Simulation of Joule Heating-based Core Drying

Simulation of Joule heating-based Core Drying

This article was contributed by Eric Riedel ^1,2

¹Otto-von-Guericke-University Magdeburg, Institute of Manufacturing Technology and Quality Management, Germany

²Soplain GmbH, Germany

Modern casting production requires the use of sand cores. Growing environmental awareness as well as tougher regulations have supported the development of inorganic, emission-free binder systems, in which the cores are dried and cured by heat. In what is known as the hot box process, heat is generated in the core boxes and transferred to the sand binder mixture. However, the hot box process exhibits two major technological disadvantages.

The first disadvantage is the very low thermal conductivity of quartz sand of about 1 W/(m·K). Due to outside-in heat transfer, the process is time-consuming, can lead to shell formation and thus quality issues. For this reason, very high core-box temperatures of up to 523.15 K or more are applied to accelerate the heat transfer. The second disadvantage of the hot box process is that the core drying itself cannot be directly measured and digitized in real time. Instead, it can only be measured passively by recording peripheral parameters, such as from the core box.

The ACS Process

The new, patented Advanced Core Solution (ACS) process aims for time- and energy-efficient core drying and curing. The ACS process uses a property common to all inorganic binder systems: because they are water-based, they are electrically conductive. The key factor is the development of electrically conductive core box materials, whose conductivity can be adjusted to that of the sand-binder mixture. When a voltage is applied, the electrical current flows uniformly through the core box and sand-binder mixture, as demonstrated in Figure 1. Put more precisely, current flows through the electrically conductive binder bridges between the sand grains. Due to its inherent electrical resistance, the sand core heats uniformly without shell formation. The scientific principle behind it, called Joule heating, is based on Joule’s first law. In the series process, the electrically conductive core box heats up through Joule heating as well, additionally accelerating the drying process. This is a further important advantage, since for the ACS process, no complicated heating devices within the core boxes are required anymore, thus simplifying core box construction.

With this new process, and for the first time, heat is generated directly where it is needed: within the core. Since the necessary heat is generated through the homogeneously-distributed binder and transferred to the adjoining sand, the low thermal conductivity of the quartz sand is no longer a limiting process factor. Additionally, for the first time, the recording of drying-specific electrical parameters allows for comprehensive real-time monitoring of the drying process itself. Using FLOW-3D, the ACS process can be simulated, fulfilling an important criterion for industrial application, including the quantification of process benefits.

Model Description

The modeling is based on the work of Starobin et al. [1], but extends it with the Electro-mechanics model in FLOW-3D. Activating the electric potential (iepot = 1), takes electro-thermal effects, i.e., Joule heating (iethermo = 1), into account. Model details can be taken from [2]. Via the electrical properties of the components, the core box is assigned a dynamic potential (ioepotm = 1) with an electrical conductivity (oecond) and, if necessary, a dielectric potential (odiel); the same applies to the sand core in order to account for electrical conductivity of the entire sand-binder mixture. The electrodes are assigned a fixed potential (ioepotm = 0), an electrical conductivity, and a negative electric potential (oepot) for one electrode and a positive electric potential for the other. Since a temperature-dependent definition of the electrical conductivity is not yet possible, we worked with restart simulations and active simulation control. This way, the average electrical conductivities of the respective temperature ranges could be considered, i.e., 293.15 to 303.15 K, 303.15 to 313.15 K, and so on. The following investigations focus on one-fluid simulation, i.e., purging was not considered.

Example

In the first step, a commercially available inorganic sand-binder mixture was used for the experimental investigation and validation of the simulation model to investigate heating and temperature-dependent electrical conductivity. The time required to reach 373.15 K as well as the power and energy input into the sand core were measured. Based on the experimental analysis and results, a basic simulation model was created. For reasons of discretion, some of the underlying results are presented only qualitatively. The results are demonstrated in Figure 2, showing high accordance between the measured values and the simulation.

Based on the validated results, the ACS process and simulation are shown using a simple but high-volume geometry, which illustrates the fundamentals and high potential of the advanced ACS development compared to the classic hot box process. The geometric alignment can be taken from Figure 3. Three cases were simulated: (1) a classic hot box process; (2) an ACS cold start process with cold tool (293.15 K); and (3) an ACS series process accounting for the tool heating due to the Joule effect. All three-dimensional models were discretized with a cell size of 1 mm. Table 1 sums up the most important details of the calculated scenarios.

