• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.772-777
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• 2001

The gas-assisted injection molding process is often perceived to be unpredictable, because of the extreme sensitivity of the gas. Since a slight change in design or process parameters can significantly change the resulting gas penetration, few designers and molders have the level of experience with the new gas-assisted injection molding process required for the development of new parts. This paper is concerned with the unified molding for a thick cosmetic chest by using gas-assisted injection molding. CAE analysis was carried out to design the part and the gas channel without inducing sink marks. And based on the part weight measurement, the processing parameters to control gas penetration percentage were chosen through the method of design of experiments. A thick cosmetic chest was successfully produced using the gas assist technology. The sink mark issue associated with the conventional injection molded parts was resolved. Weight savings and cycle-time reduction were also achieved.

• 소성∙가공
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• 제10권5호
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• pp.402-410
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• 2001

The gas-assisted injection molding process is often perceived to be unpredictable, because of the extreme sensitivity of the gas. Since a slight change in design or process parameters can significantly change the resulting gas penetration, few designers and molders have the level of experience with the new gas-assisted injection molding process required for the development of new parts. This paper is concerned with the unified molding for a thick cosmetic chest by using gas-assisted injection molding. CAE analysis was carried out to design the part and the gas channel without inducing sink marks. And based on the part weight measurement, the processing parameters to control gas penetration percentage were chosen through the method of design of experiments. A thick cosmetic chest was successfully produced using the gas assist technology. The sink mark issue associated with the conventional injection molded parts was resolved. Weight savings and cycle-time reduction were also achieved.

• Design & Manufacturing
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• 제15권4호
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• pp.24-31
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• 2021

In this study, an artificial neural network model was constructed to convert CAE analysis data into similar experimental data. In the analysis and experiment, the injection molding data for 50 conditions were acquired through the design of experiment and random selection method. The injection molding conditions and the weight, height, and diameter of the product derived from CAE results were used as the input parameters for learning of the convert model. Also the product qualities of experimental results were used as the output parameters for learning of the convert model. The accuracy of the convert model showed RMSE values of 0.06g, 0.03mm, and 0.03mm in weight, height, and diameter, respectively. As the next step, additional randomly selected conditions were created and CAE analysis was performed. Then, the additional CAE analysis data were converted to similar experimental data through the conversion model. An artificial neural network model was constructed to predict the quality of injection molded product by using converted similar experimental data and injection molding experiment data. The injection molding conditions were used as input parameters for learning of the predicted model and weight, height, and diameter of the product were used as output parameters for learning. As a result of evaluating the performance of the prediction model, the predicted weight, height, and diameter showed RMSE values of 0.11g, 0.03mm, and 0.05mm and in terms of quality criteria of the target product, all of them showed accurate results satisfying the criteria range.

• 한국자동차공학회논문집
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• 제10권4호
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• pp.23-32
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• 2002

Combustion and flame propagation characteristics of the liquid phase LPG injection (LPLI) engine were investigated in a single cylinder optical engine. Lean bum operation is needed to reduce thermal stress of exhaust manifold and engine knock in a heavy duty LPG engine. An LPLI system has advantages on lean operation. Optimized engine design parameters such as swirl, injection timing and piston geometry can improve lean bum performance with LPLI system. In this study, the effects of piston geometry along with injection timing and swirl ratio on flame propagation characteristics were investigated. A series of bottom-view flame images were taken from direct visualization using an W intensified high-speed CCD camera. Concepts of flame area speed, In addition to flame propagation patterns and thermodynamic heat release analysis, was introduced to analyze the flame propagation characteristics. The results show the correlation between the flame propagation characteristics, which is related to engine performance of lean region, and engine design parameters such as swirl ratio, piston geometry and injection timing. Stronger swirl resulted in foster flame propagation under open valve injection. The flame speed was significantly affected by injection timing under open valve injection conditions; supposedly due to the charge stratification. Piston geometry affected flame propagation through squish effects.

• 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.

• Design & Manufacturing
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• 제13권3호
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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.

• 한국기계가공학회지
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• 제11권5호
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• pp.121-128
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• 2012

The optimization of cooling system parameters for designing injection mold is very important to acquire the highest part quality. In this paper, the integration of computer simulations of injection molding and Analysis of Variance(ANOVA) with orthogonal array was used as a design tool to optimize the cooling system parameters aimed at minimizing the part warpage. The design optimizer was applied to find the optimum levels of cooling system parameters for a dustpan. This optimization resulted in more uniform temperature distribution over the part and significant reduction of a part warpage, showing the capability of present method as an effective design tool. The whole optimization process was performed systematically in a proper number of cooling simulations. The design optimizer can be utilized effectively in the industry practice for designing mold cooling system with less cost and time.

• 터널과지하공간
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• 제27권5호
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• pp.324-332
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• 2017

본 연구에서는 암반 절리 내 점성유체 주입시 주입 설계변수가 주입 성능에 미치는 영향을 평가할 목적으로 UDEC 프로그램을 사용하여 1차원 선형유동 해석을 수행하였다. 주입 설계변수로는 주입 압력, 유체 압축률, 주입재의 항복강도 및 점성도의 시간의존성, 주입 압력에 의한 절리의 역학적 변형을 설정하였으며, 주입재의 침투거리 및 주입 유량을 통해 주입 성능을 평가하였다. 수치해석 결과는 이론해를 통하여 파악한 주입 성능양상과 유사한 결과를 보였다. 주입재의 항복강도 및 점성도의 시간의존성을 고려하지 않을 경우, 주입재의 누적 주입량은 시간의존성을 고려한 해석에 비하여 약 1.2배 크게 평가되었다. 또한, 수리-역학 연계해석결과로부터 주입 압력에 의한 절리의 역학적 변형이 발생하는 경우, 절리 간극이 일정한 수리유동 해석에 비하여 누적 주입량이 약 4.4배 늘어나는 결과를 보였다.

• 한국지반환경공학회 논문집
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• 제10권6호
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• pp.125-133
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• 2009

유체를 주입하여 암반을 파쇄하는 기술은 지열이나 석유 및 가스 등을 추출하는데 널리 사용되고 있는 방법이다. 본 기술을 적용 시 단일균열이 형성되면 이러한 에너지를 추출하는데 가장 이상적이다. 그러나 이러한 단일균열의 형성은 매우 드문 현상이며 분할된 형태의 균열생성이 흔한 현상이다. 이에 균열간 기계적 상호작용의 영향으로 설계변수에서도 단일균열을 가정하고 적용되었던 값과 차이를 보일 것으로 예상된다. 본 연구에서는 균열이 분할 생성되었을 경우 기계적인 상호작용을 고려할 수 있는 수치해석기법을 기존의 개발된 모델과 연계하여 설계변수인 길이, 균열폭, 그리고 압력을 계산하였다. 그 결과 균열의 형성은 이렇게 유체를 주입하여 암반을 파쇄 시 사용되는 설계변수에 상당한 영향을 끼치는 것으로 나타났다.

• Design & Manufacturing
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• 제14권3호
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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.