• Transactions of the Korean Society of Automotive Engineers
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• v.20 no.1
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• pp.124-130
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• 2012

An injection mold cooling circuit for an automotive front bumper was optimally designed in order to simultaneously minimize the average of the standard deviations of the temperature and the difference in mean temperatures of the upper and lower molds for uniform cooling. The temperature distribution for a specified design was evaluated by Moldflow Insight 2010, a commercial injection molding analysis tool. For efficient design, PIAnO (Process Integration, Automation and Optimization), a commercial PIDO tool, was used to integrate and automate injection molding analysis procedure. The weighted-sum method was used to handle the multi-objective optimization problem and PQRSM, a function-based sequential approximate optimizer equipped in PIAnO, to handle numerically noisy responses with respect to the variation of design variables. The optimal average of the standard deviations and difference in mean temperatures were found to be reduced by 9.2% and 56.52%, respectively, compared to the initial ones.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.27-34
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• 2004

In the injection molding of plastic optical lenses, the molding conditions have critical effects on the quality of the molded lenses. Since there are many molding parameters involved in injection molding process, determination of the molding conditions for lens molding is very important in order to precisely control the surface contours of an optical lens. Therefore this paper presents the application of neural network in suggesting the optimized molding conditions for improving the quality of molded parts based on data of FE Analysis carried out through CAE software, Timon-3D. Suggested model in this paper, which serves to learn from the data of FE Analysis and induce the values for optimized molding conditions. has been implemented for searching the molding conditions without void and with minimized thickness shrinkage at lens center of injection molding optical lens. As the result of this study. we have confirmed that void creation at the inside of lens is primarily determined by mold temperature and thickness shrinkage at center of lens is primarily determined by the parameters such as holding pressure and mold temperature.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.97-100
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• 1997

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Especially when short shot is predicted by CAE simulation in the filling stage, there are mainly three ways to solve the problem. Modification of gate and runner, replacement of plastic resin, and adjustment of process conditions are the main ways. Among them, adjustment of process conditions is the most economic way in the cost and time since the mold doesn\\`t need t be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, a fuzzy neural network(FNN) based upon injection molding process is proposed to evaluate moldability in filling stage and also to solve the problem in case of short shot. An adequate mold temperature is generated through the fuzzy neural network where fill time and melt temperature are taken into considerations because process conditions affect each other.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.20 no.5
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• pp.646-652
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• 2011

Recently, lots of automobile part manufacturers try to increase glass fiber content of their plastic parts to improve strength and impact-resistance. For this reason, injection mold requires high hardness and wear-resistant. Laser surface treatment is used to improve characteristics of wear and to enhance the fatigue resistance for injection mold. In this paper, high carbon steel (HP4MA) for injection mold material was heat-treated to harden surface by using high power diode laser (HPDL). To find the process parameters for laser surface treatment of HP4MA, many experiments are carried out as changing the parameters of surface temperature and travel speed of laser. From the results of the experiments, it has been shown that the maximum average hardness is approximately 711~739 Hv when the temperature and the travel of laser are $1,050^{\circ}C$ and 2 mm/sec.

• Journal of the Korean Society of Propulsion Engineers
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• v.14 no.1
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• pp.29-39
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• 2010

The system performance of the regenerative gas-turbine cycle with the steam injection into the combustor has been studied through the thermodynamic cyclic analysis. The effects of the pressure ratio, the steam injection ratio, the ambient temperature, and the turbine inlet temperature are investigated on the thermal efficiency, the fuel consumption, and the specific power as well as the operating conditions for the maximum thermal efficiency of the system. The results of the present analysis find that the use of steam injection in the regenerative gas-turbine system can greatly enhance the thermal efficiency and the specific power of the system.

