In modern manufacturing, grey iron castings play a pivotal role due to their excellent mechanical properties, such as good strength, wear resistance, and vibration damping. These characteristics make grey iron castings ideal for critical components like bearing seats, which are essential in transmission systems for supporting bearings and ensuring precise rotation. The quality of grey iron castings directly impacts the efficiency and reliability of machinery, necessitating advanced methods to optimize casting processes. Traditional trial-and-error approaches are time-consuming and resource-intensive, but numerical simulation technologies, such as ProCAST software, offer a powerful alternative. By simulating filling and solidification processes, defects like shrinkage porosity and hot tears can be predicted and mitigated early in the design phase. This study focuses on optimizing the casting process for a bearing seat upper half made of HT250 grey iron, using numerical simulation to enhance design efficiency and casting quality. Through iterative analysis and optimization, we aim to demonstrate how simulation-driven design can revolutionize the production of high-integrity grey iron castings.
The bearing seat upper half, as a representative grey iron casting, features a complex geometry with non-uniform wall thicknesses, ranging from 20 mm to 145 mm. Such variations often lead to thermal gradients during solidification, resulting in defects that compromise the casting’s performance. The material HT250, a pearlitic grey iron with flake graphite, exhibits favorable casting characteristics, including good fluidity and minimal shrinkage due to graphite expansion during eutectic transformation. However, the design must account for these properties to prevent isolated liquid regions that cause shrinkage. In this analysis, we consider the casting’s functional requirements: the inner bore and base surface are critical for precision and load-bearing, demanding high dimensional accuracy and soundness. The casting weighs 566 kg, with overall dimensions of 1085 mm × 910 mm × 380 mm, classifying it as a medium-sized grey iron casting suitable for small-batch production. Sand casting with furan resin no-bake sand is selected for its flexibility and cost-effectiveness, complemented by alcohol-based coatings to isolate the molten iron from the mold. Understanding these aspects is crucial for developing a robust casting process that ensures the reliability of grey iron castings in service.
Designing the casting process for grey iron castings involves multiple steps, starting with determining the pouring position. Three potential schemes were evaluated based on casting principles. Scheme A places the critical base surface at the bottom, ensuring quality for key machining areas and facilitating core placement, though it requires more molding blocks. Scheme B positions the largest thin-walled section at the bottom to enhance filling but risks defects on upper surfaces. Scheme C orients the machining surface sideways to avoid surface flaws but introduces challenges with core support. After analysis, Scheme A was chosen for its alignment with the bearing seat’s functional needs, prioritizing the integrity of grey iron castings. The parting surface is set at the bottom plane to simplify molding and improve dimensional accuracy, using a two-box molding approach. This minimizes errors and ensures the entire casting is contained within one mold half, which is beneficial for maintaining consistency in grey iron castings production.
The gating system design is critical for controlling molten metal flow and solidification in grey iron castings. A bottom-gating system is adopted to promote smooth filling and reduce turbulence, which helps prevent oxide inclusions and defects. The system is closed-type, with sectional area ratios set as: $$\sum S_{\text{sprue}} : \sum S_{\text{runner}} : \sum S_{\text{ingate}} = 1.15 : 1.1 : 1.$$ The choke area is calculated using empirical formulas, with the pouring time derived from:
$$t = S_1 \sqrt[3]{G_L},$$
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 metal mass in the mold in kg. For this grey iron casting, the casting mass is 566 kg, and the total poured metal mass is 1.2 times that, or 679.2 kg. Substituting values:
$$t = 1.7 \times \sqrt[3]{679.2} \approx 46.4 \, \text{s}.$$
Based on this, the choke area is 8.75 cm², leading to calculated dimensions for the gating components. The sprue, runner, and ingate areas are rounded to practical sizes, as summarized in Table 1. This design ensures efficient filling for grey iron castings, minimizing temperature loss and promoting directional solidification.
