• Proceedings of the Korean Society of Precision Engineering Conference
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• 2011.06a
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• pp.707-708
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• 2011

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.70-75
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• 2004

In this study, the pattern of lens aberration was studied at different injection molding conditions such as injection speed, holding pressure, holding pressure time, mold temperature and cylinder temperature and, then, the results were analyzed with a laser interferometer. It was demonstrated that optimal condition of lens aberration could be achieved by adjustment of injection molding conditions.

• Transactions of Materials Processing
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• v.21 no.1
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• pp.36-41
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• 2012

Gas Assisted Injection Molding is a relatively new low-pressure injection molding technique that provides benefits such as reduced part warpage, excellent surface quality without shrink marks, greater design flexibility, etc. In the gas assisted injection molding process, the injected pressurized nitrogen gas flows through designed gas channels and forms hollow sections within the part. However, due to the characteristics of the gas, the design of the gas channels which are the paths for the injected gas is important in order to avoid defects such as gas blowout, fingering, etc. Therefore, in this study, the gas channel design for gas assisted injection molding of exterior display panels was conducted by examining the results of three CAE analyses. The designed gas channel was verified by conducting tryouts using a 450 ton injection molding machine with 3-stage pressure controlled gas kit. In addition, the hollow shapes which were formed by the gas with the installed gas channels were examined by examining the cross sections of the prototypes that were produced. As a result, it was found that exterior display panels can be produced without any defect by applying the gas assisted injection molding technique.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1992.10a
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• pp.158-160
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• 1992

To control internal temperature distribution of moulding machine is very important in molding products such as Polycabonate. Nylon and PVDF. In this experiment, we developed temperature measurement system in order to control temperature of moulding machine. It was measured by telemetering system because of rotational mechanism. Form experimental results, it was sufficient to apply to moulding machine under 250 .deg C.

• Design & Manufacturing
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• v.14 no.2
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• pp.34-41
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• 2020

Typical characteristics of medical device parts are that they can not be reused and there are many disposable products. Therefore, there is a need for an injection molding machine having excellent repeatability of molding conditions and a precision injection mold for mass production. Recently, the performance of an injection machine has made a remarkable evolution compared to the past. However, defects such as short-shot, flash, weld line, gas burning, warpage, and deformation, which are typical defects, still do not disappear at all. This is due to the lack of gas ventilation from the product cavities, even if the gas is smoothly vented from the sprue and runner of the mold. For this reason, the internal pressure of the cavity rises and is directly connected to the quality defects. In this study, an active gas vent system was designed to prevent defects due to trapped gas in the cavity. Since it can be easily adjustable in response to the molding conditions and the mold temperature changes, it is expected to improve productivity due to the reduction of the defective ratio.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.99-106
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• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method (LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.4
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• pp.160-168
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• 2018

Among the components of rotary hooks, a core component of an embroidery sewing system, a study was conducted to apply metal injection molding to the manufacture of a hook body and a housing that was very difficult to mechanical working. The correlation of feedstock, a mixture of binder and SCM 415 metal powder, and properties of the pressure-volume-temperature interrelationship, viscosity, specific heat, and thermal conductivity were measured. Injection molds for the hook body and the housing were developed through injection molding analysis using these properties and conducted injection tests. Optimal injection gate position and number, injection pressure, and injection time were obtained through a comparison of analysis results with the experiment results.

• Design & Manufacturing
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• v.13 no.3
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• pp.53-58
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• 2019

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.1
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• pp.9-13
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• 2014

The molding process can be divided into five separate steps: plastification, injection, holding, cooling, and finally ejection. In the plastic injection molding, the effect factor such as mold temperature, injection speed, packing pressure and inhomogeneous cooling under packing process affects both the article dimension and physical characteristics. Especially, the packing pressure is the most critical factor to affect molded articles quality among the packing parameters. In this paper, the CAE simulation considering the molding condition is performed to predict the faulty cause which appears in the packing process between cavities of injection molding machine. From the results of CAE simulation, the packing phenomena according to the product form and the gate position was investigated to improve the article quality and minimize the various molding defects. The effect of packing pressure and gate number on the injection molding was discussed.