Results and Discusssion

Figure 4 shows the temperature and moisture development for the classic hot box process, clearly showing the outside-in heat transfer and corresponding moisture reduction. The simulation was carried out for 120 s with moisture still present in the sand core center at the end of the simulation; in practice, cycle time targets force an early termination of the drying process with shell formation and residual moisture in the core center. However, the ACS cold start simulation (corresponding to the first shot when the core shooting machine is started up), which is shown in Figure 5, shows the basic principle of the new process: the uniform heating of the core leads to an inside-out moisture transport. Furthermore, the sand core heats up faster than the core box. In the series process, the core box also reaches temperatures greater than 373.15 K through Joule heating, resulting in a mixture of hot box and ACS processes which further accelerates the drying process. The results of the ACS series simulation are summarized in Figure 6. While the sand core is not fully cured even after 120 seconds in the hot box process, the ACS process allows the core to dry completely after 72 or 45 seconds. Despite the significantly lower core box temperature, the new process shows a significant acceleration in core drying and the great potential of the new approach. One major advantage is a massive reduction in cycle times, including the associated energy requirements and the corresponding CO₂ emissions. The energy introduced into the sand core can be measured during the real process as well as predicted in advance using simulation, which is another great advantage in terms of process design and transparency. Additionally, the simulation clearly illustrates the geometry-independent homogeneous heating of the test specimen, which means that moisture is not trapped in the core center and shell formation is avoided. All in all, the new process enables a significant increase in efficiency of the process and the quality of the inorganically bound sand cores as well. The process diagrams of all three cases are summarized in Figure 7.

Figure 4: Results of heat and moisture development in the sand core during the conventional hot box process with a constant core box temperature of 498.15 K.

Figure 5: Results of heat and moisture development in the sand core during the ACS (cold start) process with a core box start temperature of 293.15 K.

Figure 6: Results of heat and moisture development in the core during the ACS series process with a core box start temperature of 383.15 K.

Figure 7: Comparison of the simulated process sequences of the classic hot box process with the new ACS process (cold start/series): a) average core box temperature, b) average sand core temperature and c) moisture mass.

Summary and Outlook

The demonstrated modelling shows the capability of FLOW-3D to simulate the new core drying process accurately as well as the potential of the new process for more efficient core drying and curing compared to the conventional hot box process. Even if the new simulation setup is still in the development stage and needs more real-case experiments, it still allows for great insights in the drying behavior, with very good agreement with experimental measurements so far.

Presently, within the simulation, the electrical conductivity of the sand-binder mixture is generated via the quartz sand, which in reality is not electrically conductive but corresponds to the electrical conductivity of the real-measured sand-binder mixtures. This way, the electrical conductivity of the entire sand-binder mixture is accounted for in the simulation and seems to fit the experimental results. For more precise simulations, the possibility of saving a temperature-dependent electrical conductivity of the solid core (i.e. the sand-binder mixture) would be helpful in order to take the actual conductivity curve into account. Further steps will concentrate on two-fluid simulation models. Initial trials show the basic feasibility with good results.

Despite the steps still to be taken, it can be said that the ability to simulate the ACS process with FLOW-3D marks an important milestone in the holistic establishment of a Joule heating-based core drying process and shows the benefits of this process for inorganic sand core manufacturing.

References

Starobin, C.W. Hirt, H. Lang, M. Todte, Core Drying Simulation and Validation, AFS Proceedings, Schaumburg, IL USA, 2011
FLOW-3D from Flow Science, Inc., Santa Fe, NM, USA

Prediction of Shrinkage Defects During Investment Casting Process

This article was contributed by Dr. S. Savithri, Senior Principal Scientist at CSIR-NIIST

Investment casting process is one of the oldest casting processes was prevalent since about 4000 B.C. It involves pouring liquid metal into a ceramic shell mold created around an expendable (wax) pattern. Earlier it was used to produce jewellery and idols in gold, silver, copper and bronze alloys. Investment casting process came into use as a modern industrial process in the late 19th century, when dentists began using it to make crowns and inlays, as described by Barnabas Frederick Philbrook of Council Bluffs, Iowa in 1897. In the 1940s, during World War II, the use of investment casting process increased due to the demand for precision net shape manufacturing techniques of specialized alloys that could not be shaped by traditional methods, or that required too much machining. Today investment casting process is usually employed for producing small industrial parts of ferrous, nonferrous and super alloys in near net shape with good surface finish and dimensional accuracy.

