In modern industrial applications, sealing sleeves play a critical role in protecting mechanical components from wear and ensuring effective sealing. These components are characterized by their ease of replacement, low cost, and excellent economic efficiency. They can be categorized based on materials, such as soft metal liners, rubber liners, nylon liners, and non-metallic polymer liners. With advancements in technology, green products have gained significant attention, and aluminum alloys, particularly ZL114A, have emerged as a mature material due to their low density, high strength, and superior corrosion resistance. These properties make them ideal for aerospace and other high-demand sectors. The lost wax investment casting process is widely employed for manufacturing complex parts like sealing sleeves, as it ensures smooth metal filling, minimal defects, and high dimensional accuracy. This method involves creating a wax pattern, coating it with a ceramic shell, and then melting out the wax to form a mold for casting. In this study, I focus on optimizing the lost wax investment casting process for ZL114A alloy sealing sleeves using numerical simulation and experimental validation to address issues like porosity defects.
The sealing sleeve under investigation is a shell-like structure with high integration and complexity. It features distinct functional areas that are closely interconnected, making it challenging to produce. From a metallurgical perspective, the sleeve exhibits significant and irregular wall thickness variations, isolated hot spots, and difficulties in feeding, all of which demand extremely high quality standards, especially for sealing performance, dimensional tolerances, and surface integrity. The outer dimensions of the sleeve are approximately 390 mm in diameter and 127 mm in height, with a maximum thickness of 13 mm and an average thickness of 5.07 mm. The wall thickness distribution is non-uniform, as illustrated in the following analysis. To handle such complexities, lost wax investment casting is preferred over traditional sand casting, as it provides better control over surface roughness and precision.
In the pre-processing phase, I utilized UG software for 3D modeling of the casting and gating system. The material selected was ZL114A alloy, whose chemical composition is detailed in Table 1. This alloy is known for its excellent castability, weldability, and room-temperature mechanical properties, making it suitable for sealing applications. For numerical simulation, I employed Procast software to mesh the geometry using tetrahedral elements. The casting mesh size was set to 2 mm, while the gating system used a 4 mm mesh, resulting in a total of 1,917,916 elements. The mesh generation ensured accurate capture of thermal and fluid dynamics during the lost wax investment casting process. Material thermal properties were assigned from the software’s database, and the interface heat transfer coefficient between the casting and the mold shell was configured based on established literature to account for the intricacies of lost wax investment casting.
| Element | Al | Si | Mg | Ti | Zn | Cu | Mn | Be | Other Impurities | Total Impurities |
|---|---|---|---|---|---|---|---|---|---|---|
| Content | Balance | 6.8 | 0.5 | 0.15 | 0.1 | 0.1 | 0.1 | 0.07 | ≤0.05 | ≤0.15 |
The initial process parameters for the lost wax investment casting simulation included a pouring temperature of 710°C and a mold preheat temperature of 300°C. The cooling medium was set to air, representing standard conditions. The governing equations for heat transfer and solidification in the simulation are based on the Fourier heat conduction law and energy conservation. The general heat conduction equation can be expressed as:
$$ \frac{\partial T}{\partial t} = \alpha \nabla^2 T $$
where \( T \) is temperature, \( t \) is time, and \( \alpha \) is the thermal diffusivity, defined as \( \alpha = \frac{k}{\rho c_p} \), with \( k \) being thermal conductivity, \( \rho \) density, and \( c_p \) specific heat capacity. For the lost wax investment casting process, the interface heat transfer coefficient \( h \) plays a crucial role in accurately predicting thermal gradients. This coefficient is often modeled as a function of temperature and gap formation during solidification. The heat flux at the interface is given by:
$$ q = h (T_{\text{casting}} – T_{\text{mold}}) $$
where \( q \) is the heat flux, and \( T_{\text{casting}} \) and \( T_{\text{mold}} \) are the temperatures of the casting and mold, respectively. In this study, \( h \) was set based on empirical data to reflect the conditions of lost wax investment casting.
