In modern manufacturing, aluminum alloy shells serve as critical components in aerospace and automotive industries, where any defects during production can significantly impact final product performance. Traditional manufacturing methods often rely on empirical knowledge, making it difficult to precisely control various process variables. Therefore, the introduction of numerical simulation technology to analyze and optimize the lost wax investment casting process for aluminum alloy shells has become a key research focus. This approach allows for predicting and mitigating potential defects, ultimately enhancing product quality and reducing costs. The lost wax investment casting method is particularly advantageous for producing complex, near-net-shape components with minimal machining requirements, aligning with the industry’s goal of achieving “zero defects” and “near-zero allowance” castings.
The advancement of computer technology has facilitated the application of numerical simulations in lost wax investment casting, enabling detailed analysis of thermal and fluid dynamics during the process. In this study, we utilize ProCAST software to simulate the lost wax investment casting of an aluminum alloy shell under varying process conditions. We focus on optimizing two critical parameters: pouring temperature and mold shell preheating temperature. The objective is to identify the optimal combination that minimizes defects such as shrinkage porosity and isolated liquid regions, thereby improving the metallurgical quality of the cast component. Our approach integrates simulation results with experimental validation to ensure accuracy and reliability.
The material selected for this investigation is ZL114A aluminum alloy, which is commonly used in high-performance applications due to its excellent castability and mechanical properties. The chemical composition of ZL114A is detailed in Table 1. The shell component has a complex geometry with dimensions of 321 mm × 303 mm × 142 mm, a volume of approximately 1,153,702 mm³, and a mass of 3.1 kg. It is classified as a medium-sized static structural part with significant variations in wall thickness, ranging from an average of 4 mm to a maximum of 11 mm. The wall thickness distribution is relatively regular, but the presence of hot spots necessitates careful control of the casting process to avoid defects.
| Element | Si | Mg | Ti | Al | Zn | Cu | Mn | Be | Other Impurities | Total Impurities |
|---|---|---|---|---|---|---|---|---|---|---|
| Content | 6.85 | 0.45 | 0.16 | Bal. | ≤0.1 | ≤0.1 | ≤0.1 | ≤0.07 | ≤0.05 | ≤0.15 |
To conduct the numerical simulation, we developed a three-dimensional model of the shell using CAD software. The model was then imported into ProCAST for meshing and analysis. The mesh consisted of tetrahedral elements, with a element size of 3 mm for the casting and 5 mm for the gating system. This resulted in 324,757 surface elements and 2,597,994 volume elements, ensuring sufficient resolution for accurate simulation. The thermal physical properties of the materials were calculated based on the chemical composition, and the interfacial heat transfer coefficient between the casting and the mold shell was set according to established literature values. The governing equations for heat transfer and fluid flow during the lost wax investment casting process are described by the following partial differential equations:
$$ \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) = 0 $$
$$ \rho \left( \frac{\partial \mathbf{v}}{\partial t} + \mathbf{v} \cdot \nabla \mathbf{v} \right) = -\nabla p + \mu \nabla^2 \mathbf{v} + \rho \mathbf{g} $$
$$ \rho c_p \frac{\partial T}{\partial t} + \rho c_p \mathbf{v} \cdot \nabla T = \nabla \cdot (k \nabla T) + Q $$
where \( \rho \) is the density, \( \mathbf{v} \) is the velocity vector, \( p \) is the pressure, \( \mu \) is the dynamic viscosity, \( \mathbf{g} \) is the gravitational acceleration, \( c_p \) is the specific heat capacity, \( T \) is the temperature, \( k \) is the thermal conductivity, and \( Q \) represents any internal heat sources. These equations are solved numerically in ProCAST to simulate the filling, solidification, and defect formation during the lost wax investment casting process.
