• 지능정보연구
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• 제8권1호
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• pp.159-173
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• 2002

사출금형 공정계획이란 금형설계를 완료한 후에 설계된 금형을 경제적, 효율적으로 생산하기 위하여 수행하는 제조공정에 대한 계획이다. 이러한 공정계획은 전문가의 수작업에 의한 수립에 의한 문제, 전문가의 양성과 부족현상, CAD/CAM 시스템 보급 및 생산형태의 다품종소량화 현상에 의해 자동화가 필요하다. 본 연구에서는 사례기반추론(Case-Based Reasoning)을 이용하여 IIMPPS(Intelligent Injection Mold Process Planning System)라는 사출금형 공정계획시스템을 개발하였다. 사출금형 공정계획을 자동화하기 위해서 사례기반 추론을 사용한 이유는 사출금형이라는 제품은 금형의 종류와 구조 등에 따라 공정계획이 매우 다양하게 수립되지만, 성형품의 용도나 품명이 같은 경우에는 공정이 거의 유사하여, 과거에 수립된 공정계획이 새로운 금형의 공정 계획에 매우 유용하게 사용될 수 있기 때문이다. 그리고 본 연구에서는 IIMPPS의 타당성을 평가하기 위하여 전문가의 정성적인 평가와 공정계획의 정확도 평가를 수행하였다.

• 한국지능시스템학회논문지
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• 제11권6호
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• pp.465-469
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• 2001

In injection molding short shot is one of the frequent and fatal defects. Experts of Injection molding usually adjust process conditions such as injection time, mold temperature, and melt temperature because it is most economic way in time and cost. However, it is difficult task to find appropriate process conditions for troubleshooting of short shot as injection molding process is a highly nonlinear system and process conditions are coupled. In this paper, a modular fuzzy neural network (MFNN) has been applied to injection molding process to shorten troubleshooting time of short shot. Based on melt temperature and fill time, a reasonable initial mo이 temperature is recommenced by the NFNN, and then the mold temperature is inputted to injection molding process. Depending on injection molding result, specifically the insufficient quantity of an injection molded part. and appropriate mold temperature is recommend repeatedly through the NFNN.

• 한국기계가공학회지
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• 제12권5호
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• pp.94-101
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• 2013

Injection molding process is one of the most important methods to produce plastic parts with high efficiency and low cost. The objective of this study is to implement the best plastic injection molding process for LED TV speaker frame. Moldflow analysis and simulation of plastic injection molding process were carried out in order to predict optimal modeling operation conditions and then injection molded part was produced various type of resin temperature, filling time and injection pressure variation. the result was that the best injection molding condition is set as 60bar pressure, 2sec filling time and $310^{\circ}C$ degree. The study result would be useful to variety of plastic injection molding process.

• 한국정밀공학회지
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• 제22권4호
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• pp.144-150
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• 2005

Gas-assisted injection molding (GAIM) process is reducing the injection pressure during mold filling required as well as the shrinkage and warpage of the part and cycle time. Despite of these advantages, this process introduces new parameters and makes the application more difficult because the process interacts between gas and melt during injection molding process. Important GAIM factors that involved in this process include gas penetration design, locations of gas injection points, shot size, gas injection delay time as well as common injection molding parameters, gas pressure and gas injection time. In this study, the experiments were conducted to investigate effects of GAIM process variables on the gas penetration for PP and ABS moldings by changing gas injection point. Taguchi method was used fer the design of experiment. When the gas was injected at cavity's center, the most effective factor was shot size. When the gas was injected at cavity's end, the most effective factor was melt temperature. Injection speed was also an effective factor in GAIM process.

• 소성∙가공
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• 제20권1호
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• pp.5-10
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• 2011

The main objective of the present study is to predict the process window of injection-compression molding corresponding to the capability of an injection machine for fabricating 7 inch LGP. The open distance and volume filled after injection stage were found to be two important factors that affect critical requirements such as flow length, injection pressure and clamping force for the process. Process window for the key factors was also predicted by response surface method. As a result, predicted process window for open distance and volume filled after injection stage satisfying the critical requirement with a given injection machine was in the range of 60 ~ 75%, and 104.00 ~ 104.25%, respectively.