The investment casting process consists of four major steps:

Creation of a wax pattern, followed by cleaning and assembling with the gating system to make the pattern cluster, or ‘tree’
The tree is alternatively coated with the slurry of fine and coarse sand particles to obtain a ceramic shell
The shell is dried, heated to melt the wax then preheated to increase its strength and prepare for pouring
Finally the cast alloy is melted and poured into the preheated shell; after the solidification the shell is broken to obtain the cast part

The parts obtained from investment casting process are used in many critical applications and hence they need to be free of internal defects. The main defects that occur during the investment casting process are ceramic inclusion, crack, distortion, flash, misrun, shrinkage, slag inclusion and cold shut. In order to predict the quality of the obtained casting, it is necessary to study the effects of various casting process parameters such as the metal–mold heat transfer coefficient, pouring temperature, shell thickness and the shell heat transfer coefficient. With the advent of modern computer systems and simulation software, simulation of mold filling and solidification is being increasingly used in foundries to predict casting defects and optimize the design to obtain maximum output.

The main purpose of this work is to investigate whether radiation heat transfer that is a predominant factor in investment casting process, and shell molds that are unique to investment casting process can be effectively implemented in FLOW-3D. The different effects of these two components of the process are also investigated by carrying out mold filling and solidification simulations of the investment casting process for a simple geometry using FLOW-3D. The numerical values of the temperature obtained at various locations are validated with the experimental results reported in the literature [1]. The effect of the radiation heat transfer coefficient, shell mold thickness, and location of sprue and in-gates were also investigated.

Methodology

The computational geometry used in the present investigation is shown in Figure 1. A shell mold was created using the following steps:

Import the geometry as component 1 into FLOW-3D and create a mesh block around the imported geometry with a specified cell size.
Make the first sub-component of component1 of type “complement” to make everything outside of the subcomponent solid to the extents of the mesh.
Define the mold material properties for this solid block from the solids database.
There is an option to define “Thermal penetration depth” under component properties in solid properties GUI. There the shell thickness value can be defined.
Now run the preprocessor.
Go to Analyze tab> 3D tab then open the prpgrf file created in the previous step. Under both ‘Iso-surface’ and ‘color variable’ select “thermally active component volume” and select “Render”.
Now in Display it should display only the shell part of the geometry.
Save this surface as an STL file by selecting “component 1” in the object list (left side, bottom of the window), right-clicking on “component 1” and selecting “export to stl”.

After creating the STL file for the shell mold, this file is imported into a new simulation as component 1, the casting geometry created earlier is imported as a subcomponent and the type is chosen as ‘hole’. The casting geometry along with the shell mold is shown in Figure 2. This serves as our computational domain. The next task is to create a mesh to discretize the computational domain into cubical/rectangular cells. The mesh is generated in FLOW-3D by creating a mesh block. For the current work we have chosen the uniform mesh option shown in Figure 3 where a fixed cell size of 2.5 mm is chosen. Two mesh blocks were created for the current simulation where mesh block 2 is used around the inlet location. A void region is defined around the shell to account for the heat transfer between the shell and ambient air at 30 °C. This is chosen as a void region with ‘heat transfer type 1’ and a heat transfer coefficient value between the shell and ambient air is assigned. The heat transfer type 1 will be a lumped heat transfer coefficient including the radiation.

The material chosen for shell mold is zircon and the thermal properties are obtained from experiments conducted by Sabau and Vishwanathan [2]. Table 1 shows the values assigned for the materials used in the study.

