As a researcher engaged in advanced manufacturing technologies, I have devoted significant effort to the optimization of casting processes for machine tool components. The work presented in this paper focuses on a sliding seat used in a certain machine tool. Sliding seats are critical components in machine tool systems, as they support the worktable and guide precise movements. They are commonly produced by sand casting due to the complexity of their geometry and the mechanical requirements they must fulfill. However, traditional sand casting practices for such components often suffer from unpredictable distributions of casting defects, long mold-making periods, and high costs. To overcome these limitations, I applied advanced casting process simulation technology, combined with additive and subtractive manufacturing methods for rapid mold fabrication. Through this integrated approach, I successfully optimized the casting process and achieved rapid mold production, significantly reducing lead times and costs. This article provides a comprehensive account of my research, including casting process design, numerical simulation, defect analysis, process optimization, rapid mold manufacturing, and parameter optimization using orthogonal experiments.
1. Traditional Sand Casting Challenges for Sliding Seat Production
Sliding seats are typically produced by sand casting, which remains the dominant method for ferrous components. Nevertheless, traditional methods are limited by trial-and-error optimization, long production cycles, and substantial environmental pollution. In my investigation, I noted that conventional sand casting relies heavily on the experience of skilled workers, and it is difficult to precisely predict the formation of sand casting defects such as shrinkage porosities, gas holes, hot tears, and dimensional distortions. These defects not only degrade the mechanical properties of the castings but also shorten their service life. In addition, the mold-making process for a machine tool sliding seat can take weeks, which is not feasible for small-batch or single-piece production that is increasingly demanded by the market. As a consequence, I decided to adopt computational simulation methods to forecast the solidification behavior and to use digital manufacturing techniques for mold production, thus replacing traditional pattern making and improving the reliability of defect control.
The research reported here is part of a broader effort to modernize the casting industry, particularly for machine tool components. I selected a sliding seat with the overall dimensions of 1510 mm × 445 mm × 516 mm and a net weight of 305.68 kg. The casting is characterized by variable wall thicknesses, with a maximum of 45 mm and a minimum of 18 mm, and it contains several thick sections and critical machined surfaces. Such features are prone to hot spots and insufficient feeding, which often induce sand casting defects. Therefore, the main objectives of my study were to design a reliable gating system, to optimize the casting process parameters, and to manufacture the sand mold using additive and subtractive technologies, all while ensuring that the final casting is free from harmful defects.
2. Casting Process Design and Theoretical Foundations
2.1 Material Selection and Casting Parameters
After careful consideration of the mechanical requirements for the sliding seat, I selected gray cast iron HT250 as the casting material. HT250 offers a good balance among strength, wear resistance, damping capacity, and economy, making it suitable for high-load machine tool components. The chemical composition of HT250 used in this study is listed in the following table.
| Element | C | Si | Mn | S | P |
|---|---|---|---|---|---|
| Mass fraction / % | 3.16 – 3.30 | 1.79 – 1.93 | 0.89 – 1.04 | 0.094 – 0.125 | 0.120 – 0.170 |
I determined several essential casting design parameters. The casting shrinkage rate was selected as 1% based on the type of gray iron and the casting dimensions. The machining allowance was set to 2 mm, and the minimum cored hole diameter was established at 50 mm according to the small-batch production mode. These parameters directly influence the dimensional accuracy and the propensity for sand casting defects. Additionally, I designed the gating system with proper dimensions to ensure smooth filling and efficient removal of gas and inclusions.
2.2 Design of Three Gating Systems
Based on the location of the ingates, I designed three different gating systems for comparison:
- Top gating system: ingates are located at the top of the casting.
- Parting gate (middle injection) system: ingates are located at the parting line, approximately at mid-height.
- Bottom gating system: ingates are located at the lowest part of the casting.
