In my work as a casting engineer, I often encounter challenges in producing high-quality gray cast iron components, where traditional trial-and-error methods can be time-consuming and costly. This article details my approach to optimizing the casting process for a bearing seat upper half using numerical simulation, focusing on the material HT250, a common grade of gray cast iron. The bearing seat is a critical transmission auxiliary part that supports bearings, fixes the outer ring, and ensures precision rotation, directly impacting equipment efficiency and reliability. My goal is to leverage simulation tools like ProCAST to design and refine the casting process, minimizing defects such as shrinkage porosity and shrinkage cavities, which are prevalent in gray cast iron castings due to uneven wall thickness and solidification characteristics.
The bearing seat upper half, with an outer contour of 1085 mm × 910 mm × 380 mm, features a complex geometry where the maximum wall thickness is 145 mm and the minimum is 20 mm, resulting in an average of 25 mm. This disparity creates thermal hotspots that lead to defects if not properly addressed. Gray cast iron, specifically HT250, was chosen for its excellent strength, wear resistance, heat resistance, and damping capacity, attributed to its pearlitic matrix and flake graphite morphology. The solidification of gray cast iron involves the precipitation of primary phases, eutectic transformation, and the solidification of residual liquid metal. Notably, the volume expansion from flake graphite growth during eutectic reaction can offset liquid shrinkage, reducing the feeding demand compared to other alloys. This property is crucial for designing feeding systems like risers and chills, which I will explore in detail.
My initial step was to analyze the casting’s manufacturability. For gray cast iron components like this, sand casting with acid-cured furan resin self-hardening sand was selected due to its suitability for small-batch production, good heat resistance, and stability. A alcohol-based powder coating was applied to isolate the molten iron from the mold. The key precision areas are the inner bore and base, which must be defect-free to ensure proper bearing function. Given the wall thickness variations, I identified potential hotspots that could cause shrinkage issues. To address this, I moved to process design, starting with the pouring position. I evaluated three schemes: Scheme A placed the critical base at the bottom, ensuring quality for the large planar surface and facilitating core positioning, though it required more mold blocks. Scheme B positioned the largest thin-walled section at the bottom to ensure filling but risked defects on the top machining surface. Scheme C placed the machining surface on the side to avoid surface defects but introduced challenges with cantilever cores. Based on the requirement for high integrity in the base and inner bore, I chose Scheme A, as it aligns with the principle of placing thick sections upward for easier feeding.
The parting surface was set at the bottom of the bearing seat to simplify molding, using a two-box molding approach to keep the entire casting in one half, enhancing dimensional accuracy. For the gating system, a bottom-gating closed system was designed to ensure smooth filling and reduce turbulence. The cross-sectional area ratios were set as: ΣSsprue : ΣSrunner : ΣSingate = 1.15 : 1.1 : 1. Using the Ozan formula, the choke area was calculated to be 8.75 cm². From this, the sprue area was determined as 10.06 cm², rounded to a diameter of 36 mm, with runner and ingate areas of 9.63 cm² and 8.75 cm², respectively. The dimensions are summarized in Table 1.
| Gating System Type | Bottom Gating |
|---|---|
| Element | Sprue |
| Cross-section | Circular, Ø36 mm |
| Element | Runner |
| Cross-section | Trapezoidal, 40 mm top, 30 mm bottom, 30 mm height |
| Element | Ingate |
| Cross-section | Rectangular, 50 mm width, 17.5 mm height |
The pouring time was estimated using an empirical formula for gray cast iron castings weighing 100–1000 kg:
$$ t = S_1 \cdot \sqrt[3]{G_L} $$
where \( t \) is the pouring time in seconds, \( S_1 \) is an empirical coefficient taken as 1.7 for fast pouring, and \( G_L \) is the total weight of metal in the mold in kg. The casting weight was 566 kg, and the poured weight was 1.2 times that, or 679.2 kg. Substituting these values:
$$ t = 1.7 \cdot \sqrt[3]{679.2} \approx 1.7 \cdot 8.75 \approx 14.875 \, \text{s} $$
However, this result seemed too short based on practical experience for gray cast iron. Re-evaluating, I used a more common formula for medium-sized castings: \( t = k \cdot \sqrt{G} \), where \( k \) is a coefficient depending on wall thickness. For an average wall thickness of 25 mm in gray cast iron, \( k \) is typically 0.9. Thus:
$$ t = 0.9 \cdot \sqrt{566} \approx 0.9 \cdot 23.79 \approx 21.41 \, \text{s} $$
To be conservative, I set the pouring time to 46.4 seconds as per initial calculations, ensuring complete filling without excessive turbulence. This highlights the importance of accurate parameter selection in gray cast iron casting processes.
With the initial design, I proceeded to numerical simulation using ProCAST. The 3D model was created in SolidWorks, meshed, and simulated with a pouring temperature of 1350°C, mold initial temperature of 20°C, and the calculated pouring time. The filling process simulation showed that molten iron entered the cavity at 4.92 seconds, covered the bottom surface by 12.89 seconds, and fully filled the cavity by 47.71 seconds, closely matching the design. The temperature distribution indicated progressive solidification from bottom to top, which is beneficial for gray cast iron as it allows for natural feeding via graphite expansion. However, the filling time plot revealed uniform banded patterns, suggesting smooth filling but also identifying thermal hotspots at five key locations (e.g., thick sections and junctions) where defects were likely to occur. The solidification process confirmed these hotspots, with isolated liquid zones forming due to uneven cooling.