MATERIAL	PROPERTY	VALUE	UNIT
Fluid –Aluminium A356 alloy	Density Thermal conductivity Specific heat Latent heat Liquidus temperature Solidus temperature	2437 116.8 1074 433.22 608 552.4	kg/m³ W/(m K) J/(kg K) kJ/m³ 0C 0C
Zircon Mold	Thermal conductivity Specific heat* Density	1.09 1.63E+06	W/(m K) J/( m³ K)

Table 2. Initial and boundary conditions used for the simulation
Mold temperature Melt pouring temperature Filling time Interface heat transfer coefficient Heat transfer coefficient between ambient and mold (radiation effect)	430°C 680°C 7 s 850 W/m²K 30 -100 W/m²K

The initial velocity and temperature of the melt entering the sprue basin is given as the velocity boundary condition at the top boundary of the mesh block 2. By default all the other boundaries are set to the symmetry type.

Four locations that were chosen by Sabuet.al [1] in their experiments for obtaining cooling curves during filling and solidification were used for validation purposes. They are referred to as C1, C2 and S11, S12 and S21. The points C1 and C2 are at the center of the two-plate casting and S11, S12 and S21 are all located in the shell. The evolution of temperature at these locations is shown in Figure 4.

It can be seen that the comparison of numerical and experimental results of the temperature profiles is within acceptable limits. For the probe points C1 and C2, the variations between numerical and experimental results are within 5% during solidification and 12% for cooling after solidification. For the points in the shell, the numerical results are higher than the experimental results by around 5%. This may be due to the assumptions made in assigning thermophysical properties to the shell material and the value of the shell heat transfer coefficient.

Fill sequence & solidification pattern for two different sprue locations

The fill sequences during mold filling for two different sprue locations are shown in Figures 5a & 5b. It can be observed that the end sprue creates more splashing which can lead to inclusion type defects. When the sprue is placed in the middle, the flow is more uniform and shows similar temperature distributions in both casting sections. The 2D view of the temperature profile after 50% solidification is shown in Figures 5c & 5d for both cases. From the shrinkage location, it is very clear that both sprue locations will give rise to defects.

Effect of shell thickness

In order to study the effect of shell thickness on investment casting, castings with shell thickness of 7.2, 10, 15 and 20 mm were considered. Figures 6a & 6b show the cooling curve at a particular location in the casting that is depicted as C1 and at a particular location in the shell mold, which is depicted as S11 during solidification. It can be observed that the increase in thickness of the ceramic shell from 7.2 mm to 15 mm decreases the rate of cooling, and hence, leads to longer solidification times.

Effect of shell heat transfer coefficient

The shell heat transfer coefficient, h_a, represents the rate at which heat is dissipated from the outer wall of the shell mold to the surrounding air through radiation. To investigate this effect, the value of the heat transfer coefficient was varied from 20 to 80 W/m²K . It can be seen from Figures 7a & 7b that the change in h_a has a significant effect on cooling rate of the cast material and shell. When the heat transfer coefficient was increased from 20 to 80 W/m²K, it was seen that the solidification time at C1 was reduced from 812 s to 334 s (by approximately 44%). Therefore, changing the value of h_a will have a bearing on the microstructure of the cast product.

Conclusions

Mold filling and solidification simulations of the investment casting process were carried out using FLOW-3D. Parametric studies have been conducted to study the effect of casting parameters on the casting process. The following conclusions may be drawn from the present study:

FLOW-3D is capable of modeling filling and solidification in a multi-cavity mold for investment casting process. The predicted temperature profiles at the probe locations were within acceptable limits of the experimental data.
For shell thickness, it was seen that in both cases there is a critical thickness of the shell, beyond which the heat transfer characteristics reverse. As the shell thickness increases, it was seen that the solidification time increased, up to the critical thickness and then started decreasing. For the original geometry, the critical thickness lies between 15 to 20 mm, whereas for the modified geometry it lies between 10 and 15 mm.
The heat transfer coefficient between the shell and the ambient air, h_a, was found to have the most significant effect on the heat transfer characteristics. When h_a was increased by a factor of 4, from 20 to 80 W/m²K, the solidification time at the center of the sprue decreased by more than 40%.

References

Sabau, A.S., Numerical Simulation of the Investment Casting Process, Transactions of the American Foundry Society, vol. 113, Paper No. 05-160, 2005.

Sabau, A.S., and Viswanathan, S., Thermophysical Properties of Zircon and Fused Silica-based Shells used in the Investment Casting Process, Transactions of the American Foundry Society, vol. 112, Paper No. 04-081, 2004.

