In the field of modern manufacturing, the production of machine tool components still heavily relies on traditional sand casting techniques. However, these conventional methods face significant challenges in predicting defect distributions accurately, and they suffer from long mold-making cycles and high costs. The optimization of casting processes through trial-and-error is time-consuming and expensive, especially for complex castings such as machine tool sliding seats. To address these issues, I conducted a comprehensive study on a specific machine tool sliding seat, employing advanced casting process simulation technology combined with additive and subtractive manufacturing for rapid mold production. The primary goal was to optimize the casting process, reduce defects, and shorten the production cycle. This paper presents my research findings, which demonstrate that integrating numerical simulation with additive-subtractive sand mold manufacturing can significantly improve casting quality and production efficiency.
Introduction
Casting is one of the four major forming technologies in the machinery industry, alongside forging, welding, and machining. It plays a vital role in manufacturing, especially for producing complex components with good mechanical properties. Among various casting methods, sand casting dominates due to its flexibility and cost-effectiveness, accounting for more than 80% of cast products worldwide. The sliding seat of a machine tool is a critical component that supports the worktable, transmits cutting forces, and guides precision movements. It must possess excellent static and dynamic stiffness, as well as thermal stability. Typically made of gray cast iron such as HT250, the sliding seat often features complex geometries with multiple ribs, variable cross-sections, and large dimensions. These characteristics make the casting process prone to defects like shrinkage porosity, hot tears, and residual stresses.
Traditional process optimization relies heavily on empirical knowledge and trial-and-error methods. Each trial requires 2–3 weeks and significant financial investment for mold modifications. Moreover, it is difficult to precisely predict defect locations without advanced simulation tools. Consequently, the need for a more efficient and intelligent approach becomes imperative. With the advent of computer technology and multi-physics simulation, casting numerical simulation has emerged as a powerful tool to analyze filling, solidification, stress fields, and defect formation. Additionally, digital manufacturing technologies such as 3D printing (additive) and CNC milling (subtractive) offer new possibilities for rapid mold fabrication without the need for traditional patterns. In this study, I combined ProCAST simulation with additive-subtractive mold manufacturing to optimize the casting process of a sliding seat and achieve rapid production.
Casting Process Design
Structure and Material Analysis
The sliding seat investigated in this work has an overall dimension of 1510 mm × 445 mm × 516 mm, with a net weight of 305.68 kg. It is classified as a medium-sized casting. The maximum wall thickness is 45 mm, while the minimum is 18 mm, with the majority of walls around 18 mm thick. The component requires high strength and wear resistance, leading me to select HT250 gray cast iron. Table 1 lists the typical applications and tensile strengths of gray cast irons, while Table 2 presents the chemical composition of HT250.
| Grade | Tensile Strength (MPa) | Application |
|---|---|---|
| HT100 | ≥100 | Low load parts |
| HT150 | ≥150 | Medium load parts |
| HT200 | ≥200 | Higher load parts |
| HT250 | ≥250 | High load parts |
| HT300/HT350 | ≥300/350 | High strength wear-resistant parts |
| C | Si | Mn | S | P |
|---|---|---|---|---|
| 3.16–3.30 | 1.79–1.93 | 0.89–1.04 | 0.094–0.125 | 0.120–0.170 |
For the casting process design, I determined several key parameters. The casting shrinkage coefficient \(K\) is defined as:
$$K = \frac{L_M – L_J}{L_J} \times 100\% \tag{1}$$
where \(L_M\) is the pattern dimension and \(L_J\) is the casting dimension. According to Table 3, for medium-small gray iron castings, I set the free shrinkage to 1% and the hindered shrinkage to 0.9%. I selected 1% as the shrinkage allowance. The machining allowance was set to 2 mm based on the standard GB/T6414-1999. The minimum cast hole diameter was chosen as 50 mm for small-batch production, with smaller holes to be machined later.
| Gray iron type | Free shrinkage (%) | Hindered shrinkage (%) |
|---|---|---|
| Small/medium | 1.0 | 0.9 |
| Medium/large | 0.9 | 0.8 |
| Extra large | 0.8 | 0.7 |
Design of Gating Systems
The gating system consists of a sprue, runner, and ingates. Different positions of the ingates lead to different filling behaviors. I designed three types of gating systems based on the ingate location: top gating, parting gate (middle injection), and bottom gating. These are illustrated conceptually in terms of their characteristics:
- Top gating: Ingates at the top facilitate filling and promote directional solidification, but may cause splashing and oxidation.