The filling process simulation revealed a stable and progressive metal flow. At 30% filling, the molten metal front remained uniform, indicating minimal turbulence. By 50% filling, the consistency persisted, and at 70%, although slight flow separation occurred in the gating system, the overall filling was smooth. By 90% filling, the thin-walled sections had already begun to solidify, demonstrating the effectiveness of lost wax investment casting in controlling fluid dynamics. The temperature distribution during solidification, however, showed inhomogeneities, particularly in the flange areas and central thin-walled regions. These areas exhibited delayed solidification, leading to thermal gradients that promoted shrinkage porosity. The porosity prediction model in Procast, which is based on the Niyama criterion, highlighted these defects. The Niyama criterion \( Ny \) is defined as:
$$ Ny = \frac{G}{\sqrt{\dot{T}}} $$
where \( G \) is the temperature gradient and \( \dot{T} \) is the cooling rate. Regions with \( Ny \) below a critical value are prone to microporosity, which aligns with the observed defects in the simulation.
| Parameter | Value |
|---|---|
| Pouring Temperature | 710°C |
| Mold Preheat Temperature | 300°C |
| Cooling Medium | Air |
| Mesh Size (Casting) | 2 mm |
| Mesh Size (Gating) | 4 mm |
| Total Elements | 1,917,916 |
To validate the numerical results, I conducted experimental trials using the lost wax investment casting process. Wax patterns were fabricated with an IC35 single-station injection machine, assembled with the gating system, and coated with a ceramic shell of approximately 7 mm thickness. The casting was poured at 710°C into a preheated mold at 300°C, with a pouring time of 10 seconds. After pouring, the casting was cooled in air, and K-type thermocouples were placed at strategic points to monitor temperature evolution. The thermocouple locations corresponded to the simulated points, and data was recorded using a data logger. The temperature-time curves from the experiment showed trends similar to the simulation, with a maximum deviation of about 10°C, confirming the accuracy of the lost wax investment casting model. Additionally, DR (Digital Radiography) inspection of the castings revealed porosity defects in the flange and thin-walled areas, consistent with the simulation predictions.
The root cause of the porosity defects was identified as non-uniform solidification, where the central thin-walled sections solidified first, isolating the flange regions and preventing effective feeding. To address this, I optimized the process by implementing forced air cooling (wind cooling) after pouring. This modification aimed to achieve directional solidification by increasing the cooling rate in critical areas. The revised boundary conditions in the simulation involved applying a convective heat transfer coefficient for air flow, which enhanced heat extraction. The energy equation for this scenario incorporates the cooling effect:
$$ \rho c_p \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) – h_{\text{air}} (T – T_{\infty}) $$
where \( h_{\text{air}} \) is the convective heat transfer coefficient for air, and \( T_{\infty} \) is the ambient temperature. The simulation results for the optimized lost wax investment casting process showed a more uniform temperature distribution and a reduction in thermal gradients. The porosity prediction indicated complete elimination of defects in the problematic regions. Experimental validation of the optimized process involved producing castings under the same conditions but with added wind cooling. DR inspection confirmed the absence of porosity, shrinkage, or inclusions, meeting all quality requirements for the sealing sleeve.
| Measurement Point | Simulated Peak Temperature (°C) | Experimental Peak Temperature (°C) | Deviation (°C) |
|---|---|---|---|
| Point 1 (Flange) | 650 | 640 | 10 |
| Point 2 (Central Thin Wall) | 620 | 615 | 5 |
| Point 3 (Lower Flange) | 630 | 625 | 5 |
The success of this optimization underscores the importance of numerical simulation in the lost wax investment casting process. By leveraging tools like Procast, I was able to identify potential defects and implement corrective measures with minimal trial runs, reducing development time and costs. The lost wax investment casting method, combined with advanced simulation techniques, provides a robust framework for producing high-integrity components. Future work could explore further refinements, such as varying the mold materials or incorporating multi-scale modeling to enhance accuracy. Additionally, the integration of machine learning algorithms could automate the optimization process for complex geometries in lost wax investment casting.
In conclusion, the lost wax investment casting process for ZL114A alloy sealing sleeves was successfully optimized through numerical simulation and experimental validation. The initial air-cooling conditions led to porosity defects due to uneven solidification, but the implementation of wind cooling resolved these issues by promoting directional solidification. The simulated results aligned closely with experimental data, demonstrating the reliability of the approach. This study highlights the efficacy of lost wax investment casting for manufacturing high-performance components and emphasizes the value of simulation-driven design in modern foundry practices. As industries continue to demand lighter and more durable parts, the lost wax investment casting process will remain a key technology, with ongoing innovations further enhancing its capabilities.