• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 1994.10b
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• pp.6-6
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• 1994

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.10
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• pp.42-47
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• 2017

In this study,we conducted an injection molding analysis of metal powder materials for the development of flanges, which are necessary adapters for optical communication. The metal powder injection molding process is a technique for producing an injection molded article having a complicated shape by mixing ceramic or stainless powder and binders. It is used to produce products which require complex processing technology or for which the productivity is low. The purpose of this study is to minimize the manufacturing processing of products which are manufactured through existing mechanical processing procedures. For the injection molding analysis, we mixed stainless STS316 metal powder with binders at a ratio of 6 to 4 to make molding materials consisting of granular pellets. Then, three-dimensional modeling and meshing were carried out to obtain the optimal injection molding analysis conditions(molding temperature, melting temperature, injection time, injection temperature, injection pressure, packing time and cooling time). As a result of the analysis, it was discovered that the inlet became available 13.29 seconds after the first injection. Also, as the flowing and packing in the melt through the sprue, runner and gate were stable, it is expected that good molds can be manufactured.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.09b
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• pp.771-772
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• 2006

Mechanical properties of metal injection molded titanium and titanium alloy parts were investigated in this study. Material powders with low oxygen content and spherical shape were obtained by electrode induction-melting gas atomization which could melt and atomize titanium and titanium alloy bars with no touch on crucible or tundish. Tensile specimens were fabricated from obtained powders by metal injection molding process. Tensile strength of the specimens increases with increasing oxygen content. This result corresponds to a tendency of wrought metal.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 2006.04a
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• pp.23-23
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• 2006

• Journal of Korea Technology Innovation Society
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• v.5 no.3
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• pp.382-395
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• 2002

Metal Injection Molding(MIM) is a technology without any mechanical processing, which is a promising area backed up by nano powder technology developed in late 1990's. The market was about 24 billion U$ in 1999. Many applications are made in process development, uses, powder making, hindering and sintering, of which order is in terms of the number of patents. This technologies are mainly developed by US firms, and applied by Japanese firms. Europe and Korea are still catch-up stage. More efforts should be made in this field because new opportunities are opening, thanks to nano technology.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.833-836
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• 1997

Magnesium alloys are one of light weight material. Strength and stiffness of Magnesium alloys are enough to use for commercial product. Demand for strong, lightweight parts several computer and electronics have driven much of Magnesium injection molding's growth so far. And it is eighth most abundant resource on earth. In electronic device, electromagnetic interface and electrostatic discharge can affect performance. Magnesium injection molding is similar to normal plastic injection molding process. But some process condition is different. Especially injection speed and process temperature are so differs from other injection molding system. It just start for make something. But Magnesium injection molding is one of best alternate process for producing metal alloy part.

• Design & Manufacturing
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• v.2 no.5
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• pp.5-10
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• 2008

Recently, ultra precision and light-weight micro products are needed in various industries. Injection molding products with metal insert material is often satisfied with light-weight and precision simultaneously. The researches on macro-size insert deformation have been performed but, a research on micro-size insert is meager. In this paper, the injection molding product with $300{\mu}m$ thin foil insert is designed and insert injection molding process is performed. Finally, the deformation of thin foil insert is analyzed according to insert feature and gate length.

• Design & Manufacturing
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• v.17 no.4
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• Proceedings of the Korean Vacuum Society Conference
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• 2011.02a
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• pp.464-464
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• 2011

We report a method to fabricate a large-area metal nanowire-grid polarizer. Liquid-bridge-mediated nanotransfer molding (LB-nTM) is based on the direct transfer of metal nanowires from a mold to a transparent substrate via liquid layer. A metal particle solution is used as an ink in the LB-nTM, which can be used for the formation of metal nanowires. The nanowires have higher depth are preferred for high transmittance. The height of nanowires that we made is about 140 nm. Large-area WGP is fabricated with good average transmittance of 74.89% in our measuring range.