• Journal of Veterinary Clinics
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• v.9 no.2
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• pp.391-399
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• 1992

This study was carried out to evaluate the effects of doxapram after medetomidine treatment. Twenty dogs were sedated with medetomidine(0.04mg/kg IM) Ten dogs were injected doxapram(2mg/kg IV) as a experimental group and ten dogs were injected with saline(5$m\ell$ IV) as a control group in twenty minutes after the injection of medetomidine. Recovery time, heart rate, respiratory rate, body temperature. blood chemistry, electrocardiogram findings (ECG) were recorded. The results obtained were as follows ; 1. Medetomidine revealed fast and excellent sedative effect. 2. Recovery time was shorted by doxapram(p<0.01) 3. Respiratory rates were decreased significantly by medetommidine, but increased remarkably after the injection of doxapram and them decreased gradually and revealed normal levels(p<0.01). 4. Herts rates were decreased significantly by medetomidine but increased remarkably after the injection of doxapram and then decreased gradually and revealed normal levels(p<0.01). 5. Body temperature were increased slightly and then decreased by medetomldine and in experimental group revealed with higher levels than those of control group(p<0.01) 6. Arrhythmias were observed after the injection of medetomidine, but relieved after the injection of doxapram . There was no another change on electrocardiograms.

• 한국전산유체공학회:학술대회논문집
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• 2011.05a
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• pp.92-95
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• 2011

In manufacturing optical fibers, the process starts with the glass fiber drawing from the heated and softened silica preform in the furnace, and the freshly drawn glass fiber is still at high temperature when it leaves the glass fiber drawing furnace. It is necessary to cool down the glass fiber to the ambient temperature before it then enters the fiber coating applicator, since the hot glass fiber is known to cause several technical difficulties in achieving high quality fiber coating. As the fiber drawing speed keeps increasing, a current manufacturing of optical fibers requires a dedicated cooling unit with helium gas injection. A series of three-dimensional flow and heat transfer computations are carried out to investigate the effectiveness of fiber cooling in the fiber cooling unit. The glass fiber cooling unit is simplified into the long cylindrical enclosure at which the hot glass fiber passes through at high speed, and the helium is being supplied through several injection slots of rectangular shape along the cooling unit. This study presents and discusses the effects of helium injection rates on the glass fiber cooling rates.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.21 no.6
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• pp.1008-1012
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• 2012

Injection molds are fabricated by assembling a number of plates in which mold core and cavity components are inserted. The assembled structure causes a number of contact interfaces between each component where the heat transfer is affected by the thermal contact resistance. However, the mold assembly has been treated as a one body in numerical analyses of injection molding, which has a limitation in predicting the mold temperature distribution during the molding cycle. In this study, a numerical approach that considers the thermal contact effect is proposed to predict the heat transfer characteristics of an injection mold assembly. To find the thermal contact conductance between the mold core and plate, a number of finite element (FE) simulations were performed with the design of experiment (DOE) and statistical analysis. Thus, the heat transfer analyses using the obtained conductance values can provide more reliable results than conventional one-body simulations.

• Journal of ILASS-Korea
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• v.8 no.2
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• pp.38-45
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• 2003

This study was carried on the combustion characteristics of a pure light oil and emulsified fuels at high-pressure injection in a spray combustion installation, The volume fractions of water in an emulsion were varied up to 30% and the injection pressures were 7.5, 100, 200, and $300kg_f/cm^2$. The concentrations of NOx and the average temperatures of flame were measured. And Images of OH radical using ICCD camera and instantaneous schlieren photography of flames were photographed. It was found that the temperature distribution of axial distance in the emulsified fuels was increased in the upstream and decreased in the down stream. The temperature distribution of radial distance was high at the peripheral regions of the spray in the upstream and at the central regions of spray in the downstream, The intensity of OH radical was denser at the water content 10% than at the pure light oil over the injection pressure $200kg_f/cm^2$.

• Design & Manufacturing
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• v.14 no.3
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• pp.1-7
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• 2020

This paper predicts the mass and the length of injection-molded products through the Artificial Neural Network (ANN) method. The ANN was implemented with 5 input parameters and 2 output parameters(mass, length). The input parameters, such as injection time, melt temperature, mold temperature, packing pressure and packing time were selected. 44 experiments that are based on the mixed sampling method were performed to generate training data for the ANN model. The generated training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. A random search method was used to find the optimized hyper-parameter of the ANN model. After the ANN completed the training, the ANN model predicted the mass and the length of the injection-molded product. According to the result, average error of the ANN for mass was 0.3 %. In the case of length, the average deviation of ANN was 0.043 mm.