In modern manufacturing, the bearing seat is a critical component that supports bearings and ensures precise rotational motion in mechanical systems. It fixes the outer ring of the bearing, allowing the inner ring to rotate continuously with high accuracy, thereby reducing friction and enhancing the lifespan, efficiency, and reliability of equipment. Typically positioned at both ends of the bearing, the bearing seat provides stable support and maintains positional relationships between connected parts. Thus, the precision of the bearing seat directly impacts the accuracy of the transmission process. Key precision areas include the inner bore and base sections, where the inner bore serves as a cooperating surface with the bearing for support and positioning, while the base acts as a crucial machining and load-bearing face. Any inadequacy in base quality or installation can lead to failure under external forces. To address these challenges, numerical simulation technology complements traditional trial-and-error methods in casting process design and optimization. This approach not only saves resources and time but also provides intuitive insights into the casting process, ultimately improving the quality of gray iron castings.
This article focuses on the upper half of a bearing seat made from HT250, a common grade of gray iron. Gray iron, characterized by its graphite flake structure, offers excellent strength, wear resistance, heat resistance, and damping capacity. The solidification process of gray iron involves the precipitation of primary phases, eutectic transformation, and the solidification of residual liquid metal. During eutectic solidification, the expansion caused by the growth of graphite flakes compensates for liquid shrinkage, reducing the need for extensive feeding in gray iron casting. We employ ProCAST software to simulate the filling and solidification processes, analyzing defects and optimizing the process through the addition of insulating risers and chills. The initial design is refined based on simulation results to achieve directional solidification and minimize defects such as shrinkage porosity and cavities. This study demonstrates the effectiveness of numerical simulation in enhancing the quality of gray iron castings for complex components like bearing seats.
The three-dimensional structure of the bearing seat upper half is illustrated below, highlighting its asymmetrical design and varying wall thicknesses. This gray iron casting has an outer轮廓尺寸 of 1085 mm × 910 mm × 380 mm, with a maximum wall thickness of 145 mm, a minimum of 20 mm, and an average of 25 mm. The net weight is 566 kg, classifying it as a small to medium-sized gray iron casting. Due to its production in small batches, sand casting with acid-cured furan resin self-hardening sand is adopted for its flexibility, cost-effectiveness, and technical maturity. A resin-based alcohol coating is applied to isolate the molten iron from the sand mold, comprising refractory aggregates, water-soluble aldehyde resin, and methanol.
In gray iron casting, the casting process must account for the material’s solidification behavior. For HT250 gray iron, the primary phases include pearlite, and the eutectic reaction involves the formation of graphite flakes. The volume expansion during graphite growth can offset liquid contraction, but improper design may still lead to defects in thick sections. The casting process design involves selecting the pouring position, parting surface, and gating system to ensure sound gray iron casting. We evaluate three potential pouring positions: Position A places the critical base at the bottom, ensuring quality for large planar surfaces and facilitating core placement, though it may require more mold pieces; Position B positions the largest thin-walled section at the bottom to ensure filling but risks defects in upper machined surfaces; Position C places key machined surfaces on the sides to avoid surface defects but may compromise base quality. After analysis, Position A is chosen for its ability to maintain quality and support directional solidification in gray iron casting.
The parting surface is selected at the bottom of the bearing seat upper half, enabling two-box molding to simplify the process and improve dimensional accuracy. The gating system employs a bottom-gating design with a closed configuration to ensure smooth filling and reduce turbulence. The cross-sectional area ratios for the gating system are set as follows: total sprue area : total runner area : total ingate area = 1.15 : 1.1 : 1. To calculate the gating dimensions, we use the Oseen formula to determine the choke area, and then derive the sprue, runner, and ingate sizes. The pouring time is estimated using an empirical formula for gray iron castings weighing between 100 kg and 1000 kg:
$$ t = S_1 \sqrt[3]{G_L} $$
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 mass of metal in the mold in kg. For a casting mass of 566 kg and a total poured mass of 679.2 kg (1.2 times the casting mass), the pouring time is calculated as 46.4 seconds. The gating system dimensions are summarized in the table below:
| Gating System Type | Bottom Gating |
|---|---|
| Component | Sprue |
| Cross-Section | Circular, diameter = 36 mm |
| Area (cm²) | 10.06 |
| Additional Notes | Runner area = 9.63 cm², Ingate area = 8.75 cm² |
Numerical simulation using ProCAST software is conducted to analyze the filling and solidification processes. The model is created in SolidWorks, imported into ProCAST for meshing, and simulated with a pouring temperature of 1350°C, pouring time of 46.4 seconds, and initial mold and core temperatures of 20°C. The filling process simulation shows that molten iron enters the mold cavity through the sprue, fills the runners, and flows into the cavity via the ingates. The metal contacts the mold walls and fills upward steadily, completing in approximately 47.71 seconds, consistent with the designed time. Temperature distribution during filling indicates minimal heat loss initially, with cooling evident in the gating system by 12.89 seconds. After filling, the gating system solidifies by 404 seconds, isolating liquid regions and relying on gray iron’s expansion for feeding. The filling time plot shows uniform banded patterns, indicating smooth filling that minimizes turbulence and oxide inclusions in gray iron casting.