Learn more about the versatility and power of modeling metal casting processes with FLOW-3D CAST.

Investigation of Mould Leakages in a Gravity Casting

This article was contributed by Gabriele Taricco of CM Taricco and Stefano Mascetti of XC Engineering.

Mould design is a very complex undertaking that must consider not only fluid dynamics and metal solidification patterns, but problems that may arise from the mould itself and how it reacts to stresses from heat transfer. CM Taricco, a mould maker company based in Italy, recently encountered a problem of metal leakages at the bottom of one of their new moulds. The cause of the mould leakages was initially obscure and only appeared after a few process cycles. It was evident that the problem was critical, since it would compromise the production timeline and dramatically increase the costs to cast the part.

Investigation of an idea

The process itself was a gravity casting, with well-controlled pouring dynamics and overflow designs, so the problem could not come from the fluid dynamics part. The hypothesis of Gabriele Taricco (owner of CM Taricco) was that the metal leakages were resulting from a bad design of the thermal dissipation of the mould, causing a non-uniform distribution of temperature and hence large and unwanted deformations at the bottom of the mould, that were enforced cycle after cycle up to the opening of a critical area where metal could flows out. To verify this and to find a quick solution to the problem, a FLOW-3D simulation was run to exactly visualize what was happening to the mould as it was being heated.

A careful setup, to achieve a fast resolution of the issue

The cause of the problem was to be identified quickly, and an accurate setup – taking advantage of all the most recent FLOW-3D features – was necessary. In particular, the meshing technique adopted was very helpful in order to reduce the number of computational cells to a very low number, keeping almost the same accuracy of a traditional setup, but providing a very fast simulation.

The first trick that was adopted for meshing the mould was to rotate it around the vertical axis in order to align the inner thin cavity of the mould with an orthogonal axis.

The second trick was to use the new conformal mesh feature for the inner cavity (thin walls), while keeping a traditional larger mesh block for the entire domain. The conformal mesh was conformed to the open volume, and was restricted to the filling cavity only, that had quite small clearances.

A global view of the mould with cores and its alignment with the mesh blocks

Finally, to limit the space externally to the mould (that now became a shoebox shape, rotated 20 degrees, so badly aligned with the model axis), some ‘domain removing’ components were used, defining them directly through the internal solid modeller of FLOW-3D.

The rest of the setup followed a traditional scheme, taking advantage of most of the recommended default values of the software. Thanks to these features and the new sub-domain decomposition feature in FLOW-3D, it was possible to reduce the designed 9 000 000 cells to only 1 840 000 cells for the fluid sub-domain and 2 430 000 cells for the solid sub-domain.

The analysis

After a filling simulation, to ensure a good filling pattern, the focus of the simulation was redirected to a thermal die cycling analysis. The setup in this case is fast and straightforward requiring only 1 hour to reproduce 10 production cycles on a common desktop machine (i7 5930K, commercial value 1500 dollars). The result confirmed CM’s initial hypothesis: by looking at the temperature field, from various points of view and cross sections in a single image using FlowSight, it was clear that the temperature distribution of the mould would easily cause the expected deformations and metal leakages.

Further analysis with the Fluid-Structure Interaction module

Once the problem was identified and the technical staff could start designing an improved mould, CM Taricco wanted to have a final confirmation running a FEM analysis of stresses and deformations on the die. To perform this analysis, XC Engineering Srl helped CM in setting up and performing the calculation. The result of the analysis showed exactly what CM thought was happening: FLOW-3D was able to reproduce with extreme accuracy the same location and size of the real deformations found on the mould after few pouring cycles. This was good news for CM, and enforces an additional recommendation to use the FSI module at the design stage to predict the real die deformations based on the real casting conditions.

Simulation of the mould’s temperature during the die cyclings

Deformation of the mould during the die cyclings, simulated using the Fluid Structure Interaction model. Deformations are amplified x20.

Conclusion

As a conclusion of the analysis, the CM staff was able to design a new optimized mould, using all of the information on the temperature field from the CFD solution. The new mould was able to dissipate the thermal energy in a more efficient way, and the cast pieces were no longer affected by metal leakages even after dozens of process cycles.

Learn more about the versatility and power of modeling metal casting processes with FLOW-3D CAST.

