In the field of modern manufacturing, the integration of additive manufacturing with conventional foundry technology has opened up new avenues for producing high-quality metal components. As a researcher focusing on material processing engineering, I have dedicated my work to investigating the optimization of steel casting processes using 3D sand mold printing. This technology, also known as binder jetting for sand molds, offers exceptional design freedom, low production costs for small batches, and the ability to fabricate complex geometries that are difficult or impossible to achieve with traditional sand mold making. The present study aims to optimize both the printing parameters of furan resin sand and the subsequent casting process design for a stainless steel gas meter housing. By combining response surface methodology with numerical simulation using ProCAST, I systematically improved the quality of steel castings produced via 3D printed sand molds.
1. Introduction and Research Background
Sand mold 3D printing is an indirect additive manufacturing technique. Unlike direct metal additive manufacturing, it builds a sand mold layer by layer, which is then used for conventional casting. The main advantages include shortened development cycles, reduced tooling costs, and the ability to produce complex sand cores and molds without pattern equipment. In the context of steel casting, this technology is particularly beneficial because steel castings often require precise gating systems, controlled solidification, and intricate internal features. My research focuses on a natural gas meter housing made of 0Cr17Ni12Mo2 stainless steel (equivalent to 316 stainless steel). The part has thin walls, varying cross-sections, and a flange structure with high requirements for strength and tightness. Therefore, the design of the casting process is critical to avoid defects such as shrinkage porosity, cold shuts, and misruns.
The objective of this study is twofold:
- To optimize the 3D printing parameters (layer thickness, furan resin content, curing agent content) of the sand mold using response surface methodology (RSM), ensuring that the mold possesses adequate tensile strength and low gas evolution.
- To design and simulate the casting process for the stainless steel gas meter housing using ProCAST, compare different gating systems, and optimize the process to minimize shrinkage porosity.
2. Experimental Materials and Methods
2.1 Materials
The sand used for 3D printing is a special silica sand designed for binder jetting. Its properties are listed in Table 1. The sand has a uniform particle size, low acid demand, and low loss on ignition, which are beneficial for consistent printing.
| Property | Value |
|---|---|
| Moisture content / % | 0.08 |
| Clay content / % | 0.12 |
| Acid demand / % | 3.1 |
| Loss on ignition / % | 0.15 |
| Bulk density / g·cm⁻³ | 1.47 |
The binder used is furan resin, whose typical properties are presented in Table 2. The curing agent is p-toluenesulfonic acid, with a density of about 1.2 g/cm³ and a total acidity of 12%.
| Property | Value |
|---|---|
| Water content / % | 3.47 |
| Nitrogen content / % | 0.02 |
| Density / (g/cm³) | 1.151 |
| Viscosity at 20°C / (MPa·s) | 19 |
| Free formaldehyde / % | 0.09 |
2.2 3D Printing Equipment
The experiments were conducted on an HX1000 sand mold 3D printer. The machine has a build volume of 1000 mm × 800 mm × 600 mm. It uses a layer thickness adjustable between 0.25 mm and 0.4 mm. The binder content can be set as a percentage of sand weight (0.5%–2%), and the curing agent content can be adjusted in the range of 2‰–4‰ of sand weight. The printing process begins with mixing the sand and curing agent in a mixing chamber, then the mixed sand is transported to a hopper and spread by a roller. The printhead selectively deposits furan resin onto the sand bed according to the sliced CAD model. After printing, the mold is allowed to cure naturally for 24 hours at room temperature.
2.3 Testing Methods
2.3.1 Surface Morphology Examination
Scanning electron microscopy (SEM) was used to observe the sand grain morphology and the bonding bridges between grains.
2.3.2 Tensile Strength Test
Standard “8”-shaped tensile specimens were printed according to GB/T 2684-2009. After curing for 24 h, the specimens were tested using an XQY-II intelligent sand strength machine. The maximum load was recorded, and the tensile strength was calculated. For each parameter combination, eight specimens were tested and the average value was taken.
2.3.3 Gas Evolution Measurement
A GET-III intelligent gas evolution tester was used to measure the amount of gas released when a 1.00 g sample of the sand mold is heated to 850°C. The sample was taken from the broken tensile specimens. Six measurements were performed for each condition and averaged.
3. Response Surface Optimization of 3D Printing Parameters
3.1 Single-Factor Experiments
Before conducting the response surface design, single-factor experiments were performed to determine the central values. Three factors were investigated: layer thickness (A), furan resin content (B), and curing agent content (C).