Each gating system has distinct advantages and drawbacks with respect to filling stability, temperature distribution, and feeding capability. The top gating system promotes directional solidification and riser feeding but can cause turbulence and sand erosion. The bottom gating system offers smooth filling but results in a higher temperature at the bottom, which may inhibit feeding from risers. The middle injection system combines the benefits of both, providing stable filling and a more favorable temperature gradient.
For each design, I included a sprue well to reduce the impact of the molten metal and to improve the flow distribution. The three-dimensional models were prepared using the UG software, and the important machined surfaces were identified as the top surfaces that later require high precision.
2.3 Thermophysical and Thermodynamic Properties of HT250
To perform accurate numerical simulations, I used the material database of the ProCAST software. The Scheil model was selected to compute the thermophysical properties as functions of temperature. The solidus and liquidus temperatures were determined as 1146 °C and 1220 °C, respectively. The variation curves of thermal conductivity, viscosity, solid fraction, enthalpy, and density with temperature were obtained. In particular, the thermal conductivity decreases slightly with temperature, then increases after 700 °C. The viscosity decreases with temperature. The solid fraction decreases when the temperature rises above the solidus and reaches zero at the liquidus. Enthalpy increases with temperature, and the density decreases with temperature. These data were essential for the subsequent filling and solidification simulations.
For the stress analysis, I also extracted the mechanical properties: secant coefficient of thermal expansion, Young’s modulus, Poisson’s ratio, and yield stress as functions of temperature. The Young’s modulus and yield stress decrease with rising temperature, while Poisson’s ratio increases sharply between the solidus and liquidus temperatures.
2.4 Pouring Parameters
I calculated the optimal pouring parameters based on the casting geometry and material:
- Pouring temperature: 1370 – 1440 °C
- Pouring time: 24 s
- Sand mold preheating temperature: 430 – 510 °C
The pouring time was determined using the formula for gray iron castings:
$$t = S_1 \sqrt[3]{\delta G} \; ,$$
where $\delta = 18$ mm is the main wall thickness, $G = 452.7$ kg is the pouring weight including the gating and riser system, and $S_1 = 1.2$ is the coefficient. This yielded $t = 24$ s. The liquid rising velocity was subsequently checked, which gave $v = h/t = 288/24 = 12$ mm/s, above the minimum allowed value of 10 mm/s for a wall thickness of 18 mm, thus confirming the chosen pouring time is reasonable.
2.5 Numerical Simulation Theoretical Models
I employed the finite element method embedded in ProCAST, which is well suited for analyzing coupled flow, heat transfer, and stress fields. The governing equations for the filling process include the continuity, momentum (Navier–Stokes), energy, and volume-of-fluid equations. The equations are expressed below.
Continuity equation for an incompressible fluid:
$$\frac{\partial u}{\partial x} + \frac{\partial v}{\partial y} + \frac{\partial w}{\partial z} = 0 \; .$$
Momentum equation in the $x$-direction:
$$\rho \left( \frac{\partial u}{\partial t} + u \frac{\partial u}{\partial x} + v \frac{\partial u}{\partial y} + w \frac{\partial u}{\partial z} \right) = -\frac{\partial p}{\partial x} + \rho g_x + \mu \nabla^2 u \; .$$
Energy equation:
$$\rho c \left( \frac{\partial T}{\partial t} + u \frac{\partial T}{\partial x} + v \frac{\partial T}{\partial y} + w \frac{\partial T}{\partial z} \right) = \nabla \cdot (k \nabla T) + S \; .$$
For solidification, I applied the heat conduction equation with latent heat release, and for shrinkage porosity prediction, I used the POROS criterion, which is applicable to gravity casting. The Niyama criterion is less suitable for gray cast iron, so I adopted the POROS model with the newest porosity model (POROS = 1), which couples micro-porosity and macro-porosity. The critical solid fraction for porosity is set to 0.7, the critical solid fraction for pipe formation is 0.3, the density model flag is 1, and the critical feeding length is 1.5×10^-2. Porosity values greater than 0.01 are regarded as macro-porosity, while values below 0.01 are considered micro-porosity.