- Bottom gating: Ingates at the bottom provide smooth filling, but the bottom remains hotter, which is not favorable for feeding.
- Parting gate: Ingates at the parting line combine advantages of both, suitable for medium-wall castings.
I designed the three gating systems in 3D using UG software, ensuring a sprue well to reduce impact. The important machined surfaces were identified for later optimization.
Material Thermophysical Properties
Using ProCAST, I applied the Scheil model to calculate the thermophysical and thermodynamic parameters of HT250. The solidus and liquidus temperatures were found to be 1146 °C and 1220 °C, respectively. Figure-like trends are described as follows:
- Thermal conductivity initially rises slightly, then drops, and rises again after 700 °C. Between solidus and liquidus, it decreases; above liquidus, it increases.
- Newtonian viscosity decreases with increasing temperature.
- The solid fraction decreases with temperature, reaching zero at the liquidus.
- Enthalpy increases with temperature, with a more significant increase during the solid-to-liquid transition.
- Density decreases with temperature.
For thermodynamic parameters, the secant coefficient of thermal expansion initially remains steady, then drops sharply, later rises near 800 °C, and again drops in the mushy zone. Young’s modulus decreases with temperature. Poisson’s ratio remains constant until the mushy zone, then rises. Yield stress gradually decreases with temperature and stabilizes at the liquidus.
Pouring Process Parameters
I determined the key pouring parameters based on existing standards and empirical formulas.
Pouring Temperature
The pouring temperature must be above the liquidus to ensure fluidity. For HT250 with a main wall thickness of 18 mm, Table 4 gives the recommended range.
| Main wall thickness (mm) | Pouring temperature (°C) |
|---|---|
| 8–15 | 1390–1450 |
| 15–30 | 1370–1440 |
| 30–50 | 1350–1430 |
| ＞50 | 1270–1360 |
I selected a pouring temperature range of 1370–1440 °C.
Pouring Time
For gray cast iron, the pouring time \(t\) can be calculated using the empirical formula:
$$t = S_1 \sqrt[3]{\delta G} \tag{2}$$
where \(S_1\) is a coefficient (taken as 1.2), \(\delta\) is the main wall thickness (18 mm), and \(G\) is the pouring weight including gating (452.7 kg). This gives \(t \approx 24\) s. I verified the liquid rise speed \(v = h/t\) where \(h = 288\) mm (casting height), yielding \(v = 12\) mm/s, which exceeds the minimum requirement of 10 mm/s for wall thickness 10–40 mm. Thus, the pouring time of 24 s is acceptable.
Sand Mold Preheating Temperature
To reduce thermal shock, the sand mold is preheated. The preheating temperature \(T_{mold}\) is calculated by:
$$T_{mold} = \frac{1}{3} T_{pouring} + T_{corr} \tag{3}$$
where \(T_{corr}\) is a correction factor of 30 °C for metal molds. Substituting the pouring temperature gives a range of 430–510 °C.
Numerical Simulation Theoretical Models
The filling and solidification processes are governed by conservation laws. The following equations form the basis of my numerical simulation.
Filling Process Models
For incompressible Newtonian fluid, the continuity equation (mass conservation) is:
$$\frac{\partial u_x}{\partial x} + \frac{\partial u_y}{\partial y} + \frac{\partial u_z}{\partial z} = 0 \tag{4}$$
where \(u_x, u_y, u_z\) are velocity components. The Navier-Stokes equations (momentum conservation) are:
$$\rho \frac{\partial u_x}{\partial t} + \rho \left( u_x \frac{\partial u_x}{\partial x} + u_y \frac{\partial u_x}{\partial y} + u_z \frac{\partial u_x}{\partial z} \right) = -\frac{\partial p}{\partial x} + \rho g_x + \mu \nabla^2 u_x \tag{5}$$
$$\rho \frac{\partial u_y}{\partial t} + \rho \left( u_x \frac{\partial u_y}{\partial x} + u_y \frac{\partial u_y}{\partial y} + u_z \frac{\partial u_y}{\partial z} \right) = -\frac{\partial p}{\partial y} + \rho g_y + \mu \nabla^2 u_y \tag{6}$$
$$\rho \frac{\partial u_z}{\partial t} + \rho \left( u_x \frac{\partial u_z}{\partial x} + u_y \frac{\partial u_z}{\partial y} + u_z \frac{\partial u_z}{\partial z} \right) = -\frac{\partial p}{\partial z} + \rho g_z + \mu \nabla^2 u_z \tag{7}$$
The energy conservation equation is:
$$\rho c \frac{\partial T}{\partial t} + \rho c \left( u_x \frac{\partial T}{\partial x} + u_y \frac{\partial T}{\partial y} + u_z \frac{\partial T}{\partial z} \right) = \frac{\partial}{\partial x} \left(k \frac{\partial T}{\partial x} \right) + \frac{\partial}{\partial y} \left(k \frac{\partial T}{\partial y} \right) + \frac{\partial}{\partial z} \left(k \frac{\partial T}{\partial z} \right) + S \tag{8}$$
where \(c\) is specific heat, \(k\) is thermal conductivity, \(S\) is volumetric heat source. The volume-of-fluid (VOF) method is used to track the free surface:
$$\frac{\partial F}{\partial t} + u_x \frac{\partial F}{\partial x} + u_y \frac{\partial F}{\partial y} + u_z \frac{\partial F}{\partial z} = 0 \tag{9}$$
where \(F\) is the volume fraction.