3.1.1 Effect of Layer Thickness
With a constant curing agent content of 3.5‰ and resin content of 2%, the layer thickness was varied from 0.30 mm to 0.40 mm in increments of 0.05 mm. The results are shown in Figure 1 (not reproduced here). As the layer thickness increased, the tensile strength decreased from 0.993 MPa to 0.626 MPa. This is because thicker layers reduce the total resin content in the mold and result in lower density, weakening the bonding between sand grains. Conversely, the gas evolution decreased from 12.9 mL/g to 10.8 mL/g with increasing layer thickness, because less resin is present to decompose into gas at high temperature. Based on these results, 0.35 mm was chosen as the central value for the response surface experiment.
3.1.2 Effect of Furan Resin Content
With a layer thickness of 0.35 mm and curing agent content of 3.5‰, the resin content was varied from 1% to 2% in steps of 0.5%. The tensile strength increased from 0.714 MPa to 1.011 MPa, while the gas evolution increased from 10.1 mL/g to 11.9 mL/g. Higher resin content improves the bonding bridges among sand particles, but also increases gas evolution. Therefore, a central value of 1.5% was selected as a compromise.
3.1.3 Effect of Curing Agent Content
Fixing the layer thickness at 0.35 mm and resin content at 1.5%, the curing agent content was varied from 3‰ to 4‰. The tensile strength increased from 0.715 MPa to 0.731 MPa, and the gas evolution rose from 8.3 mL/g to 12.2 mL/g. The increase in gas evolution is significant (46%), mainly due to decomposition of the curing agent producing SO₂ and H₂S. The central value was set to 3.5‰.
3.2 Response Surface Experimental Design
Using a Box-Behnken design, a three-factor, three-level experiment was constructed. The factors and levels are shown in Table 3.
| Factor | Level -1 | Level 0 | Level 1 |
|---|---|---|---|
| Layer thickness / mm | 0.3 | 0.35 | 0.4 |
| Resin content / % | 1.0 | 1.5 | 2.0 |
| Curing agent content / ‰ | 3.0 | 3.5 | 4.0 |
Two responses were measured: tensile strength (Y₁) and gas evolution (Y₂). The complete experimental matrix is shown in Table 4.
| Run | A: Layer (mm) | B: Resin (%) | C: Curing (‰) | Tensile strength (MPa) | Gas evolution (mL/g) |
|---|---|---|---|---|---|
| 1 | -1 | -1 | 0 | 0.682 | 10.8 |
| 2 | 1 | -1 | 0 | 0.535 | 9.4 |
| 3 | -1 | 1 | 0 | 1.071 | 13.6 |
| 4 | 1 | 1 | 0 | 0.518 | 12.3 |
| 5 | -1 | 0 | -1 | 0.712 | 8.8 |
| 6 | 1 | 0 | -1 | 0.533 | 7.8 |
| 7 | -1 | 0 | 1 | 1.011 | 14.2 |
| 8 | 1 | 0 | 1 | 0.468 | 11.9 |
| 9 | 0 | -1 | -1 | 0.426 | 8.6 |
| 10 | 0 | 1 | -1 | 0.586 | 8.1 |
| 11 | 0 | -1 | 1 | 0.679 | 11.8 |
| 12 | 0 | 0 | 0 | 0.754 | 9.9 |
| 13 | 0 | 0 | 0 | 0.714 | 11.9 |
| 14 | 0 | 0 | 0 | 0.634 | 12.7 |
| 15 | 0 | 0 | 0 | 0.628 | 11.9 |
| 16 | 0 | 0 | 0 | 0.681 | 12.4 |
| 17 | 0 | 0 | 0 | 0.637 | 11.5 |
3.3 Regression Models and Variance Analysis
Using multiple regression, the following second-order polynomial equations were obtained for tensile strength (Y₁) and gas evolution (Y₂):
$$Y_1 = -0.66 – 0.18A + 0.076B + 0.082C – 0.10AB – 0.091AC – 0.021BC + 0.056A^2 – 0.014B^2 – 0.034C^2 \tag{1}$$
$$Y_2 = 10.72 – 1.08A + 1.21B + 0.36C + 0.075AB + 0.025AC – 1.05BC – 0.54A^2 + 0.44B^2 – 0.61C^2 \tag{2}$$
The analysis of variance (ANOVA) for the tensile strength model is summarized in Table 5.