The solidification process reveals several hot spots where defects are likely to occur, primarily in thick sections and thin-walled areas. Defect prediction without risers shows significant shrinkage porosity and cavities in these regions, aligned with the hot spots. To address this, we design two insulating risers and seven chills based on directional solidification principles. Riser design follows the proportional method, with dimensions calculated using:
$$ D_R = K \cdot T $$
$$ H_R = K \cdot D_R $$
where \( D_R \) is the riser diameter, \( H_R \) is the riser height, \( T \) is the thermal node diameter or casting thickness, and \( K \) is a coefficient ranging from 1.2 to 2.5. For the main thick section with \( T_1 = 66.5 \) mm, \( K = 1.5 \), giving \( D_{R1} = 100 \) mm and \( H_{R1} = 150 \) mm. For the rear section with \( T_2 = 50 \) mm, \( D_{R2} = 75 \) mm and \( H_{R2} = 112.5 \) mm. The riser necks are designed with diameters \( d_1 = 59.85 \) mm and \( d_2 = 45 \) mm, and heights \( h_1 = 35 \) mm and \( h_2 = 26.25 \) mm. Chills are external, with thicknesses of 10 mm, positioned to enhance cooling in critical areas. The table below summarizes common riser types and parameters for gray iron casting:
| Riser Type | Open Top Riser | Open Side Riser | Blind Side Riser |
|---|---|---|---|
| Parameters | \( D_R = (1.2-2.5)T \), \( H_R = (1.2-2.5)D_R \), \( d = (0.8-0.9)T \), \( h = (0.3-0.35)D_R \) | \( D_R = (1.2-2.5)T \), \( H_R = (1.2-2.5)D_R \), \( a = (0.8-0.9)T \), \( b = (0.6-0.8)T \) | \( D_R = (1.2-2.0)T \), \( H_R = (1.2-1.5)D_R \), \( H = 0.3H_R \), \( d = (0.5-0.66)T \) |
After adding risers and chills, simulation results show a reduction in defect size and quantity, with defects shifting to the risers, indicating effective directional solidification. However, a significant defect remains near riser 1, as identified through temperature field slicing. This area exhibits an elliptical high-temperature zone, leading to isolated liquid regions. To further optimize the gray iron casting process, an additional chill (number 7) with a thickness of 30 mm is placed near riser 1. The secondary optimization simulation demonstrates a further decrease in defects, with most defects concentrated in the risers, confirming improved feeding and solidification control.
In conclusion, the casting process for the bearing seat upper half in HT250 gray iron is successfully optimized using numerical simulation. Through analysis of the casting structure and material properties, a bottom-gating system with a defined parting surface is implemented. Simulation identifies defect-prone areas, and the addition of insulating risers and chills achieves directional solidification, reducing shrinkage defects. The optimized process ensures high-quality gray iron casting, meeting the stringent requirements for bearing seat applications. This approach highlights the value of numerical simulation in enhancing gray iron casting processes for complex components, providing a theoretical foundation for practical implementation in foundries.
The use of gray iron in such applications leverages its inherent properties, such as good machinability and vibration damping, which are essential for bearing seats. In gray iron casting, the control of solidification is paramount to avoid defects that could compromise performance. The simulation-assisted optimization not only improves the reliability of the gray iron casting but also reduces production costs and time. Future work could explore the effects of varying alloy compositions in gray iron or the integration of advanced simulation features for multi-scale modeling. Overall, this study underscores the importance of combining traditional foundry knowledge with modern numerical tools to advance gray iron casting technologies.
Furthermore, the economic and environmental benefits of optimizing gray iron casting processes cannot be overstated. By minimizing defects, manufacturers reduce scrap rates and material waste, contributing to sustainable practices. The gray iron casting industry can adopt these methods to enhance product quality and competitiveness. As demand for high-performance components grows, the role of simulation in gray iron casting will become increasingly critical, enabling the production of reliable and durable parts for various industrial sectors.