• 한국분말야금학회:학술대회논문집
• /
• 한국분말야금학회 1994년도 추계학술대회강연 및 발표대회 강연및 발표논문 초록집
• /
• pp.6-6
• /
• 1994

• 한국산학기술학회논문지
• /
• 제18권10호
• /
• pp.42-47
• /
• 2017

본 연구는 광통신용 아답타의 필수품인 플랜지 개발을 위한 금속분말 사출성형해석에 관한 내용이다. 금속분말 사출성형법은 세라믹 또는 스테인레스 분말과 바인더를 혼합하여 복잡한 형상의 사출성형품을 제조하는 기술로써, 지금까지 가공기술로 제작이 복잡하거나 생산성이 저조한 제품들에 대한 생산을 대체 할 수 있는 기술로 관심을 받고 있다. 연구 목적은 기존의 기계가공을 통해 제작했던 제품에 대해 공정을 최소화하기 위함이다. 사출성형해석을 위해 먼저 스테인레스 계 STS316 금속분말과 바인더를 6대4 비율로 혼합하여 과립형 펠렛의 사출 성형재료를 완성하여 해결하였다. 이후, 3차원 모델링, 모델의 메시화 작업 등을 수행하여 최적의 사출성형 해석조건(금형 온도, 용융 온도, 사출 시간, 사출 온도, 사출 압력, 충진 시간 및 냉각 시간 등)을 도출하였다. 해석결과 성형품은 최초 사출 후 13.29초가 경과되면 취출이 가능하였다. 또한 용융수지는 스프루, 러너, 게이트를 거쳐 금형 내부까지 유동 및 충전이 안정적으로 진행되어 양호한 성형품의 제조가 기대되었다.

• 한국분말야금학회:학술대회논문집
• /
• 한국분말야금학회 2006년도 Extended Abstracts of 2006 POWDER METALLURGY World Congress Part2
• /
• pp.771-772
• /
• 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
• /
• 제16권3호
• /
• pp.1-8
• /
• 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.

• 한국분말야금학회:학술대회논문집
• /
• 한국분말야금학회 2006년도 춘계학술강연 및 발표대회강연 및 발표논문 초록집
• /
• pp.23-23
• /
• 2006

• 기술혁신학회지
• /
• 제5권3호
• /
• pp.382-395
• /
• 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.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1997년도 추계학술대회 논문집
• /
• pp.833-836
• /
• 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
• /
• 제2권5호
• /
• pp.5-10
• /
• 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
• /
• 제17권4호
• /
• pp.72-78
• /
• 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.

• 한국진공학회:학술대회논문집
• /
• 한국진공학회 2011년도 제40회 동계학술대회 초록집
• /
• pp.464-464
• /
• 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.