| Source | Sum of squares | df | Mean square | F-value | p-value | Significance |
|---|---|---|---|---|---|---|
| Model | 0.45 | 9 | 0.050 | 28.01 | 0.0001 | ** |
| A | 0.25 | 1 | 0.25 | 142.67 | <0.0001 | ** |
| B | 0.046 | 1 | 0.046 | 26.00 | 0.0014 | ** |
| C | 0.054 | 1 | 0.054 | 30.27 | 0.0009 | ** |
| AB | 0.041 | 1 | 0.041 | 23.26 | 0.0019 | ** |
| AC | 0.033 | 1 | 0.033 | 18.70 | 0.0035 | ** |
| BC | 1.81×10⁻⁴ | 1 | 1.81×10⁻⁴ | 1.02 | 0.3463 | – |
| A² | 0.016 | 1 | 0.013 | 7.49 | 0.0291 | * |
| B² | 7.82×10⁻⁴ | 1 | 7.82×10⁻⁴ | 1.12 | 0.5278 | – |
| C² | 4.90×10⁻⁴ | 1 | 4.90×10⁻³ | 2.77 | 0.1401 | – |
| Residual | 0.012 | 7 | 1.77×10⁻³ | |||
| Lack of fit | 6.77×10⁻³ | 3 | 2.26×10⁻³ | 1.61 | 0.3212 | not significant |
| Pure error | 5.62×10⁻³ | 4 | 1.41×10⁻⁴ | |||
| Total | 0.46 | 16 | ||||
| R² = 0.9730, adjusted R² = 0.9382 | ||||||
The model is highly significant (p < 0.001), and the lack of fit is not significant (p = 0.3212), proving that the model adequately describes the relationship. The order of factor influence on tensile strength is: layer thickness > curing agent content > resin content.
The ANOVA for the gas evolution model is presented in Table 6.
| Source | Sum of squares | df | Mean square | F-value | p-value | Significance |
|---|---|---|---|---|---|---|
| Model | 30.02 | 9 | 3.34 | 68.57 | < 0.0001 | ** |
| A | 9.24 | 1 | 9.24 | 190.06 | < 0.0001 | ** |
| B | 11.76 | 1 | 11.76 | 241.79 | < 0.0001 | ** |
| C | 1.05 | 1 | 1.05 | 21.61 | 0.0023 | ** |
| AB | 0.023 | 1 | 0.023 | 0.46 | 0.5183 | – |
| AC | 2.50×10⁻³ | 1 | 2.50×10⁻³ | 0.051 | 0.8271 | – |
| BC | 4.41 | 1 | 4.41 | 90.66 | < 0.0001 | ** |
| A² | 1.21 | 1 | 1.21 | 24.78 | 0.0016 | * |
| B² | 0.82 | 1 | 0.82 | 16.76 | 0.0046 | * |
| C² | 1.57 | 1 | 1.57 | 32.21 | 0.0008 | ** |
| Residual | 0.34 | 7 | 0.049 | |||
| Lack of fit | 0.23 | 3 | 0.077 | 2.87 | 0.1673 | not significant |
| Pure error | 0.11 | 4 | 0.027 | |||
| Total | 30.86 | 16 | ||||
| R² = 0.9888, adjusted R² = 0.9744 | ||||||
The gas evolution model is also highly significant, with the order of influence: resin content > layer thickness > curing agent content. The interaction between resin content and curing agent content (BC) is significant.
3.4 Interaction Effects
The response surface plots for tensile strength (not shown here) show that the effect of layer thickness is the most dominant. The curvature of the response surface indicates that the interactions AB and AC are significant, while BC is weak.
For gas evolution, the interaction BC is prominently significant. This is because higher resin content with higher curing agent content increases the amount of volatile decomposition products. The other interactions (AB and AC) are negligible.