Increasing Productivity by Reducing Ejection Times

Increasing Productivity by Reducing Ejection Time

This article was contributed by Eugene Moore of Hellebusch Tool & Die

Simulation software is a valuable tool that helps designers and engineers understand the details of the casting process, enabling them to consistently create high-quality parts faster and with lower costs than their competitors. In high pressure die casting, simulation software is used to help design better gating systems to feed the metal into the casting, improve the timing of the shot sleeve tip to prevent air entrainment due to turbulence, and identify the most effective locations for overflows, among other things. In this article, we will look at how to reduce the time before a part can be ejected from the die in order to reduce the process time.

The biscuit is a natural place to focus our efforts since it is the last place to solidify in the casting and, therefore, determines when the part can be ejected. So, if we can reduce the solidification time of the biscuit, then we can reduce the overall process time. One way to do this is to remove more heat from the metal through the shot tip by increasing the amount of area in contact with the fluid. While not exactly applicable in this case, the basis for this approach is most easily shown using the equation for steady-state convection, shown below.

$Q=hA\Delta T$

In this equation, $Q$ is the heat flow, $h$ is the convective heat transfer coefficient, $\Delta T$ is the difference between the metal and shot tip temperatures, and $A$ is the surface area of the shot tip in contact with the metal. There are different shapes of plunger tips available in the market today that are designed to increase the surface area in contact with the metal, as shown in Figure 1.

Another method for increasing the heat removed from the biscuit is to moderate the temperature difference between the shot tip and the metal in the biscuit. This is done by adding cooling lines to the tip, as seen in Figure 2. The main drawback with this approach is that it adds considerable complexity to the piston assembly.

New design

For this article a new plunger tip design was analyzed using FLOW-3D Cast and compared to a standard, unmodified cylindrical tip. The modified tip, consisting of a cylindrical tip with a star-shaped cutout on the end as shown in Figure 3, has 20% more surface area than the unmodified shot tip. Neither tip will be water cooled for the analysis.

Analysis

A simulation of the filling (including shot tip motion) and of solidification (without flow) was run for each shot tip design; all other parameters were identical between the cases. There were two primary results of interest: the flow pattern during filling and the overall solidification time. The flow pattern during filling is important because, if the shot tip design were to cause breaking waves and air entrainment, then the tip or the shot sleeve profile would had to have been redesigned.

The first comparison is of the flow patterns in the shot sleeve, shown in Figure 4. This figure shows an image of the fluid during shot sleeve with and without the modified tip and it is seen that the shape of the tip is not significantly affecting the flow patterns. Since there is little effect on shot profile we can focus on the solidification.

The second comparison is of the solidification time. Figure 5 shows the comparison of the average temperature of the tips as a function of time, the heat flux from the metal to the tip as a function of time, and the temperature profile of the liquid metal at time of extraction.

As can be seen in Figure 5, the graphs show that the average temperature of the modified tip is higher because it extracted more heat from the metal. This is also shown in the plot of heat flux; notice the negative value indicating energy removal. The images below the graphs show the liquid metal at the interface of the biscuit and the shot tip. The data shows that there is a 12.7% increase in heat removal using the modified tip.

Conclusions

The shot tip design does have a noticeable effect on the solidification time of the cast part. Simulation software provides a way to analyze its effects and use this knowledge to optimize process parameters.

References

[1] http://www.metalminotti.it/copper-alloys-semi-and-finished-products/plunger-tips-for-die-casting/

[2] http://www.castool.com/product/plunger-rod

Learn more about the versatility and power of modeling metal casting processes with FLOW-3D CAST.

Detecting Porosity with the Core Gas Model

Detecting Porosity with the Gas Core Model

Producing High Quality Castings

Foundries must perform a great deal of up-front engineering to ensure casting quality is achieved on the first trial. In recent years, numerical tools for modeling metal flow, solidification, microstructure evolution and residual stresses have become commonplace. However, one casting defect that has yet to be thoroughly addressed is the common core gas blow defect. The physics of this problem involves a complicated interaction between the metal, core and binder. Failure to solve it can result in high scrap levels. In most instances, the problem is merely managed – but never completely solved – by using a higher pour temperature and adding more wall stock to the affected areas.