For residual stress and deformation predictions, I used the elasto-plastic constitutive model. The total strain is the sum of elastic, plastic, and thermal strains:
$$\{ \varepsilon \} = \{ \varepsilon_e \} + \{ \varepsilon_p \} + \{ \varepsilon_t \} \; .$$
Von Mises yield criterion was employed to determine the onset of plastic deformation.
3. Numerical Simulation of the Sliding Seat Casting Process
3.1 Pre-processing and Simulation Setup
I imported the three-dimensional models of the sliding seat with each gating system into ProCAST. After checking and repairing the surfaces, I constructed the sand mold with dimensions of 1780 mm × 765 mm × 390 mm. Mesh generation was performed with a maximum element size of 5 mm for the casting and gating system, and 20 mm for the sand mold. The interface heat transfer coefficients were set as follows:
| Interface | Heat transfer coefficient / (W/m²·K) |
|---|---|
| Casting – sand mold | 500 |
| Casting – cold iron | 2000 |
| Sand mold – cold iron | 500 |
| Sand mold exterior – air | 10 (film coefficient, ambient 20 °C) |
I set the gravity vector as 9.8 m/s². The pouring temperature was 1380 °C, the pouring time was 24 s, and the sand mold preheating temperature was 450 °C for the initial comparison study.
3.2 Filling Process Simulation Results
I simulated the filling process for the three gating systems. For the top gating system, at 25% filling, the molten metal entered the cavity from the top and fell to the bottom, causing slight splashing and turbulence. At 50% filling, the flow became smoother. At 75% and 100%, the cavity was gradually filled from bottom to top. Overall, the top gating system showed acceptable filling, but the initial impact was undesirable.
For the parting gate (middle injection) system, the molten metal entered from the middle and gently filled the lower part of the cavity. No splashing nor vortex was observed. The flow remained stable throughout the entire filling, which is beneficial for reducing gas entrapment and sand erosion. The cavity was completely filled at the end.
For the bottom gating system, the molten metal rose smoothly from the bottom, with no visible turbulence. However, the temperature of the lower region remained higher, which could affect the subsequent solidification sequence.
3.3 Solidification Process Simulation Results
I analyzed the solidification sequences by visualizing the fraction of solid at different times. For all three gating systems, the thin walls solidified first, while the thick sections at the bottom and the top machined surfaces remained liquid for a long time. This behavior creates isolated hot spots that are prone to shrinkage porosities. The bottom gating system showed the slowest solidification time because of the higher temperature near the bottom. In all cases, the top important machined surfaces experienced lack of feeding because they solidified after the liquid metal had been consumed to feed the lower parts.
3.4 Shrinkage Porosity Prediction Results
Using the POROS criterion, I obtained the shrinkage porosity volumes for the three gating systems:
| Gating system | Shrinkage porosity volume / cm³ |
|---|---|
| Top gating | 147.20 |
| Parting gate (middle injection) | 132.58 |
| Bottom gating | 136.43 |
These results clearly showed that the middle injection system produced the least amount of shrinkage porosity. In addition, it offered stable filling and a moderate solidification time. Therefore, I selected the middle injection gating system as the optimal design for the sliding seat. The shrinkage porosities were mainly concentrated at thick sections and on the important machined top surface, as expected.
3.5 Casting Process Optimization for Defect Reduction
To reduce the sand casting defects identified by the simulation, I proposed an optimized casting design that included the following features:
- Four insulating risers placed on the important machined surfaces to provide liquid metal feeding during solidification.
- Steel chills (10–15 mm thick) placed in the thick wall regions to accelerate cooling and to reduce hot spots.
- Exhaust holes added at the highest points of the casting to facilitate gas removal and avoid gas entrapment.
These modifications promote a more uniform temperature gradient and ensure that solidification proceeds from the thinner sections toward the risers, thus eliminating isolated hot spots.