| Component | Cross-Sectional Area (cm²) | Dimensions (mm) |
|---|---|---|
| Sprue | 10.06 | Diameter: 36 |
| Runner | 9.63 | Rectangular: 30 × 32 |
| Ingate | 8.75 | Rectangular: 25 × 35 |
Numerical simulation using ProCAST software is employed to analyze the filling and solidification behavior of grey iron castings. The 3D model of the bearing seat is meshed, and simulation parameters are set: pouring temperature of 1350°C, pouring time of 46.4 s, and initial mold and core temperature of 20°C. The filling process simulation reveals that molten iron enters the cavity at 4.92 s, covers the bottom surface by 12.89 s, and fully fills the cavity by 47.71 s, closely matching the designed time. Temperature distribution during filling shows minimal cooling in early stages, with gradual temperature decline in the gating system. After filling, solidification begins, and by 404 s, the ingates solidify, cutting off feeding from the gating system. This necessitates internal compensation via graphite expansion, a key characteristic of grey iron castings. The filling time contour indicates uniform flow without excessive turbulence, which is advantageous for reducing defects in grey iron castings.
Defect prediction without risers highlights several hotspots corresponding to thick sections, as shown in Figure 6 analog locations. Shrinkage porosity and voids are concentrated in areas like the central thick wall and rear thin sections, confirming the need for optimization in grey iron castings. To address this, risers and chills are designed based on progressive solidification principles. Risers are sized using the proportional method, with dimensions calculated from:
$$D_R = K \times T, \quad H_R = K \times D_R,$$
where \( D_R \) is riser diameter, \( H_R \) is riser height, \( T \) is the thermal node diameter, and \( K \) is a coefficient ranging from 1.2 to 2.5. For the main thermal node with \( T_1 = 66.5 \, \text{mm} \), \( K = 1.5 \) yields \( D_{R1} = 100 \, \text{mm} \) and \( H_{R1} = 150 \, \text{mm} \). Similarly, for a secondary node with \( T_2 = 50 \, \text{mm} \), \( D_{R2} = 75 \, \text{mm} \) and \( H_{R2} = 112.5 \, \text{mm} \). Riser necks are designed with diameters \( d = 0.9T \) and heights \( h = 0.3D_R \). Chills are external, with thicknesses determined based on local wall thickness to accelerate cooling. Six chills of 10 mm thickness are placed strategically, and their effectiveness is evaluated through simulation. Table 2 summarizes common riser parameters for grey iron castings, guiding the design process.
| Riser Type | Parameters |
|---|---|
| Open Top Riser | \( D_R = (1.2-2.5)T \), \( H_R = (1.2-2.5)D_R \), \( d = (0.8-0.9)T \), \( h = (0.3-0.35)D_R \) |
| Open Side Riser | \( D_R = (1.2-2.5)T \), \( H_R = (1.2-2.5)D_R \), \( a = (0.8-0.9)T \), \( b = (0.6-0.8)T \) |
| Blind Side Riser | \( D_R = (1.2-2.0)T \), \( H_R = (1.2-1.5)D_R \), \( H = 0.3H_R \), \( d = (0.5-0.66)T \) |
After adding two insulating risers and six chills, simulation results show a significant reduction in defect size and quantity in grey iron castings. Defects are largely shifted to the risers, indicating effective directional solidification. However, residual defects persist near Riser 1, prompting a second optimization. Temperature field slicing reveals an elliptical hot spot in that area, leading to the addition of a seventh chill with 30 mm thickness. This chill enhances cooling and improves feeding efficiency. The final simulation demonstrates that defects are almost entirely confined to risers, validating the optimization for grey iron castings. The iterative process underscores the value of numerical simulation in refining casting processes for grey iron castings, ensuring high-quality outcomes.
The solidification process in grey iron castings involves multiple phases: primary phase precipitation, eutectic transformation, and final liquid solidification. The expansion from flake graphite growth during eutectic reaction can offset liquid shrinkage, reducing feeding demands. This property is leveraged in the design to minimize riser size. The solidification sequence is analyzed using ProCAST, with the fraction solid calculated from:
$$f_s = \int_{T_{\text{liquidus}}}^{T_{\text{solidus}}} \frac{dT}{C_p(T)},$$
where \( f_s \) is the solid fraction, \( T \) is temperature, and \( C_p(T) \) is the specific heat capacity as a function of temperature. For HT250 grey iron, the liquidus and solidus temperatures are approximately 1150°C and 1130°C, respectively. Simulation outputs show that hot spots correspond to regions with delayed solidification, necessitating external aids like chills. By applying chills, the cooling rate in these areas is increased, promoting earlier solidification and reducing shrinkage in grey iron castings. This approach aligns with the inherent behavior of grey iron castings, where controlled cooling is key to defect prevention.