The experimental design involved varying the pouring temperature (650°C, 700°C, and 750°C) and the mold shell preheating temperature (250°C, 300°C, and 350°C), resulting in nine distinct simulation cases as outlined in Table 2. Each case was analyzed to evaluate the filling behavior, isolated liquid regions, and porosity formation. The optimization criterion was based on minimizing the volume of shrinkage porosity, which is a critical defect in lost wax investment casting.
| Case | Pouring Temperature (°C) | Mold Shell Preheating Temperature (°C) |
|---|---|---|
| 1 | 650 | 250 |
| 2 | 650 | 300 |
| 3 | 650 | 350 |
| 4 | 700 | 250 |
| 5 | 700 | 300 |
| 6 | 700 | 350 |
| 7 | 750 | 250 |
| 8 | 750 | 300 |
| 9 | 750 | 350 |
The filling process simulation for Case 1 revealed a stable and gradual filling of the mold cavity. At 30% filling, the bottom gating system was fully filled, and the alloy liquid began to rise steadily into the shell’s lower sections. By 60% filling, both the gating system and the casting were being filled uniformly, with the liquid levels remaining consistent throughout. At 80% filling, the thin-walled regions started to solidify, and at 100% filling, the entire system was filled, marking the end of the filling phase. This smooth filling behavior is crucial in lost wax investment casting to avoid turbulence and entrapped gases.
Analysis of isolated liquid regions, which occur when molten metal is surrounded by solidified material, showed that for all cases, the solidification sequence was directional. The thin-walled areas solidified first, followed by the thicker sections near the gating system. No significant isolated liquid regions were observed, indicating that the design promotes sequential solidification, which is beneficial for reducing defects in lost wax investment casting.
The evaluation of shrinkage porosity was conducted with a criterion ratio of 3%. The results, summarized in Table 3, indicate that Case 5 (pouring temperature of 700°C and mold shell preheating temperature of 300°C) exhibited the lowest porosity volume of 0.15 cm³, while Case 1 had the highest at 1.31 cm³. The porosity volume \( V_p \) can be related to the solidification parameters through the following empirical relation:
$$ V_p = k_p \cdot \left( \frac{G}{R} \right)^{-n} $$
where \( k_p \) is a material constant, \( G \) is the temperature gradient, \( R \) is the solidification rate, and \( n \) is an exponent typically between 0.5 and 1. This equation highlights the importance of controlling thermal conditions to minimize porosity in lost wax investment casting.
| Case | Pouring Temperature (°C) | Mold Shell Preheating Temperature (°C) | Porosity Volume (cm³) | Average Porosity Result (%) |
|---|---|---|---|---|
| 1 | 650 | 250 | 1.31 | 1.87 |
| 2 | 650 | 300 | 0.43 | 1.62 |
| 3 | 650 | 350 | 0.50 | 1.61 |
| 4 | 700 | 250 | 0.50 | 1.50 |
| 5 | 700 | 300 | 0.16 | 2.11 |
| 6 | 700 | 350 | 0.20 | 1.24 |
| 7 | 750 | 250 | 0.43 | 1.24 |
| 8 | 750 | 300 | 0.17 | 1.29 |
| 9 | 750 | 350 | 0.29 | 1.25 |
Based on the simulation results, we selected the optimal parameters from Case 5 for experimental validation. The lost wax investment casting process was carried out using an IC50DM wax injection machine to produce the shell pattern, which was then assembled with the gating system. A ceramic shell with a thickness of 7 mm was fabricated, and the mold was preheated to 300°C before pouring the molten ZL114A alloy at 700°C. After pouring, the casting was allowed to cool in air. The resulting shell was inspected using digital radiography (DR), which confirmed the absence of significant shrinkage porosity or other defects, validating the simulation predictions.
The successful application of numerical simulation in this study demonstrates its effectiveness in optimizing the lost wax investment casting process. By accurately predicting defect formation, we can reduce the need for costly trial-and-error experiments. The integration of simulation with practical casting not only enhances product quality but also shortens the development cycle for complex components. Future work could involve extending this approach to other alloys or more intricate geometries, further leveraging the capabilities of lost wax investment casting.
In conclusion, the lost wax investment casting process for aluminum alloy shells can be significantly improved through numerical simulation. The optimal parameters identified—pouring temperature of 700°C and mold shell preheating temperature of 300°C—result in minimal defects and high metallurgical quality. This methodology provides a robust framework for optimizing lost wax investment casting processes, contributing to the advancement of precision manufacturing in high-performance industries.