3.6 Simulation Verification of the Optimized Design
I re-ran the simulation with the optimized casting design while keeping the same boundary conditions. The filling process remained smooth and fully completed. The solidification sequence improved significantly: the thick sections cooled faster because of the chills, and the risers were the last to solidify, acting as reservoirs. The shrinkage porosity volume was dramatically reduced from 132.58 cm³ to 1.42 cm³, as shown in the following table.
| Condition | Shrinkage porosity volume / cm³ |
|---|---|
| Before optimization | 132.58 |
| After optimization | 1.42 |
This improvement confirmed that the optimized casting design effectively controls sand casting defects. The residual stress and deformation after optimization were predicted to be 1351.1 MPa and 2.32 mm, respectively. Although these values are still notable, the overall quality of the casting meets the application requirements.
In the context of defect analysis, it is essential to understand that sand casting defects such as shrinkage, gas porosity, and hot tears are frequently caused by improper feeding, high thermal gradients, and inadequate mold venting. My simulation-driven optimization directly addressed these root causes.
4. Rapid Fabrication of Sand Molds Using Additive and Subtractive Manufacturing
4.1 Equipment and Materials
After finalizing the casting process, I manufactured the sand molds using combined additive and subtractive techniques. The upper sand mold, which contained many internal cavities and undercuts, was fabricated by three-dimensional printing (3DP), while the lower sand mold was produced by CNC milling. This approach eliminated the need for expensive and time-consuming patterns.
The additive equipment used was a high-efficiency digital sand mold printing precision forming machine with a working volume of 2000 mm × 1000 mm × 800 mm. The subtractive equipment was a digital die-less casting precision forming machine with a working volume of 3000 mm × 1500 mm × 900 mm. I selected silica sand as the base material, with particle size distribution suitable for the 3DP process. Through sieving and scanning electron microscopy analysis, I chose a 70/140 mesh silica sand, which provides a good balance between surface finish and strength.
4.2 Sand Sample Performance Tests
To verify that the 3D printed sand would meet the casting requirements, I prepared test samples and measured their mechanical and physical properties. The tests included tensile strength, compressive strength, shear strength, permeability, and gas evolution. The results are summarized below.
| Property | Required value | Measured value |
|---|---|---|
| Tensile strength / MPa | > 1.5 | 2.465 |
| Compressive strength / MPa | > 3.5 | 4.093 |
| Shear strength / MPa | > 1.5 | 1.820 |
| Permeability | 160 | 160.8 |
| Gas evolution / (ml/g) | < 12 | 10.0 |
These test results confirmed that the printed sand has adequate strength and permeability for the casting process. The low gas evolution value also ensures that gas-related sand casting defects during pouring are minimized.
4.3 Upper Sand Mold Manufacturing by 3DP
I performed the pre-processing of the upper sand mold model using Materialise Magics software. The sand mold was positioned in the virtual build box with proper orientation. Slicing was performed with a layer thickness of 0.3 mm, resulting in 951 layers. The 3DP machine used furan resin as the binder and a sulfonic acid-based curing agent. The resin addition was in the range of 1.2%–3.0%, and the curing agent addition was 0.18%–0.6%. The printing process ran automatically, and I monitored the quality of the printed layers. The total build time was approximately 6.56 hours. After printing and allowing the resin to fully cure, I removed the upper sand mold from the build chamber. The printed sand mold was clean and free of defects, and it met the dimensional requirements. The total production time for the upper mold was only 1–2 days, compared to several weeks for a traditional pattern.
4.4 Lower Sand Mold Manufacturing by CNC Milling
For the lower sand mold, I adopted CNC milling because it is more economical and faster for the relatively simple geometry without undercuts. I selected three cutting tools: D50 for facing and profiling, D20 for roughing, and B8 ball-end mill for finishing. The tool paths were generated using UG CAM software. The machining process was performed on the digital die-less casting forming machine. A preformed sand block was fixed on the machine table, and the tools were used sequentially according to the programmed paths. The entire lower mold was manufactured in about one day. After milling, I cleaned the surface and applied a refractory coating to improve the surface finish and thermal resistance.