Solidification and Defect Criteria
Heat transfer during solidification involves conduction, convection, and radiation. Fourier’s law for conduction is:
$$\rho c \frac{\partial T}{\partial t} = \lambda \left( \frac{\partial^2 T}{\partial x^2} + \frac{\partial^2 T}{\partial y^2} + \frac{\partial^2 T}{\partial z^2} \right) + Q \tag{10}$$
Convection at boundaries is described by Newton’s cooling law:
$$q = \alpha (T_f – T_w) \tag{11}$$
Radiation follows the Stefan-Boltzmann law:
$$E = \epsilon \sigma_0 T_s^4 \tag{12}$$
where \(\epsilon\) is emissivity, \(\sigma_0 = 5.67\times10^{-8} \, \mathrm{W/(m^2 K^4)}\).
For shrinkage porosity prediction, since HT250 is not a steel, I used the POROS criterion in ProCAST. This model considers micro-porosity and macro-porosity coupling. Values above 0.01 indicate macro-porosity. The critical solid fraction for pore formation is 0.7, and for feeding channel formation is 0.3.
Stress Model
The elastic-plastic model is employed. The von Mises yield criterion is:
$$\sigma_{vm} = \sqrt{ \frac{(\sigma_1 – \sigma_2)^2 + (\sigma_2 – \sigma_3)^2 + (\sigma_3 – \sigma_1)^2}{2} } \tag{13}$$
Total strain is the sum of elastic, plastic, and thermal strains:
$$\{\varepsilon\} = \{\varepsilon_e\} + \{\varepsilon_p\} + \{\varepsilon_t\} \tag{14}$$
Thermal strain increment is related to the coefficient of thermal expansion \(\alpha\) and temperature change \(dT\).
Simulation Pre-processing
I imported the 3D models of the sliding seat with each gating system into ProCAST. The mesh was generated using finite elements. The sand mold was sized at 1780 mm × 765 mm × 390 mm. A mesh size of 5 mm was used for the casting and gating system, while 20 mm was used for the sand mold to balance accuracy and computational time. The simulation parameters and boundary conditions are summarized in Table 5.
| Parameter | Value |
|---|---|
| Fill direction | Gravity, 9.8 N/s² |
| Metal material | HT250 |
| Mold material | Resin sand |
| Chill material | Steel |
| Pouring temperature | 1380 °C |
| Pouring time | 24 s |
| Mold preheat temperature | 450 °C |
| Heat transfer coefficient (casting-mold) | 500 W/(m²·K) |
| Heat transfer coefficient (casting-chill) | 2000 W/(m²·K) |
| Heat transfer coefficient (mold-chill) | 500 W/(m²·K) |
| Boundary condition (mold exterior) | Air cool, FilmCo=10, T=20 °C |
| Maximum simulation steps | 10000 |
| Initial time step | 0.001 |
Simulation Results and Analysis
Filling Process
I simulated the filling process for the three gating systems. For the top gating, at 25% fill, the metal jet from the top caused slight splashing and scouring. At 50% and 75% fill, the flow became more stable, and the mold was completely filled at 100%. The initial instability might introduce sand inclusion or surface defects.