Designing the Optimum Break-Down

In the past, if materials and casting engineers found a porosity defect issue due to core gas bubbles, they would step through a standard series of problem-solving tasks: reduce the binder content, increase the core venting, coat the core or possibly bake the cores ahead of time. Since it was impossible to see the path that the gas followed, this was a long drawn-out process often taking weeks to complete for one part. And, it had to be repeated every time there was a problem with a different part.

The market-driven need to compress this processing timeline has prompted the development of casting simulation software. Useful for both design and manufacturing, computer-based modeling allows engineers to test a variety of approaches without any real-part cost or waste. To help foundries apply simulation specifically to venting design, Flow Science has added core gas modeling to its casting analysis capabilities.

Applying CFD Methods to Core Gas Flow

Due to the chemical complexity of resin-based binders, understanding just where and how the gas flows after sand-core thermal break-down is complicated. However, Flow Science collaborated with several groups to obtain experimental data and compare results with those from the numerical models. The company gathered core gas flow rate information from General Motors, Graham-White Manufacturing Co. and AlchemCast, getting real-world data on sand-resin cores used with aluminum, iron and steel.

Dr. David Goettsch, a casting analysis engineer at GM Powertrain, has used FLOW-3D for fifteen years for analyzing filling and solidification of metal castings. The new core gas model has been quite useful for optimizing the jacket core venting at the design stage. It is very difficult to implement vent tracks into an existing core box with all the other demands on the core prints. “Upfront analysis work on core gas venting can save you from high scrap rates during your start up,” he explains. “Perhaps process changes can solve the problem. But it may take a lengthy test period to get to that point.”

With the core gas model now available in FLOW-3D, Goettsch can try different insertion and venting locations and get a global diagnosis: seeing how much gas develops, where it goes, and how much got out before the metal front caught up to it.

Multi-Core Challenges

Another experienced foundry engineer, Elizabeth Ryder of Graham-White Manufacturing Co., echoes the opinion that gas porosity has always been difficult to investigate. She adds that, “Particularly with multiple cores, it was hard to pinpoint which core was the source of the problem. You tried to address the whole system.”

With ongoing production runs of 1700 parts, some of them in quantities of 10,000 parts per year, Graham-White was very receptive to improving its manufacturing processes through simulation.

Working with a 3D model of a grey-iron part (roughly 3in x 4in) created by laser scanning, Graham-White provided the current venting design for evaluation. This gating design comprised four impressions per pattern plate in a horizontally parted mold, with each impression having vents for each core. A central sprue enabled filling each mold in less than two seconds.

With ongoing production runs of 1700 parts, some of them in quantities of 10,000 parts per year, Graham-White was very receptive to improving its manufacturing processes through simulation.

Simulation with FLOW-3D confirmed the fill rate, but also showed that one core had insufficient venting. Graham-White then began drilling deeper holes in the core to help channel more gas through the existing vents. Since switching its approach to the new venting design, the company has seen an approximately 30% decrease in core blow scrap.

Ryder says that FLOW-3D results helped narrow their design focus, letting them immediately zero in on which core (of a multi-core design) was the culprit, and even which area of the core was the problem source.

Learn more about the versatility and power of modeling metal casting processes with FLOW-3D CAST.

The more you can do on a computer ahead of time, the better. It all comes down to saving time.

It’s very nice when you can actually see the root cause of the problem. These visualizations are great to try to get a little window on what the real phenomenon is doing.

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Simulation of Joule heating-based Core Drying

The ACS Process

Model Description

Example

Results and Discusssion

Summary and Outlook

References

Prediction of Shrinkage Defects During Investment Casting Process

Methodology

Fill sequence & solidification pattern for two different sprue locations

Effect of shell thickness

Effect of shell heat transfer coefficient

Conclusions

References

Investigation of Mould Leakages in a Gravity Casting

Investigation of an idea

A careful setup, to achieve a fast resolution of the issue

The analysis

Further analysis with the Fluid-Structure Interaction module

Conclusion

Increasing Productivity by Reducing Ejection Time

New design

Analysis

Conclusions

References

Detecting Porosity with the Gas Core Model

Producing High Quality Castings

Designing the Optimum Break-Down

Applying CFD Methods to Core Gas Flow

Multi-Core Challenges

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