• 한국기계가공학회지
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• 제11권3호
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• pp.92-97
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• 2012

Injection mold is a manufacturing process used to produce parts of complicated shape at a low cost. Many factors affect the quality of injection molded part during injection molding process. A study on the optimization of injection mold is progressed by using a simulation software like Moldflow. Filling, packing and cooling phases of injection molding processes are analyzed according to the mold design considering the shrinkage of molded part, the degree of filling rate and the wearing of a mold. Taguchi method is applied to analyze the significance of processing parameter and the dynamic characteristics according to the variation of processing parameters. From the results, the mold temperature and packing pressure influenced the shrinkage of injection molded part.

• 정보저장시스템학회논문집
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• 제1권2호
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• pp.192-196
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• 2005

In this paper, we investigated the possibility of replicating patterned media by nano-injection molding process with a metallic nano-stamper. The original nano-master was fabricated by E-beam lithography and ICP etching process. The metallic nano-stamper was fabricated using a nanoimprint lithography and nano-electroforming process. The nano-patterned substrate was replicated using a nano-injection molding process without additional etching process. In nano-injection molding process, since the solidified layer, generated during the polymer filling, deteriorates transcribability of nano patterns by preventing the polymer melt from filling the nano cavities, an injection-mold system was constructed to actively control the stamper surface temperature using MEMS heater and sensors. The replicated polymeric patterns using nano-injection molding process were as small as 50 nm in diameter, 150 nm in pitch, and 50 nm in depth. The replicated polymeric patterns can be applied to high density patterned media.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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• pp.83-86
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• 2001

In injection molding, short shot is one of the frequent and fatal defects. Experts of injection molding usually adjust process conditions such as injection time, mold temperature, and melt temperature because it is the most economic way in time and cost. However it is a difficult task to find appropriate process conditions for troubleshooting of short shot as injection molding process is a highly nonlinear system and process conditions are coupled. In this paper, a fuzzy neural network(FNN) has been applied to injection molding process to shorten troubleshooting time of short shot. Based on melt temperature and fill time, a reasonable initial mold temperature is recommended by the FNN, and then the mold temperature is inputted to injection molding process. Depending on injection molding result, specifically the insufficient quantity of an injection molded part, an appropriate mold temperature is recommend repeatedly through the FNN.

• International Journal of Precision Engineering and Manufacturing
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• 제7권2호
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• pp.8-11
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• 2006

Gas-assisted injection molding (GAIM) process reduces the required injection pressure during mold filling stage as well as the shrinkage and warpage of the part and cycle time. Despite of these advantages, this process needs new parameters and makes the application more difficult because gas and melt interact during the injection molding process. Important GAIM factors involved in this process are gas penetration design, locations of gas injection points, shot size, delay time to inject gas as well as common injection molding parameters. In this study, the experiments are conducted to investigate effects of GAIM process variables on the gas penetration for PP (Polypropylene) and ABS (Acrylonitrile Butadiene Styrene) moldings by changing the gas injection point. Taguchi method is used for the design of the experiments. When the gas is injected at a cavity's center, the most effective factor is the shot size. When the gas is injected at a cavity's end, the most effective factor is the melt temperature. The injection speed is also an effective factor in GAIM process.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.882-885
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• 2002

Magnesium alloys are very attractive materials for appling to the development of autemobile parts or electric goods where light weight and higher stiffness. Due to higher ratio of strength vs. weight and stillness vs. weight, various magnesium alloys are well applied in much weight saving design applications though extrusion or die-casting process. However for the requisites of higher strength and weight savings, some new fabrication processes has been and it can be realized though the aid of injection modeling technology. To obtain the parametric data base for the injection molding process, various experiments were executed for AZ91D magnesium alloy. This paper propose the optimum condition of injection temperature, first and second pressure. the process was lined-up successfully often changing the injection unit. fluid pressure system from the conventional plastic injection molding process.