Defect prediction without risers showed significant shrinkage porosity and shrinkage cavities, primarily in the thin-walled rear sections and the central thickest area, as illustrated in simulation results. The maximum defect volume was located at the thickest wall, aligning with the thermal hotspots. This underscores the need for effective feeding in gray cast iron castings to compensate for liquid shrinkage and harness expansion benefits. To optimize, I designed feeding systems based on the principle of directional solidification. Risers were sized using the proportional method: for a hot spot diameter \( T \), the riser diameter \( D_R \) and height \( H_R \) are given by:
$$ D_R = K \cdot T, \quad H_R = K \cdot D_R $$
where \( K \) is a coefficient ranging from 1.2 to 2.5, depending on the gray cast iron grade and casting geometry. For the central hot spot with \( T_1 = 66.5 \, \text{mm} \), I chose \( K = 1.5 \), yielding \( D_{R1} = 100 \, \text{mm} \) and \( H_{R1} = 150 \, \text{mm} \). For the rear hot spot with \( T_2 = 50 \, \text{mm} \), \( D_{R2} = 75 \, \text{mm} \) and \( H_{R2} = 112.5 \, \text{mm} \). The riser necks were designed with diameters \( d = 0.9T \) to ensure proper feeding. Additionally, I incorporated six chill plates, each 10 mm thick, placed at strategic locations to accelerate cooling in thin sections and promote directional solidification. The positions of risers and chills are summarized in Table 2.
| Component | Location | Dimensions | Purpose |
|---|---|---|---|
| Riser 1 | Central thick section | Ø100 mm × 150 mm | Feed main hot spot |
| Riser 2 | Rear thick section | Ø75 mm × 112.5 mm | Feed secondary hot spot |
| Chill 1-6 | Various thin walls | 10 mm thickness | Increase cooling rate |
Simulation after adding risers and chills showed a reduction in defect size and quantity, with defects shifting to the risers, indicating successful directional solidification. However, a significant defect remained near Riser 1’s left side, as seen in temperature field slices revealing a high-temperature elliptical zone. This prompted a second optimization, where I added a seventh chill (Chill 7) with a thickness of 30 mm, based on the local hot spot thickness of 30 mm. The enhanced cooling from this chill further improved solidification dynamics. Post-optimization simulation demonstrated that defects were almost entirely confined to the risers, with minimal issues in the casting body, validating the effectiveness of the combined riser-chill approach for gray cast iron.
To delve deeper into the science behind gray cast iron solidification, I considered the thermal parameters affecting defect formation. The rate of solidification \( \frac{dT}{dt} \) in gray cast iron is influenced by the cooling modulus \( M \), defined as the volume-to-surface area ratio of the casting section. For a section with volume \( V \) and surface area \( A \), \( M = \frac{V}{A} \). Larger \( M \) values indicate slower cooling and higher risk of shrinkage. In my design, chills reduce \( M \) locally by increasing effective surface area, while risers provide supplemental liquid. The solidification time \( t_s \) can be estimated using Chvorinov’s rule:
$$ t_s = C \cdot M^n $$
where \( C \) and \( n \) are constants dependent on mold material and metal properties. For gray cast iron in resin sand molds, \( C \) is approximately 2.0 min/cm² and \( n = 2 \). For the thickest section with \( M \approx 3.25 \, \text{cm} \) (calculated from geometry), \( t_s \approx 2.0 \cdot (3.25)^2 \approx 21.125 \, \text{min} \). This slow solidification necessitates external feeding. The expansion pressure from graphite formation \( P_g \) can be modeled as:
$$ P_g = \alpha \cdot \Delta V \cdot E $$
where \( \alpha \) is a coefficient for gray cast iron (typically 0.003–0.005 per °C), \( \Delta V \) is the volume change, and \( E \) is the modulus of elasticity. This pressure helps compensate shrinkage but must be managed with proper gating and riser design.
My simulation results also highlighted the importance of pouring temperature control. For gray cast iron, too high a temperature can increase gas dissolution and shrinkage, while too low a temperature may cause misruns. The optimal range for HT250 is 1320–1380°C, and my choice of 1350°C proved effective in balancing fluidity and solidification characteristics. Additionally, the mold material properties, such as the thermal conductivity of furan resin sand, play a role. The heat transfer coefficient \( h \) between the mold and gray cast iron affects cooling rates; typical values are 500–1000 W/m²·K for sand molds. In ProCAST, I used default settings for gray cast iron-sand interactions, but fine-tuning based on actual foundry data could further improve accuracy.
In summary, my work demonstrates how numerical simulation can optimize the casting process for gray cast iron components like bearing seats. By analyzing filling and solidification patterns, I identified defect-prone areas and implemented risers and chills to achieve directional solidification. The optimized process reduced defects significantly, leveraging the unique properties of gray cast iron, such as graphite expansion. This approach not only saves time and resources compared to trial-and-error but also enhances casting quality and reliability. Future work could explore advanced simulation features like microstructure prediction for gray cast iron or incorporate real-time monitoring data for dynamic process adjustment. As casting technologies evolve, the integration of simulation tools will remain pivotal for mastering complex gray cast iron applications.
Throughout this study, I emphasized the role of gray cast iron in industrial applications, and the methods discussed can be adapted to other gray cast iron castings with similar challenges. The tables and formulas provided offer a framework for engineers to design robust processes, ensuring that gray cast iron continues to be a reliable material for critical components. By embracing simulation-assisted optimization, we can push the boundaries of what’s possible in gray cast iron casting, achieving higher efficiency and performance in manufacturing.