As a researcher in the field of steel casting, I have long been aware of the critical role that compressor support rings play in heavy-duty gas turbines. These components, subject to high-temperature and high-pressure environments, demand impeccable quality to ensure operational reliability. However, the sand casting process for such large steel castings often leads to defects like shrinkage porosity, shrinkage cavities, and thermal cracks, primarily due to complex geometries and challenging solidification control. In this study, I aimed to leverage the ProCAST simulation software to analyze these defects and optimize the casting process, thereby enhancing the quality and yield of compressor support ring steel castings.
1. Research Background and Objectives
Steel casting is a cornerstone of industrial manufacturing, particularly for components requiring high strength and durability. The compressor support ring, a vital steel casting in gas turbines, features a large size (3070mm×1175mm×1462mm) and varying wall thicknesses, with the thickest section reaching 368mm. Such characteristics pose significant challenges in achieving uniform solidification and adequate feeding, making it prone to shrinkage-related defects.
1.1 Defects in Steel Castings
Common defects in steel castings include:
- Shrinkage Cavities: Large, isolated voids formed due to insufficient molten metal feeding during solidification.
- Shrinkage Porosity: Dispersed small voids resulting from interrupted feeding channels during solidification.
- Thermal Cracks: Cracks formed due to thermal stress caused by uneven cooling.
These defects compromise the mechanical properties of the casting, necessitating a systematic approach to process optimization.
1.2 Role of ProCAST in Steel Casting Simulation
ProCAST, a finite element-based simulation software, enables detailed modeling of filling, solidification, and stress development in steel castings. By simulating temperature fields, flow velocities, and solidification sequences, it helps predict defect locations and optimize process parameters. My goal was to use ProCAST to:
- Analyze the filling and solidification behavior of the compressor support ring.
- Predict defect formation in the as-cast design.
- Optimize gating systems, riser designs, and process parameters to eliminate defects.
2. Material and Casting Design
2.1 Material Selection
The casting material chosen was ZG13Cr9Mo2Co1NiVNbNB, a high-temperature resistant steel designed for supercritical steam turbines. Its chemical composition (Table 1) and mechanical properties (Table 2) make it suitable for harsh operating conditions, but also prone to solidification shrinkage due to its narrow solidification temperature range (liquidus: 1494°C, solidus: 1186°C).
Table 1: Chemical Composition of ZG13Cr9Mo2Co1NiVNbNB (wt.%)
| Element | C | Si | Mn | P | S | Cr | Ni | Mo | Co | V | Nb | B | N | Al |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Content | 0.11-0.14 | 0.20-0.30 | 0.80-1.00 | ≤0.020 | ≤0.010 | 9.00-9.60 | 0.10-0.20 | 1.40-1.60 | 0.90-1.10 | 0.18-0.23 | 0.05-0.08 | 0.008-0.011 | 0.015-0.022 | ≤0.020 |
Table 2: Mechanical Properties at Room Temperature
| Property | Tensile Strength (MPa) | Yield Strength (MPa) | Elongation (%) | Impact Toughness (J) |
|---|---|---|---|---|
| Value | ≥630-750 | ≥500 | ≥15 | ≥24 (Charpy V-notch) |
2.2 Initial Casting Design
The initial design employed a bottom-gating system with three risers placed at thick sections for feeding. The gating system included:
- Sprue Cup: Diameter 265mm
- Downsprue: Diameter 80mm
- Runner: Uniform cross-section to ensure even flow distribution
- In-gates: Three in-gates at the bottom of the casting to facilitate smooth filling.
Risers were designed using the modulus method, with dimensions calculated to ensure longer solidification time than the casting sections they fed. The formula for riser modulus (\(M_r\)) relative to the casting modulus (\(M_c\)) is:\(M_r = (1.1-1.2) M_c\) for top risers, ensuring sufficient feeding capacity.