4.5 Mold Assembly
After cleaning and coating, I assembled the upper and lower sand molds. The mating surfaces were properly aligned, and the molds were clamped together. The assembly was then ready for pouring. The use of additive and subtractive technologies reduced the total mold manufacturing time to about one week, whereas traditional pattern-based methods would have required at least two weeks. This digital manufacturing route not only shortens the lead time but also lowers the cost, reduces manual labor, and improves the working environment.
5. Optimization of Pouring Process Parameters Using Orthogonal Experiments
5.1 Influence of Individual Process Parameters on Defects
To further improve the quality of the sliding seat casting, I investigated the influence of three process parameters: pouring temperature, pouring time, and sand mold preheating temperature. I selected five levels for each parameter, as listed below.
| Parameter | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 |
|---|---|---|---|---|---|
| A: Pouring temperature / °C | 1360 | 1380 | 1400 | 1420 | 1440 |
| B: Pouring time / s | 20 | 22 | 24 | 26 | 28 |
| C: Sand mold preheating temperature / °C | 430 | 450 | 470 | 490 | 510 |
I ran simulations while changing one parameter at a time while keeping the others at their middle levels. The results show that the shrinkage porosity volume tends to decrease with increasing pouring temperature. The effect of pouring time is non-monotonic, with minima at 22 s and 26 s, while the sand mold preheating temperature shows a general decreasing trend in shrinkage porosity. For residual stress, the pouring temperature causes an initial increase followed by a decrease, then a slight increase. The pouring time has a more complex effect, and the residual stress decreases steadily with increasing preheating temperature. For deformation, I observed that the deformation first increases and then decreases with temperature; it fluctuates with pouring time and increases with preheating temperature. Overall, the sand mold preheating temperature has the most pronounced influence on residual stress and deformation, while pouring temperature has the greatest influence on shrinkage porosity.
5.2 Orthogonal Experiment Design and Results
To identify the optimal combination of the three parameters for minimizing sand casting defects, I designed an orthogonal experiment L25(5³). The evaluation indicators were the shrinkage porosity volume, the maximum residual stress, and the maximum deformation. The experimental arrangement and simulation results are presented below.
| No. | A / °C | B / s | C / °C | Shrinkage porosity / cm³ | Residual stress / MPa | Deformation / mm |
|---|---|---|---|---|---|---|
| 1 | 1360 | 20 | 430 | 1.89 | 1446 | 2.13 |
| 2 | 1360 | 22 | 450 | 1.81 | 1320.6 | 2.15 |
| 3 | 1360 | 24 | 470 | 1.71 | 1379.8 | 2.22 |
| 4 | 1360 | 26 | 490 | 1.77 | 1318.7 | 2.31 |
| 5 | 1360 | 28 | 510 | 1.74 | 1272.4 | 2.30 |
| 6 | 1380 | 20 | 450 | 1.58 | 1396.8 | 2.13 |
| 7 | 1380 | 22 | 470 | 1.51 | 1369.4 | 2.18 |
| 8 | 1380 | 24 | 490 | 1.70 | 1340.4 | 2.23 |
| 9 | 1380 | 26 | 510 | 1.35 | 1264.1 | 2.33 |
| 10 | 1380 | 28 | 430 | 1.69 | 1374.5 | 2.05 |
| 11 | 1400 | 20 | 470 | 1.17 | 1336.1 | 2.24 |
| 12 | 1400 | 22 | 490 | 1.31 | 1327.8 | 2.26 |
| 13 | 1400 | 24 | 510 | 1.28 | 1236.5 | 2.33 |
| 14 | 1400 | 26 | 430 | 1.22 | 1330.4 | 2.16 |
| 15 | 1400 | 28 | 450 | 1.36 | 1398.5 | 2.17 |
| 16 | 1420 | 20 | 490 | 1.35 | 1278.2 | 2.27 |
| 17 | 1420 | 22 | 510 | 1.16 | 1206.8 | 2.33 |
| 18 | 1420 | 24 | 430 | 1.20 | 1295.2 | 2.08 |
| 19 | 1420 | 26 | 450 | 1.13 | 1369.3 | 2.19 |
| 20 | 1420 | 28 | 470 | 1.19 | 1316.2 | 2.24 |
| 21 | 1440 | 20 | 510 | 1.01 | 1173.7 | 2.32 |
| 22 | 1440 | 22 | 430 | 1.02 | 1426.4 | 2.09 |
| 23 | 1440 | 24 | 450 | 1.02 | 1373.9 | 2.16 |
| 24 | 1440 | 26 | 470 | 0.99 | 1293.4 | 2.26 |
| 25 | 1440 | 28 | 490 | 1.02 | 1289.9 | 2.25 |
5.3 Range Analysis of Orthogonal Experiment Results
I performed range (extreme difference) analysis for each evaluation indicator. The average values of each factor at each level and the range values are summarized below.