For the parting gate, the flow was smooth from the beginning. No splashing or air entrainment was observed. The liquid level rose uniformly, indicating excellent filling performance.
For the bottom gating, the filling was also stable and smooth, with no splashing. However, the bottom remains hotter, which may affect directional solidification.
Solidification Process
For all three systems, the solidification sequence was similar. Thin walls solidified first, while thick sections lagged. At 75% solidification, the thick bottom areas remained liquid and acted as feeders, but eventually, shrinkage porosity formed on important machined surfaces as no additional liquid metal was available.
Shrinkage Porosity Prediction
Figure-like results from ProCAST showed that shrinkage porosity concentrated in the thickest regions and on the critical machined surfaces. The total shrinkage porosity volumes for top, parting, and bottom gating were 147.20 cm³, 132.58 cm³, and 136.43 cm³, respectively. Based on these results and the filling stability, I selected the parting gate system as the optimal choice.
Casting Process Optimization
To reduce the shrinkage porosity in the chosen design, I implemented several modifications:
- Added four insulating risers on the important machined surfaces to provide additional molten metal for feeding and to help with degassing and slag collection.
- Added steel chills (10–15 mm thick) on thick wall sections to accelerate cooling and promote directional solidification.
- Added exhaust holes at the highest points of the casting to facilitate gas escape and prevent gas defects.
I modeled the optimized design and reconducted the simulation.
Optimized Filling and Solidification
The optimized filling process remained smooth, with no turbulence or splashing. The metal temperature stayed above the liquidus throughout filling, ensuring complete filling without cold shuts. During solidification, the chills accelerated cooling of thick sections, while the risers remained hot and fed the casting, finally solidifying last. This achieved a directional solidification pattern.
Defect Reduction
The shrinkage porosity volume decreased significantly from 132.58 cm³ to 1.42 cm³ after optimization. This is a major improvement. The residual stress and deformation in the optimized design were predicted to be 1351.1 MPa and 2.32 mm, respectively. Table 6 compares the porosity before and after optimization.
| Condition | Shrinkage porosity volume (cm³) |
|---|---|
| Before optimization | 132.58 |
| After optimization | 1.42 |
Rapid Mold Manufacturing Using Additive and Subtractive Technologies
Traditional sand mold fabrication requires the production of a pattern, which is time-consuming and expensive, especially for single-piece or small-batch orders. To overcome this, I employed a combination of additive and subtractive manufacturing to create the sand molds directly from the 3D model, bypassing the pattern.
Equipment Selection
For the upper sand mold, which contains undercuts and complex internal channels, I used an additive manufacturing approach with a large-format 3D sand printer (model LSMP2000). This machine has a build volume of 2000 mm × 1000 mm × 800 mm and uses a binder jetting (3DP) process. The lower sand mold is simpler, with no undercuts, so I used subtractive manufacturing with a CNC milling machine (digital die-less casting precision forming machine) having a work envelope of 3000 mm × 1500 mm × 900 mm.
Sand Material Selection
I evaluated three types of silica sands by scanning electron microscopy and particle size analysis. The standard sample sand with particle size distribution primarily around 0.25 mm (70/140 mesh) was chosen because smaller grains provide better resin bonding and higher strength. The binder used was furan resin (type 200D), with a curing agent (GS3D200) and a cleaning agent (R100). Table 7 lists the properties of the resin.
| Property | Value |
|---|---|
| Appearance | Brown-red transparent solution |
| Density (20 °C) | 1.10–1.20 g/cm³ |
| Viscosity (20 °C) | 13.5–17.0 mPa·s |
| Free formaldehyde | ≤0.2% |
| pH | 6.0–7.5 |
Sand Sample Testing
I printed test specimens to evaluate the mechanical and physical properties of the 3D-printed sand. The tensile, compressive, and shear strengths were measured using a sand strength machine, with five samples each, discarding the highest and lowest values. Table 8 shows the average results.
| Property | Average value (MPa) | Requirement (MPa) |
|---|---|---|
| Tensile strength | 2.465 | ＞1.5 |
| Compressive strength | 4.093 | ＞3.5 |
| Shear strength | 1.820 | ＞1.5 |
I also tested the permeability and gas evolution using an intelligent permeability tester and a gas evolution tester. The results are given in Table 9.
| Property | Average value | Requirement |
|---|---|---|
| Permeability | 160.8 | 160 |
| Gas evolution (ml/g) | 10.0 | ＜12 |
All measured properties met the required standards, confirming the suitability of the selected materials.