3. Simulation Setup with ProCAST
3.1 3D Modeling and Meshing
I created a 3D model of the casting, gating system, and risers using NX 11.0, then imported it into ProCAST’s Visual-Mesh module. The model was meshed with tetrahedral elements, with finer meshing (20mm) at critical areas like riser junctions and coarser meshing (100mm) for the sand mold. The final mesh comprised 2.23 million elements, balancing accuracy and computational efficiency.
3.2 Boundary and Initial Conditions
- Pouring Temperature: 1575°C, typical for high-chromium steel castings.
- Mold Temperature: 20°C, representing ambient conditions.
- Interface Heat Transfer Coefficient: 500 W/(m²·K) between steel and sand mold, based on empirical data for quartz sand.
- Gravity Direction: +Y direction to simulate vertical casting orientation.
3.3 Defect Prediction Criteria
The Niyama criterion, a widely used metric for shrinkage defect prediction, was employed:\(\frac{G}{\sqrt{R}} < C_{Niyama}\) where G is the temperature gradient (°C/m), R is the solidification rate (°C/s), and \(C_{Niyama}\) is a critical value (1.1 for large steel castings). Values below this threshold indicate potential shrinkage porosity.
4. Results of Initial Simulation
4.1 Filling Process Analysis
The filling simulation revealed:
- Flow Velocity: Initial high velocity (2.5 m/s) in the downsprue, leading to potential turbulence at the in-gates, causing air entrainment and oxidation.
- Temperature Field: A bottom-up temperature gradient (1575°C at inlet to 1510°C at top) at filling completion, which is favorable for sequential solidification but insufficient for uniform feeding.
Figure 1: Velocity vectors during filling, showing turbulent flow at in-gates.
4.2 Solidification Analysis
- Solid Fraction Development: Solidification began at the mold walls, progressing inward. The thick sections (368mm) solidified last, with the risers showing early solidification at the top, blocking feeding channels.
- Temperature Gradient: Inadequate gradient in the central thick section, leading to isolated solidification zones without effective feeding from risers.
4.3 Defect Prediction
ProCAST predicted three major shrinkage cavities at the base of the risers and two regions of shrinkage porosity at the mid-thick sections (Figure 2). The Niyama criterion indicated values as low as 0.8 in these areas, confirming insufficient feeding.
Figure 2: Shrinkage defects predicted by ProCAST, with red areas indicating high defect probability.
5. Process Optimization
5.1 Riser and Chiller Redesign
To address feeding issues, I modified the risers and added chillers:
- Insulated Risers: Replaced conventional sand risers with FT400-insulated risers (50mm thickness), which prolonged solidification time by 30%, ensuring continuous feeding. The effective modulus of insulated risers is calculated as:\(M’_r = \frac{M_r}{a + (b-a)\frac{S_{top}}{S_{total}}}\) where \(a = 0.7\), \(b = 0.7\) for dark insulated risers, increasing \(M’_r\) by 43%.
- Chiller Placement: Installed two pairs of low-carbon steel chillers (740mm×210mm×130mm and 217mm×200mm×156mm) at mid-thick sections to accelerate cooling, creating a stronger temperature gradient for directional solidification.
5.2 正交试验设计 (Orthogonal Experiment Design)
To optimize pouring parameters (浇注温度，浇注速度，砂型温度), I designed a L9 (3⁴) orthogonal array with three factors and three levels (Table 3).
Table 3: Orthogonal Experiment Factors and Levels
| Level | Pouring Temperature (°C) | Pouring Speed (kg/s) | Mold Temperature (°C) |
|---|---|---|---|
| 1 | 1565 | 90 | 20 |
| 2 | 1575 | 100 | 25 |
| 3 | 1585 | 105 | 30 |
Each trial was simulated to measure shrinkage porosity volume, with results analyzed using range analysis to determine factor significance.
6. Optimized Simulation Results
6.1 Filling and Solidification after Optimization
- Smooth Filling: Reduced turbulence due to chillers, with uniform velocity (0.5-1.0 m/s) across in-gates, minimizing oxidation.
- Effective Temperature Gradient: A clear bottom-to-top gradient in riser regions, with chillers accelerating cooling at mid-sections, ensuring sequential solidification from thin to thick sections.