| Factor | Level 1 | Level 2 | Level 3 | Level 4 | Level 5 | Range |
|---|---|---|---|---|---|---|
| A for shrinkage porosity | 1.784 | 1.566 | 1.206 | 1.206 | 1.012 | 0.772 |
| B for shrinkage porosity | 1.400 | 1.362 | 1.382 | 1.292 | 1.400 | 0.108 |
| C for shrinkage porosity | 1.404 | 1.380 | 1.314 | 1.430 | 1.308 | 0.122 |
| A for residual stress / MPa | 1347.5 | 1349.04 | 1325.86 | 1293.14 | 1311.46 | 55.9 |
| B for residual stress / MPa | 1326.16 | 1330.2 | 1325.16 | 1315.18 | 1330.3 | 15.12 |
| C for residual stress / MPa | 1374.5 | 1371.82 | 1338.98 | 1311.0 | 1230.7 | 143.8 |
| A for deformation / mm | 2.222 | 2.184 | 2.232 | 2.222 | 2.216 | 0.016 |
| B for deformation / mm | 2.218 | 2.203 | 2.204 | 2.250 | 2.202 | 0.048 |
| C for deformation / mm | 2.102 | 2.160 | 2.228 | 2.264 | 2.322 | 0.220 |
Based on the range values, if shrinkage porosity is the sole criterion, the influence order is: pouring temperature > sand mold preheating temperature > pouring time. The optimal combination is A5B4C5, i.e., pouring temperature 1440 °C, pouring time 26 s, and preheating temperature 510 °C.
If residual stress is the sole criterion, the influence order is: sand mold preheating temperature > pouring temperature > pouring time. The optimal combination is A4B4C5 (1420 °C, 26 s, 510 °C).
If deformation is the sole criterion, the influence order is: sand mold preheating temperature > pouring time > pouring temperature. The optimal combination is A2B5C1 (1380 °C, 28 s, 430 °C).
Since all three indicators are important, a comprehensive method is required to balance them. Therefore, I applied the orthogonal matrix analysis method.
5.4 Matrix Analysis of Multi-Objective Orthogonal Experiments
Matrix analysis is an effective technique for multi-objective orthogonal experiments. It computes the weight of each level for each factor and identifies the optimal combination. I constructed the indicator layer matrix M, factor layer matrix T, and level layer matrix E. For each indicator, since smaller values are better, the entries were calculated as the inverse of the average values.