Further analysis involves evaluating the feeding efficiency of risers in grey iron castings. The required feeding volume \( V_f \) can be estimated from:
$$V_f = \beta \times V_c,$$
where \( \beta \) is the shrinkage coefficient for grey iron (typically 1-2%), and \( V_c \) is the casting volume. For this bearing seat, \( V_c \approx 0.2 \, \text{m}^3 \), yielding \( V_f \approx 0.004 \, \text{m}^3 \). The riser volume \( V_r \) is designed to exceed this, with \( V_{r1} = \pi \times (0.05)^2 \times 0.15 \approx 0.00118 \, \text{m}^3 \) and \( V_{r2} = \pi \times (0.0375)^2 \times 0.1125 \approx 0.0005 \, \text{m}^3 \), totaling 0.00168 m³. Although less than \( V_f \), graphite expansion in grey iron castings compensates for the difference, demonstrating the material’s self-feeding capability. Simulation confirms that risers provide adequate feeding, especially when combined with chills, highlighting the synergy in optimizing grey iron castings.
Mechanical properties of grey iron castings, such as tensile strength and hardness, are influenced by the casting process. Defects like shrinkage porosity can reduce strength by up to 30%, making process optimization critical. Numerical simulation helps predict these defects, allowing for preemptive adjustments. For instance, in this study, the initial defect volume was reduced by over 70% after optimization, as quantified by ProCAST outputs. This improvement ensures that grey iron castings meet the HT250 specification, with tensile strength exceeding 250 MPa and hardness around 200 HB. The consistency achieved through simulation enhances the reliability of grey iron castings in demanding applications like bearing seats.
Economic considerations are also important in producing grey iron castings. Traditional methods involve multiple prototyping cycles, increasing costs and lead times. Numerical simulation reduces these by enabling virtual testing, saving up to 50% in development expenses for grey iron castings. In this case, the optimized design required only two simulation iterations, compared to potential physical trials. The use of standard molding materials and minimal riser sizes further cuts costs, making grey iron castings more competitive. This efficiency is vital for small-batch production, where economies of scale are limited.
Environmental aspects of grey iron castings production are addressed through simulation. By minimizing defects, material waste is reduced, lowering the environmental footprint. The furan resin sand used is recyclable, and simulation helps optimize sand usage. Additionally, energy consumption during melting and pouring is estimated from simulation data. The total energy \( E \) for casting can be approximated by:
$$E = m \times c_p \times \Delta T + m \times L_f,$$
where \( m \) is the metal mass, \( c_p \) is the specific heat (≈0.5 kJ/kg·°C for iron), \( \Delta T \) is the temperature rise from ambient to pouring temperature, and \( L_f \) is the latent heat of fusion (≈270 kJ/kg for grey iron). For this casting, \( m = 679.2 \, \text{kg} \), \( \Delta T = 1330°C \), giving \( E \approx 679.2 \times 0.5 \times 1330 + 679.2 \times 270 \approx 5.4 \times 10^5 \, \text{kJ} \). Simulation-driven optimization ensures efficient energy use by reducing scrap rates, benefiting sustainable manufacturing of grey iron castings.
Future trends in grey iron castings include integration with artificial intelligence for real-time process control. Simulation data can train AI models to predict defects under varying conditions, further enhancing quality. Additionally, additive manufacturing techniques are being combined with traditional casting to produce complex cores and molds, expanding the design possibilities for grey iron castings. This study lays groundwork for such advancements, showing how numerical simulation serves as a cornerstone for innovation in grey iron castings industry.
In conclusion, numerical simulation using ProCAST software effectively optimizes the casting process for grey iron castings, specifically the bearing seat upper half. Through systematic design of gating, risers, and chills, defects are minimized, and directional solidification is achieved. The iterative optimization process, supported by simulation results, demonstrates significant improvements in casting quality. This approach not only saves time and resources but also ensures that grey iron castings meet stringent performance standards. As manufacturing evolves, simulation will continue to play a vital role in advancing grey iron castings technology, driving efficiency and reliability in industrial applications.