Mold Design and Manufacturing
I designed the mold with a vertical parting line. The sand mold dimensions are 1780 mm × 765 mm × 389 mm. The upper mold was printed using the 3DP process. The 3D model was processed in Materialise Magics, oriented in the build box, and sliced with a layer thickness of 0.3 mm. The printing parameters are listed in Table 10.
| Parameter | Value |
|---|---|
| Layer thickness (mm) | 0.3 |
| Printing speed (mm/s) | 350 |
| Re-coater speed (mm/s) | 350 |
| Total layers | 951 |
| Estimated print time (h) | 6.56 |
After printing, the upper mold was left to cure and then removed from the build box. The lower mold was CNC machined from a preformed sand block. I used three cutting tools: D50 for facing and roughing, D20 for intermediate machining, and B8 ball-end cutter for finishing. The CNC program was generated using UG CAM, and the machining process was monitored through the control system. The total manufacturing time for both molds was approximately one week, compared to two weeks or more for traditional pattern-based methods, significantly reducing lead time and cost.
After machining, I cleaned the surfaces, applied a refractory coating, and assembled the upper and lower molds. The assembled mold was ready for pouring. This hybrid additive-subtractive approach proved to be efficient, cost-effective, and environmentally friendlier than traditional mold making.
Further Optimization of Pouring Parameters via Orthogonal Experiments
Although the optimized gating system reduced defects dramatically, I aimed to further improve the casting quality by optimizing the pouring parameters. The key parameters are pouring temperature, pouring time, and sand mold preheating temperature. I studied their individual effects and then employed an orthogonal experimental design to find the optimal combination.
Effect of Individual Parameters
I varied one parameter at a time while keeping others constant and observed the shrinkage porosity, residual stress, and deformation.
- Pouring temperature: As the temperature increased from 1360 °C to 1440 °C, shrinkage porosity generally decreased. The residual stress first increased slightly, then decreased, then increased again. Deformation first increased then decreased.
- Pouring time: Shrinkage porosity showed a fluctuating trend with pouring time. Residual stress also varied non-monotonically. Deformation increased initially, then decreased, then increased again.
- Mold preheating temperature: Shrinkage porosity decreased initially, then increased, then decreased. Residual stress decreased continuously with higher preheating temperature. Deformation increased with preheating temperature.
These trends highlighted the complex interactions among the parameters.
Orthogonal Experiment Design
I selected three factors and five levels each, as shown in Table 11. The orthogonal array \(L_{25}(5^3)\) was used, leading to 25 simulation experiments. The evaluation indices were shrinkage porosity volume, maximum residual stress, and maximum deformation.
| Level | Pouring temperature A (°C) | Pouring time B (s) | Mold preheating C (°C) |
|---|---|---|---|
| 1 | 1360 | 20 | 430 |
| 2 | 1380 | 22 | 450 |
| 3 | 1400 | 24 | 470 |
| 4 | 1420 | 26 | 490 |
| 5 | 1440 | 28 | 510 |
Table 12 presents the experimental arrangement and results for each of the 25 runs.
| Run | A (°C) | B (s) | C (°C) | Shrinkage porosity (cm³) | Residual stress (MPa) | Deformation (mm) |
|---|---|---|---|---|---|---|
| 1 | 1360 | 20 | 430 | 1.89 | 1446.0 | 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 |
Range Analysis
I performed a range analysis for each evaluation index. The results are summarized in Table 13.
| Index | Factor | K1 | K2 | K3 | K4 | K5 | Range R | Optimal level |
|---|---|---|---|---|---|---|---|---|
| Shrinkage porosity (cm³) | A | 1.784 | 1.566 | 1.206 | 1.206 | 1.012 | 0.772 | A5 |
| B | 1.400 | 1.362 | 1.382 | 1.292 | 1.400 | 0.108 | B4 | |
| C | 1.404 | 1.380 | 1.314 | 1.430 | 1.308 | 0.122 | C5 | |
| Residual stress (MPa) | A | 1347.5 | 1349.0 | 1325.9 | 1293.1 | 1311.5 | 55.9 | A4 |
| B | 1326.2 | 1330.2 | 1325.2 | 1315.2 | 1330.3 | 15.1 | B4 | |
| C | 1374.5 | 1371.8 | 1339.0 | 1311.0 | 1230.7 | 143.8 | C5 | |
| Deformation (mm) | A | 2.222 | 2.184 | 2.232 | 2.222 | 2.216 | 0.048 | A2 |
| B | 2.218 | 2.203 | 2.204 | 2.250 | 2.202 | 0.048 | B5 | |
| C | 2.102 | 2.160 | 2.228 | 2.264 | 2.322 | 0.220 | C1 |
For shrinkage porosity, the order of influence is A > C > B, with A5B4C5 being optimal (pouring temperature 1440 °C, pouring time 26 s, preheating 510 °C). For residual stress, the order is C > A > B, with A4B4C5 being optimal (1420 °C, 26 s, 510 °C). For deformation, the order is C > B > A, with A2B5C1 being optimal (1380 °C, 28 s, 430 °C).
