In injection-molding process [1-4], a runner system is known to play a highly critical role. Runners are generally used for transporting melt from an injection machine nozzle to the cavities. The qualities of molded parts are also considerably influenced by runner designs. Although the quality runner design is quite helpful in enhancing product qualities, conventional cold runner systems have certain inherent problems — for example, regrind problem, material waste, increased molding cycle, etc. and also poor product cosmetics like low gloss matter and welding line.
Therefore, hot runner technology [5-10] is increasingly used to address these problems in the injection-molding process. Benefits include easy cavity filling, reduced injection pressure/clamping force, decreased cycle time, energy/material saving, and improved product quality. Applications include automobile dashboard, bumper, TV/LCD front/back cover, food plastic containers, bottle caps, and so on.
A hot runner system includes a hot nozzle, hot runner gate, manifold, and heating coil. Different types of designs are available for each component; for instance, hot runner gate options include valve gate, edge gate, thermal sprue gate, and hot-tip gate. Different component designs and dimensions of each component influence the performance and behavior of the hot runner system, making it difficult to fully understand its mechanism. This article proposes a true 3D numerical technique to simulate the temperature behavior of a real hot runner system for PC materials.
Moldex3D can be used to develop the CAE model by importing each key hot runner component, defining its attributes, and meshing automatically in the preprocessing stage.
A three-dimensional, cyclic, transient heat conduction problem with boundary conditions on hot runner component surfaces is involved. A three-dimensional Poisson equation controls the overall heat transfer phenomenon [11-15]:
T = temperature
t = time
x, y, and z = the Cartesian coordinates
ρ = density
Cp = specific heat
k = thermal conductivity
The equation holds for hot runner components as well as part component except with varying thermal properties. It is assumed that the initial mold temperature is equivalent to the input of initial settings.
The initial part temperature distribution is achieved from the analysis results toward the end of the filling and packing stages:
The impact of thermal radiation is overlooked in this study. The conditions defined over the mold’s boundary surfaces and interfaces are specified as
Where h is the heat transfer coefficient, and n is the normal direction of mold boundary. On the exterior surfaces of the moldbase,
Description of CAE Model
This numerical simulation technology is applied to a true hot runner system developed by ANNTONG IND. CO. LTD, which includes the part, a metal mold, a hot runner nozzle, three sets of heating coils, three brass bushings around which the heating coils are wrapped, and other bushing components. P20 is the material used for the metal mold, and PC (Panlite L-1225Y) is the thermoplastic material used for the melt. Figure 1 shows the constructed CAE model for this hot runner system.
Figure 1. The CAE model for hot runner system
Table 1 shows the material properties, such as thermal conductivity, heat capacity, and density, for each hot runner component in the system. These parameters are the inputs in the study and will be used to consider the impacts from various thermal properties.
Table 1. Material property for hot runner components
|Material Properties for Hot Runner Components
The information of boundary conditions and sensor node locations defined in accordance with the real experiments is shown in Figure 2.
Figure 2. Boundary conditions and sensor node positions
The injection-molding process is performed cycle by cycle in the hot runner experiment. The melt temperature is determined at the same locations as the positions of sensor nodes 1 to 10 in the simulation. Seven sensor lines are arranged in the hot runner system and are represented by the sensor nodes (1A, 2A, 3A, 1B, 2B, 3B, 1C) in the simulation. The hot runner system used in the experiment is shown in Figure 3, and the metal mold in the experiment is depicted in Figure 4. In Figure 5, sensor lines are integrated in the grooves of the hot runner nozzle. The molding parameters are shown in Table 2. The temperature measurement will be applied after 10 shots to ensure the stability of the melt temperature.
Figure 3. Heating coils and cooling channel
Figure 4. The hot runner system in the experiment
Figure 5. Experimental metal mold
Figure 6. Temperature sensor lines in the hot runner system
Table 2. Injection-molding process parameters
|Injection molding process condition
|Filling speed (mm/s)
|Injection time (s)
|Melt temperature (℃)
|Mold temperature (℃)
|Packing pressure (MPa)
|Packing time (s)
|Cooling time (s)
|Mold open time (s)
|Cycle time (s)
Results and Discussions
In the experimental result, the melt temperature as a function of the certain locations is calculated and plotted, followed by a comparison of the simulating result with that from the experiment. The comparison reveals excellent agreement in terms of trend and magnitude. This is done to verify this simulation technology applied for the hot runner system.
The Simulation Results
Shown in Figure 7 is the simulating melt temperature field within the melt channel or runner at the end of 10 injection-molding cycles.
Figure 7. Temperature profile in the runner after 10 injection-molding cycles
Figure 8 shows the simulating temperature field within the moldbase after 10 injection-molding cycles. The moldbase close to the cooling water has lower temperature as shown in dark blue color.
Figure 8. Temperature profile inside of moldbase after 10 injection-molding cycles
The simulating melt temperature history curves for 1 to 10 sensor nodes are shown in Figure 9. It can be seen that the melt temperature detected from each sensor node increases cycle after cycle. The temperature should eventually reach a steady state after an extended time.
Figure 9. Melt temperature history curves for the sensor nodes 1 to 10
The Validation from the Experiment
Shown in Figure 10 are the experimental melt temperature distributions as a function of location using controlling sensor nodes 1A, 2A, and 3A.
Figure 10. Melt temperature distribution as a function of location for 1A, 2A, and 3A controlling sensor nodes.
The experimental melt temperature distributions as a function of location using controlling sensor nodes 1C, 2A, and 3A are shown in Figure 11. Both the simulation and experimental results are found to be similar to the case of controlling sensor nodes 1A, 2A, and 3A.
Figure 11. Melt temperature distribution as a function of location for 1C, 2A, and 3A controlling sensor nodes.
Shown in Figure 12 are the experimental melt temperature distributions as a function of location using controlling sensor nodes 1B, 2B, and 3B. The experiment result shows no apparent higher temperature region at the position of 60 mm. Moreover, the simulation result denotes this phenomenon, which is different from the earlier two cases.
Figure 12. Melt temperature distribution as a function of location for 1B, 2B, and 3B controlling sensor nodes.
In the above three cases, the comparisons between the experimental and simulation results indicate good agreement in terms of magnitude and trend.
In this research, a true 3D numerical method to predict the temperature behavior in the hot runner system has been developed. A hot runner system CAE model can be constructed, attribute-defined, and meshed in an efficient way in the preprocessing step. Through this numerical technique, the dynamic feature of the temperature field in hot runner systems can be simulated and investigated. A real case from ANNTONG hot runner systems was used for the simulation. The simulating melt temperature profile can be obtained.
The experimental study for this hot runner system is conducted and the melt temperature is measured at the locations of interest. The numerical results are compared with those from the experiment to validate Moldex3D simulation technology applied in this work. The simulation results are in good agreement with those from the experiment in both trend and magnitude.
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This information has been sourced, reviewed and adapted from materials provided by Moldex3D.
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