Figure 3: Solid fraction contours showing directional solidification toward insulated risers.
6.2 Defect Reduction
- Shrinkage Cavity Elimination: All major cavities were confined to the risers, with no defects in the casting body.
- Porosity Reduction: Mid-thick sections showed no shrinkage porosity, as chillers improved feeding channel continuity.
6.3 Orthogonal Test Results
The range analysis (Table 4) showed that pouring temperature had the most significant impact on porosity (range \(R_A = 13.96\)), followed by pouring speed (\(R_B = 8.56\)) and mold temperature (\(R_C = 0.57\)). The optimal parameters were determined as:
- Pouring Temperature: 1575°C
- Pouring Speed: 100 kg/s
- Mold Temperature: 20°C
Table 4: Range Analysis of Orthogonal Test Results
| Factor | K1 | K2 | K3 | k1 | k2 | k3 | Range (R) |
|---|---|---|---|---|---|---|---|
| 浇注温度 (A) | 46.33 | 36.86 | 50.82 | 15.44 | 12.29 | 16.94 | 13.96 |
| 浇注速度 (B) | 42.98 | 46.25 | 44.79 | 14.33 | 15.42 | 14.93 | 8.56 |
| 砂型温度 (C) | 44.39 | 44.96 | 44.67 | 14.80 | 14.99 | 14.89 | 0.57 |
7. Validation through Physical Casting
7.1 Production Validation
The optimized process was tested in industrial casting:
- Melting and Pouring: Metal composition verified within specified ranges; pouring parameters strictly controlled (1575°C, 100 kg/s, 20°C mold temperature).
- Nondestructive Testing: Radiographic and ultrasonic tests showed no macro-defects. Microscopic analysis revealed a fine-grained structure with no shrinkage porosity, meeting the required mechanical properties (Table 2).
7.2 Performance Comparison)
Compared to the initial design, the optimized casting showed:
- Defect Rate Reduction: From 16% porosity in the initial design to 8.4% in the optimized design, with no critical defects.
- Yield Improvement: From 65% to 82%, due to reduced material waste from defects.
8. Discussion
8.1 Role of Insulated Risers
Insulated risers prolonged their solidification time, maintaining a liquid pool longer than the casting sections. This ensured continuous feeding, as demonstrated by the shifted final solidification zone to the riser top, eliminating internal cavities. The modulus calculation confirmed a 43% increase in effective modulus, aligning with theoretical expectations for insulated risers.
8.2 Chiller Effect on Solidification
Chillers effectively reduced solidification time at thick sections, creating a thermal gradient that promoted directional solidification. This not only eliminated shrinkage porosity but also reduced thermal stress, minimizing the risk of cracking. The placement of chillers at mid-sections was critical, as it balanced cooling rates between thin and thick regions.
8.3 Interaction of Process Parameters
The orthogonal test highlighted the dominant role of pouring temperature in defect formation. Higher temperatures increased liquid metal volume shrinkage, while lower temperatures improved feeding by reducing solidification time. Pouring speed affected flow turbulence and filling uniformity, with moderate speeds (100 kg/s) optimizing both filling smoothness and feeding capacity. Mold temperature had a negligible effect, likely due to the high thermal mass of the sand mold.
9. Conclusion
In this study, I successfully utilized ProCAST to analyze and optimize the casting process for compressor support ring steel castings. Through detailed simulation of filling and solidification, I identified critical defects in the initial design and implemented targeted improvements: replacing conventional risers with insulated ones, adding chillers for directional solidification, and optimizing pouring parameters via orthogonal testing. The results demonstrated a significant reduction in shrinkage defects, improved mechanical properties, and higher casting yield.
This work underscores the value of CAE tools like ProCAST in steel casting optimization, enabling data-driven decisions to overcome traditional trial-and-error approaches. By integrating material science, numerical simulation, and experimental validation, I have established a robust framework for enhancing the quality of complex steel castings, which can be extended to other high-performance components in industrial applications.