Matrix Analysis
To reconcile the conflicting optimal conditions from individual indices, I used the matrix analysis method. This method constructs an indicator layer matrix \(M_i\), a factor layer matrix \(T_i\), and a level layer matrix \(E_i\), and then calculates the weighted combination.
For the indicator layer, since smaller values are better, I set \(k_{ij} = 1/K_{ji}\). Then \(M_i\) is constructed with ones and \(k_{ij}\) values. The factor layer \(T_i\) uses \(t_i = 1/\sum_{j=1}^{n} K_{ji}\). The level layer \(E_i\) uses \(r_i = R_i/\sum_{j=1}^{m} R_j\). The weight matrix for the \(i\)-th indicator is:
$$\omega_i = M_i \, T_i \, E_i \tag{15}$$
After computing \(\omega_1, \omega_2, \omega_3\) for the three indicators, I summed them to obtain a comprehensive weight matrix \(\omega_{total}\). The largest value in each factor column indicates the best level. The results are shown in Table 14.
| Factor level | A (pouring temperature) | B (pouring time) | C (preheating temperature) |
|---|---|---|---|
| Level 1 | 0.0582 | 0.0230 | 0.1052 |
| Level 2 | 0.0635 | 0.0232 | 0.1039 |
| Level 3 | 0.0764 | 0.0231 | 0.1038 |
| Level 4 | 0.0768 | 0.0234 | 0.1032 |
| Level 5 | 0.0872 | 0.0230 | 0.1057 |
| Optimal level | A5 (1440 °C) | B4 (26 s) | C5 (510 °C) |
Both the range analysis (for shrinkage porosity) and the matrix analysis (comprehensive) indicate the same optimal combination: A5B4C5, i.e., pouring temperature = 1440 °C, pouring time = 26 s, and preheating temperature = 510 °C.
Verification Simulation
I performed a verification simulation using the optimal parameters. The results are compared with the pre-optimization values in Table 15.
| 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 |
The shrinkage porosity volume was reduced from 1.42 cm³ to 1.00 cm³, the maximum residual stress decreased from 1351.1 MPa to 1178.7 MPa, and the maximum deformation was reduced from 2.32 mm to 2.22 mm. This confirms that the optimized process parameters further improve the quality of the sliding seat casting.
Conclusion
In this study, I successfully combined numerical simulation with additive-subtractive manufacturing to optimize and produce a machine tool sliding seat sand casting. The main conclusions are:
- Through casting process design, I determined HT250 as the material, set the pouring temperature range of 1370–1440 °C, a pouring time of 24 s, and a mold preheating temperature of 430–510 °C.
- ProCAST simulations of three gating systems revealed that the parting gate system offered the best filling stability and the lowest shrinkage porosity (132.58 cm³). After adding chills, risers, and vent holes, the shrinkage porosity volume was drastically reduced to 1.42 cm³.
- The hybrid additive-subtractive mold manufacturing approach, using 3D printing for the upper mold and CNC milling for the lower mold, reduced production time from about two weeks to one week and lowered costs, while meeting all required sand properties (strength, permeability, gas evolution).
- Orthogonal experiments and matrix analysis identified the optimal pouring parameters: pouring temperature 1440 °C, pouring time 26 s, and preheating temperature 510 °C. This further reduced shrinkage porosity to 1.00 cm³, residual stress to 1178.7 MPa, and deformation to 2.22 mm.
This research demonstrates that the integration of casting simulation, process optimization, and digital sand mold manufacturing offers significant advantages for producing high-quality sand casting parts, especially for single-piece and small-batch production of machine tool components. The methodology provides a practical reference for the digital transformation of traditional casting processes.