For the shrinkage porosity indicator, the indicator matrix M1 was built from the reciprocal of the average values for each factor–level combination. Then the factor matrix T1 and level matrix E1 were determined. The weight matrix ω1 was obtained by multiplying these matrices:
$$\omega_1 = M_1 T_1 E_1 \; .$$
The resulting weights for the levels of each factor were obtained. Similarly, I computed the weight matrices for residual stress and deformation. Finally, the total weight for each factor level was calculated by summing the corresponding weights from the three indicators. The factor level with the largest total weight is the optimal one. The total weight values are presented below.
| Factor level | Total weight |
|---|---|
| A1 | 0.0582 |
| A2 | 0.0635 |
| A3 | 0.0764 |
| A4 | 0.0768 |
| A5 | 0.0872 |
| B1 | 0.0230 |
| B2 | 0.0232 |
| B3 | 0.0231 |
| B4 | 0.0234 |
| B5 | 0.0230 |
| C1 | 0.1052 |
| C2 | 0.1039 |
| C3 | 0.1038 |
| C4 | 0.1032 |
| C5 | 0.1057 |
The largest weights correspond to A5, B4, and C5. Therefore, the matrix analysis indicates that the optimal combination is A5B4C5, i.e., pouring temperature 1440 °C, pouring time 26 s, and sand mold preheating temperature 510 °C. This result coincides with the range analysis for the shrinkage porosity criterion, but it also accounts for residual stress and deformation in a balanced manner.
5.5 Verification of the Optimal Process Parameters
I verified the optimal parameter combination by performing a numerical simulation with A = 1440 °C, B = 26 s, and C = 510 °C. The results are compared with the initial optimized casting design (which used 1380 °C, 24 s, and 450 °C) in the following table.
| Parameter set | Shrinkage porosity / cm³ | Residual stress / MPa | Deformation / mm |
|---|---|---|---|
| Before parameter optimization | 1.42 | 1351.1 | 2.32 |
| After parameter optimization | 1.00 | 1178.7 | 2.22 |
These results demonstrate that the optimal process parameters further reduce sand casting defects. The shrinkage porosity volume decreased from 1.42 cm³ to 1.00 cm³, while the maximum residual stress decreased by 172.4 MPa and the deformation reduced by 0.10 mm. The optimization not only improves the quality of the sliding seat casting but also provides a valuable reference for practical production.
6. Conclusions and Outlook
In this research, I successfully combined numerical simulation, process optimization, and digital mold manufacturing to produce a high-quality machine tool sliding seat casting. The main conclusions are as follows:
- I analyzed the casting structure and material, and designed three gating systems. The optimum parting gate (middle injection) system was selected based on filling stability, solidification behavior, and shrinkage porosity prediction.
- Through simulation, I identified the locations of sand casting defects, mainly in thick sections and on the top machined surface. By adding chills, insulating risers, and exhaust holes, the shrinkage porosity volume was reduced from 132.58 cm³ to 1.42 cm³, effectively eliminating macro-porosity.
- I fabricated the sand molds using additive (3DP) for the upper mold and subtractive (CNC milling) for the lower mold. The sand material was carefully selected and tested, and its strength, permeability, and gas evolution met all casting requirements. The mold manufacturing time was shortened to about one week, significantly reducing lead time and cost.
- I performed an orthogonal experiment with three factors and five levels to optimize pouring parameters. The range analysis and matrix analysis both indicated that the optimal combination is pouring temperature 1440 °C, pouring time 26 s, and sand mold preheating temperature 510 °C. Simulation verification confirmed a further reduction in shrinkage porosity, residual stress, and deformation.
This study demonstrates that integrated computational simulation and additive/subtractive manufacturing can significantly improve the quality and efficiency of machine tool casting production. It also offers a sustainable alternative to traditional pattern-based sand casting, which is particularly suitable for single-piece and small-batch manufacturing.
Outlook
Future work can be directed toward several aspects. First, the interfacial heat transfer coefficients between the sand mold, chills, and molten metal were treated as constants in this study; in reality, they are temperature-dependent, so a refined model could improve prediction accuracy. Second, the residual stress values predicted in this work appear higher than typical experimental data for gray cast iron; more sophisticated constitutive models or calibration experiments are recommended. Third, actual pouring experiments should be carried out to validate the numerical predictions and to further verify the effectiveness of the optimized process parameters. Finally, the digital manufacturing route can be extended to other complex machine tool components, promoting the intelligent and green development of the casting industry.