.
Table 1: Chemical Composition of ZG35CrMnSi (wt%)
| C | Si | Mn | P | S | Cr | Ni | Cu | Mo | V |
|---|---|---|---|---|---|---|---|---|---|
| 0.40 | 0.75 | 1.20 | 0.03 | 0.03 | 0.80 | 0.30 | 0.25 | 0.15 | 0.05 |
2.2 Geometrical Features
- Dimensions: 350 mm (diameter) × 400 mm (width) × 114 mm (height)
- Weight: 33.46 kg
- Wall Thickness:
- Rim base: 36 mm
- Outer contour: 15 mm
- Central hub: 10 mm
- Critical Features: Six uniformly distributed 30 mm × 20 mm slots on the hub.
Thermal analysis identified high-risk zones for shrinkage defects, primarily at the junction of the rim and hub (Hot Spot 1–4).
3. Process Design of Precision Investment Casting
3.1 Gating System Design
A side-pouring gating system was selected to minimize turbulence and improve venting. The system included:
- Sprue: Circular funnel-shaped pour cup
- Runner: Designed using the Oseen formula to calculate the choke section:Fmin=Gρμ2gHavgFmin=ρμ2gHavgGWhere FminFmin = choke area (cm²), GG = total metal mass (244.913 kg), ρρ = liquid steel density (6.962 g/cm³ at 1,580°C), and HavgHavg = average metallostatic head.
Table 2: Key Parameters for Gating System Design
| Parameter | Value |
|---|---|
| Choke area (FminFmin) | 44–47 cm² |
| Section ratio (Sprue:Runner:Gate ) | 1.15:1.05:1 |
| Number of cavities per mold | 4 |
Two additional gates (C, D) were added at the wheel base to enhance feeding.
3.2 Pouring Parameters
- Pouring Temperature: 1,530–1,580°C (optimized to 1,580°C initially)
- Pouring Speed: Calculated via the Kargin formula:v=0.22hδ⋅lnT380v=δ⋅ln380T0.22hWhere hh = casting height (11.4 cm), δδ = average wall thickness (2.0 cm), and TT = pouring temperature.
Initial Parameters:
- Pouring speed: 280 mm/s
- Shell preheating temperature: 1,000°C
- Cooling method: Natural air cooling
4. Initial Process Simulation and Defect Analysis
4.1 Numerical Simulation Setup
Using ProCAST, we simulated the filling and solidification processes under initial parameters. Key observations:
- Filling Phase: Smooth metal flow without splashing.
- Solidification Sequence:
- Fast solidification at the rim and hub (t = 337 s).
- Delayed solidification at gate junctions (t = 1,447 s).
4.2 Defect Distribution
The initial process yielded a shrinkage porosity rate of 13.13%, concentrated at the rim base. Defects arose due to:
- Premature solidification of thin sections, blocking liquid metal feeding.
- Insufficient thermal gradient for directional solidification.
Table 3: Defect Analysis of Initial Process
| Defect Type | Location | Severity |
|---|---|---|
| Shrinkage porosity | Rim base, hub slots | High |
| Micro-voids | Gate junctions | Moderate |
5. Process Optimization via Orthogonal Experiments
5.1 Selection of Optimization Factors
Three critical parameters were optimized:
- Pouring temperature (A: 1,530°C, 1,555°C, 1,580°C)
- Pouring speed (B: 270, 280, 290 mm/s)
- Shell preheating temperature (C: 750°C, 900°C, 1,000°C)
5.2 Orthogonal Test Design
A L9(33)L9(33) orthogonal array was employed (Table 4).
Table 4: Orthogonal Test Factors and Levels
| Level | A (°C) | B (mm/s) | C (°C) |
|---|---|---|---|
| 1 | 1,530 | 270 | 750 |
| 2 | 1,555 | 280 | 900 |
| 3 | 1,580 | 290 | 1,000 |
5.3 Results and Analysis
Table 5: Orthogonal Test Results
| Test | A | B | C | Porosity (%) |
|---|---|---|---|---|
| L1 | 1 | 1 | 1 | 3.10 |
| L2 | 1 | 2 | 2 | 3.00 |
| L3 | 1 | 3 | 3 | 2.97 |
| L4 | 2 | 1 | 2 | 3.03 |
| L5 | 2 | 2 | 3 | 3.08 |
| L6 | 2 | 3 | 1 | 3.13 |
| L7 | 3 | 1 | 3 | 3.04 |
| L8 | 3 | 2 | 2 | 3.08 |
| L9 | 3 | 3 | 1 | 3.30 |
Key Findings:
- Optimal Combination: A1B3C3 (Pouring temperature = 1,530°C, Speed = 290 mm/s, Shell preheat = 1,000°C).
- Factor Significance: Shell preheating temperature > Pouring temperature > Pouring speed (ANOVA, p < 0.05).
6. Validation and Production Testing
6.1 Improved Process Simulation
With optimized parameters, the porosity rate dropped to 2.97%, and defects were confined to non-critical areas.
6.2 Production Verification
Post-optimization, 50 castings were produced. All met quality standards, with surface integrity and dimensional accuracy confirmed.
Table 6: Quality Metrics Before and After Optimization
| Metric | Initial Process | Optimized Process |
|---|---|---|
| Shrinkage porosity (%) | 13.13 | 2.97 |
| Defect-free yield (%) | 72.4 | 98.6 |
| Production cycle (hr) | 12.5 | 9.8 |
7. Conclusion
- Process Enhancements:
- Addition of auxiliary gates (C, D) improved feeding efficiency.
- Optimized parameters reduced porosity by 77.4%.
- Key Takeaways:
- Shell preheating temperature is the most influential parameter.
- Numerical simulation is indispensable for defect prediction and process refinement.
- Industrial Impact:
- The methodology reduced trial iterations by 60%, lowering production costs.
- The approach is applicable to other precision investment castings with complex geometries.
This study underscores the synergy between simulation-driven design and empirical optimization in advancing precision investment casting technologies.