3.5 Optimization and Verification
Using the developed regression models, I performed a numerical optimization to maximize tensile strength and minimize gas evolution. The optimal parameters obtained are: layer thickness = 0.3 mm, resin content = 1.6%, and curing agent content = 4‰. The corresponding predicted values are a tensile strength of 1.071 MPa and a gas evolution of 11.039 mL/g. Since the printer can only adjust the resin content in discrete steps, a slight adjustment to 1.6% was made. Verification experiments were conducted, and the results are shown in Table 7.
| No. | Layer thickness (mm) | Resin (%) | Curing (‰) | Tensile strength (MPa) | Gas evolution (mL/g) |
|---|---|---|---|---|---|
| 1 | 0.3 | 1.6 | 4 | 1.063 | 11.0 |
| 2 | 0.3 | 1.6 | 4 | 1.049 | 11.3 |
| 3 | 0.3 | 1.6 | 4 | 1.050 | 11.2 |
| 4 | 0.3 | 1.6 | 4 | 1.058 | 10.9 |
| Average | 1.055 | 11.1 |
The measured average tensile strength is 1.055 MPa, which is close to the predicted value of 1.071 MPa. The gas evolution is 11.1 mL/g, also in good agreement with the predicted 11.039 mL/g. This confirms the accuracy and reliability of the response surface models. Therefore, I adopted these optimized parameters for subsequent steel casting experiments and simulations.
4. Casting Process Design for the Stainless Steel Gas Meter Housing
4.1 Part Analysis
The target component is a gas meter housing made of 0Cr17Ni12Mo2 stainless steel. Its geometry includes thin walls (about 4 mm), thicker sections up to 15 mm at the flange, and a central partition plate with four holes. The part has a complex structure with three bosses on the side, one of which is a flange. This complexity makes conventional sand molding difficult due to the need for multiple cores. Sand mold 3D printing can produce the mold as a single piece without core assembly, improving dimensional accuracy and reducing manufacturing time.
Some key properties of the stainless steel are given in Table 8.
| Element/Property | Value |
|---|---|
| C / % | ≤0.08 |
| Si / % | ≤1.00 |
| Mn / % | ≤2.00 |
| P / % | ≤0.035 |
| S / % | ≤0.03 |
| Ni / % | 10–14 |
| Cr / % | 16.0–18.5 |
| Mo / % | 2.0–3.0 |
| Ultimate tensile strength / MPa | ≥520 |
| Yield strength / MPa | ≥205 |
| Elongation / % | ≥40 |
| Density / (g/cm³) | 7.98 |
| Specific heat (20°C) / J/(g·K) | 0.502 |
4.2 Gating System Design
Because 316 stainless steel has high melting point, poor fluidity, and is prone to oxidation, a closed-open gating system was selected. A circular runner cross-section was adopted because it minimizes heat loss and provides smooth flow. Using the cross-sectional ratio method, the recommended area ratio for the sprue, runner, and ingate was chosen as:
$$A_{\text{sprue}} : A_{\text{runner}} : A_{\text{ingate}} = 11 : 9 : 10 \tag{3}$$
The ingate area was calculated by:
$$A_{\text{ingate}} = \frac{G}{\mu \rho \tau \sqrt{2gH_p}} \tag{4}$$
where \(G\) is the mass of molten metal flowing through the gating system, \(\rho\) is the density of the metal, \(\tau\) is the pouring time, \(\mu\) is the flow coefficient, and \(H_p\) is the average pressure head.
After calculation, the final dimensions of the gating system were set as: sprue radius 15 mm, runner radius 12 mm, and ingate radius 13 mm. Three different pouring schemes were designed:
- Scheme A: single-side ingate injection
- Scheme B: bilateral ingate injection
- Scheme C: reverse bilateral ingate injection
These schemes were aimed at studying the effect of filling patterns on defect formation in steel casting.
4.3 Simulation Setup in ProCAST
The 3D model was created in NX 12.0 and exported as a Parasolid file. The model was then meshed with triangular surface elements of 3 mm for the casting and 4 mm for the mold. Fine local meshing was applied to thin-walled regions to ensure accuracy. The material properties of 0Cr17Ni12Mo2 and furan resin sand were defined in the ProCAST database. Figure 4-5 in the original thesis showed the solid fraction, enthalpy, and density as functions of temperature. I used the lever rule to compute these properties.
The pouring temperature was set to 1560 °C, and the initial mold temperature was 25 °C. The heat transfer coefficient at the casting-mold interface was set to 1000 W·m⁻²·K⁻¹, and at the casting-chill interface to 2000 W·m⁻²·K⁻¹. The filling time was calculated as 4 s, with a mass flow rate corresponding to the sprue inlet. The Niyama criterion was used to predict shrinkage porosity.
The governing equations for fluid flow and heat transfer in the simulation are:
$$\frac{\partial u}{\partial x} + \frac{\partial v}{\partial y} + \frac{\partial w}{\partial z} = 0 \tag{5}$$
$$\rho \left( \frac{\partial \mathbf{v}}{\partial t} + (\mathbf{v} \cdot \nabla) \mathbf{v} \right) = \mathbf{F} – \nabla p + \mu \Delta \mathbf{v} \tag{6}$$
$$\rho c \frac{\partial T}{\partial t} = \nabla \cdot (k \nabla T) + Q \tag{7}$$
where \(u, v, w\) are velocity components, \(\mathbf{v}\) is the velocity vector, \(p\) is pressure, \(T\) is temperature, \(k\) is thermal conductivity, and \(Q\) is the heat source including latent heat.
4.4 Simulation Results and Comparison of Schemes
4.4.1 Filling Behavior
Scheme A (single-side injection) exhibited rapid filling but with significant unevenness. At 2 s, there was a noticeable reverse flow region in the center, increasing the risk of cold shuts. The metal flow on both sides of the cavity was unbalanced.
Scheme B (bilateral injection) showed uniform filling with the metal advancing symmetrically. The liquid level remained almost constant on both sides, and the speed was controlled between 0.5 and 0.8 m/s in critical sections. There was no misrun or cold shut observed.
Scheme C (reverse bilateral injection) filled the flange first, which caused turbulence in the boss region. The flow velocity varied significantly within the same cross-section, potentially leading to gas entrapment and oxide inclusions.
4.4.2 Solidification Process
Scheme A solidified quickly but with non-uniform solidification front. A hot spot appeared at the lower thick section, which led to isolated liquid regions and possible shrinkage porosity.
Scheme B exhibited progressive and uniform solidification from the thin walls toward the thicker sections. The total solidification time was about 147 s, and the temperature distribution was consistent. The final solidified region was in the risers, which is desirable for feeding.
Scheme C had a slower solidification rate, and the temperature remained high near the ingates, causing the formation of isolated liquid pockets. This could promote porosity and misrun defects.
4.4.3 Defect Prediction
Using the Niyama criterion, the shrinkage porosity volume for each scheme was calculated. The results are summarized in Table 9.
| Parameter | Scheme A | Scheme B | Scheme C |
|---|---|---|---|
| Total defect volume / cc | 3.533 | 3.445 | 2.476 |
| Shrinkage porosity volume / cc | 0.559 | 0.391 | 0.499 |
| Density / g·cc⁻¹ | 1.20 | 1.20 | 1.20 |
| Weight / mg | 0.671 | 0.469 | 0.599 |
Scheme A has the highest shrinkage porosity volume (0.559 cc) and is widely distributed. Scheme C has better total volume but defects are concentrated on the working surfaces. Scheme B has the lowest shrinkage porosity (0.391 cc) and the defects are localized in the flange area, making them easier to eliminate with risers. Therefore, Scheme B was selected as the initial process for further optimization.
4.5 Process Optimization with Risers and Chills
To reduce the shrinkage porosity in the flange area of Scheme B, I designed two open-top cylindrical risers. The riser modulus was calculated using the modulus method:
$$M = \frac{V}{A} \tag{8}$$
The modulus of the riser was found to be 3.8 mm. Based on the thermal center circle method, the riser diameter was set to 29 mm, and the riser height was calculated as:
$$H = 1.15 \times D \approx 35 \ \text{mm} \tag{9}$$
Both the top and bottom edges of the risers were rounded with a radius of 4 mm. The risers were placed on the top surface of the flange to feed the hot spot during solidification.
In addition, a steel chill was placed at the bottom thick section of the casting to accelerate its solidification and promote directional solidification. The chill geometry was designed to match the local contour of the part.
4.6 Casting Process Parameters
Key casting process parameters were designed according to the casting handbook. The linear dimensional tolerance grade was CT13 with a tolerance value of 10 mm. The weight tolerance was M13, corresponding to 24%. The machining allowance was set to 9 mm. For the flange area, a process correction allowance of 2 mm was added to compensate for solidification shrinkage.
4.7 Simulation Results after Optimization
After adding risers and chill, the casting process was re-simulated.
4.7.1 Filling Process after Optimization
The optimized filling process showed more uniform velocities. The initial filling speed through the ingates was around 0.7 m/s, then gradually decreased to 0.5 m/s as the metal rose. No visible height difference between the two sides was observed, indicating balanced filling.
4.7.2 Solidification after Optimization
The solidification sequence became more favorable. Because of the chill, the lower thick section solidified earlier, which helped to avoid isolated liquid regions. The risers effectively delayed the solidification of the flange, allowing molten metal to feed the solidifying shrinkage. The total solidification time remained similar, but the temperature gradients were more uniform. The last solid regions were within the risers, which is ideal for eliminating internal defects in the steel casting.
4.7.3 Defect Reduction
The shrinkage porosity distribution after optimization is shown in the original thesis. The internal porosity of the casting was reduced to 0.0008 cc, while the porosity in the riser was 0.073 cc. Compared to the unoptimized Scheme B (0.391 cc), the defect volume decreased remarkably by more than 99%. The residual micro-porosity is negligible and located in non-critical areas.
Table 10 summarizes the defect volume comparison.
| Condition | Shrinkage porosity volume / cc |
|---|---|
| Scheme B (unoptimized) | 0.391 |
| After adding risers and chill (casting) | 0.0008 |
| After adding risers and chill (risers) | 0.073 |
5. Discussion
The combination of response surface optimization and numerical simulation has proven to be an effective approach for improving the quality of steel castings produced by 3D sand mold printing. The optimized printing parameters ensure that the sand mold has sufficient strength to withstand the molten steel during casting and low gas evolution to minimize gas-related defects. The process simulation allowed me to compare different gating designs without conducting costly and time-consuming physical trials. The use of bilateral ingate injection together with properly sized risers and a chill resulted in a near defect-free steel casting.
One important observation from the simulations is that the filling pattern has a significant influence on the temperature distribution and hence on the solidification behavior of steel castings. Uneven filling can lead to localized overheating and premature solidification, creating hot spots and isolated liquid regions. The bilateral injection method provided symmetric filling, which promoted uniform temperature fields and progressive solidification.
Riser design is critical in steel casting because the solidification shrinkage of steel is large. The modulus-based calculation ensured that the risers would solidify after the casting, allowing for effective feeding. The chill further promoted directional solidification by accelerating the cooling rate in thick sections. As a result, the shrinkage porosity was eliminated almost completely.
The response surface models not only helped in finding the optimal printable sand mixture but also revealed the relative importance of each parameter. For steel casting, the mold must withstand high thermal and mechanical stresses; hence, tensile strength is of prime importance. Gas evolution is equally critical because excessive gas can cause blowholes in the casting. The optimal balance was achieved at a layer thickness of 0.3 mm, resin content of 1.6%, and curing agent content of 4‰.
The integration of 3D printing with simulation offers a digital thread from mold design to final product. In the future, this approach can be extended to other types of steel castings, such as pump impellers, valve bodies, and structural components. By leveraging the design freedom of sand mold 3D printing, novel gating systems and mold geometries can be explored to further enhance the quality and performance of steel castings.
6. Conclusions
The main conclusions of this research are summarized as follows:
- Through single-factor experiments and response surface methodology, the optimal 3D printing parameters for furan resin sand mold were determined: layer thickness of 0.3 mm, furan resin content of 1.6% of sand weight, and curing agent content of 4‰ of sand weight. The corresponding tensile strength and gas evolution were measured as 1.055 MPa and 11.1 mL/g, respectively, which were consistent with the predicted values.
- The regression models for tensile strength and gas evolution were established with high accuracy (R² = 0.973 and 0.989, respectively). The significance order of factors for tensile strength is layer thickness > curing agent content > resin content, while for gas evolution it is resin content > layer thickness > curing agent content.
- Three gating schemes for the stainless steel gas meter housing were designed and simulated: single-side injection, bilateral injection, and reverse bilateral injection. The bilateral injection scheme produced the most uniform filling and progressive solidification, with the lowest shrinkage porosity volume of 0.391 cc.
- Process optimization with two open-top risers and a steel chill significantly reduced defects in the steel casting. The optimized casting had an internal shrinkage porosity of only 0.0008 cc, while the riser contained 0.073 cc of porosity, representing a reduction of over 99% compared to the unoptimized condition.
- The combination of 3D sand mold printing and numerical simulation provides an efficient and reliable route for manufacturing complex steel castings with minimized defects, reduced lead time, and lower cost.
This study demonstrates that the careful optimization of both mold printing parameters and casting process parameters is essential for achieving high-quality steel castings. The methodology presented here can be readily adapted to other types of steel castings and complex components, contributing to the advancement of intelligent and green foundry manufacturing.