• 한국자동차공학회논문집
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• 제25권1호
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• pp.35-41
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• 2017

The use of engineering plastics in automotive components is increasing with the trend towards improving the car strength and reducing weight. Among the different choices of materials, engineering plastic emerged as the necessary material for achieving lower costs, reduced weight and improved production efficiency. To produce the automotive parts, it is important to predict defect and validation of injection molding prior to design. Injection molding analysis and structural analysis are widely applied as a part of the design process when developing automotive parts. Injection molding analysis, in particular, involves a highly complicated mechanism that requires deep knowledge of polymer properties as well as an analytic approach different from that used for a general isotropic material when the molded material is used as a structural material. This is because the parts made of polymer have pre-stress factors such as intrinsic deformation and residual stress. The most important factors for injection molded plastic products are injection molding condition and cavity design, taking into account ease of molding, mass production and application. Despite optimal injection molding conditions and cavity design, however, glass fiber orientation is critically linked to strength reduction. The application of injection molding and structural coupled analysis provides a low-cost solution for product molding and structural validation, all prior to the actual molding. The purpose of this study involves the validation, pre-study, and solution of defect in injection-molded polymer automotive parts using the simulation software for injection molding and structural coupled analysis. Finally, this thesis provides validation of an injection molding and structural coupled analytic mechanism that can demonstrate the effect of glass fiber orientation on mechanical strength. Design improvement ideas for the injection molded product of PPS (Poly Phenylene Sulfide)+40% glass fiber are also suggested.

• 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.

• 동력기계공학회지
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• 제18권3호
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• pp.51-58
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• 2014

Injection molding simulation provided optimized design results by analyzing quality problems while the product is in assembly or in the process of manufacturing with make automobile plastics. Frequent change of design, change of injection molding, repetition of test injection which was held in the old way can now be stopped. And quality upgrade is expected instead. This report deals with the effect which the position of injection molding automobile console gate and number has on product quality including pressure at end of fill, bulk temperature at end of fill, shear stress of end of fill, residual stress at post filling end, product weld lines and warpage results. Simpoe-Mold simulates the complete manufacturing process of plastic injected parts, from filling to warpage. Simpoe-Mold users, whether they are product designers, mold makers or part manufacturers, can identify early into the design stage potential manufacturing problems, study alternative solutions and directly assess the impact of such part modification, whatever the complexity and geometry of such parts, shell part as plain solid parts.

• 대한기계학회논문집A
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• 제35권12호
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• pp.1619-1625
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• 2011

본 논문의 목적은 3 차원 사출성형 해석을 통하여 컴퓨터 마우스 제작용 이중사출성형 금형의 공정조건을 결정하는 것이다. 적합한 사출성형 공정조건을 선정하기 위하여 사출 시간, 최대 사출압력 및 보압 시간이 사출성형 특성과 제품 품질에 미치는 영향을 정량적으로 고찰하였다. 사출성형 해석 결과로부터 제품의 수축과 휨 변형을 최소화 할 수 있는 최적 사출성형 공정조건을 예측하였다. 최종적으로 사출성형 실험을 수행하여 이종재료로 구성된 컴퓨터 마우스 제품을 제작하였다. 이 결과로부터 선정한 사출성형 공정조건을 적용할 경우 적절한 이종 재료로 구성된 컴퓨터 마우스가 제작될 수 있음을 알 수 있었다.

• Design & Manufacturing
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• 제16권3호
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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.

• Design & Manufacturing
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• 제16권3호
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• pp.50-57
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• 2022

The qualities of the products produced by injection molding are strongly influenced by the process variables of the injection molding machine set by the engineer. It is very difficult to predict the qualities of the injection molded product considering the stochastic nature of the manufacturing process, since the processing conditions have a complex impact on the quality of the injection molded product. It is recognized that the artificial neural network(ANN) is capable of mapping the intricate relationship between the input and output variables very accurately, therefore, many studies are being conducted to predict the relationship between the results of the product and the process variables using ANN. However in the condition of a small number of data sets, the predicting performance and robustness of the ANN model could be reduced due to too many input variables. In the present study, the ANN model that predicts the length of the injection molded product for multiple combinations of process variables was developed. And the accuracy of each ANN model was compared for 8 process variables and 4 important process inputs that were determined by the feature selection. Based on the comparison, it was verified that the performance of the ANN model increased when only 4 important variables were applied.

• 한국산학기술학회논문지
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• 제13권12호
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• pp.5709-5715
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• 2012

플라스틱 소재를 사용한 사출성형부품은 차량 경량화뿐만 아니라 충격흡수를 통한 운전자와 보행자 보호, 제품 디자인의 자율성등의 우수한 장점을 가지고 있어 그 사용량이 급속히 증가하고 있다. 유리섬유함유 플라스틱재료를 이용한 레귤레이터 레일의 사출성형시 불량률최소화를 위해 유한요소해석과 실험계획법을 사용하여 성형공정중의 휨변형량(warpage)과 수축변형를 최소화할 수 있는 최적의 성형조건을 제시하고자 한다. 또한 유리섬유의 유동성과 냉각효율의 확보를 위해 최적의 게이트 및 런너방식을 설정한다. 사출금형을 제작하여 제품을 성형제작한 후 휨변형등의 성능확인을 통해 해석방법의 유효성과 최적화의 실효성을 확인하고자 한다.

• 소성∙가공
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• 제18권2호
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• pp.177-182
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• 2009

A work of thin-walled outsell injection molding technology for a plastic part of moldframe applicable in a display product was performed in the present study. The thin-walled plastic part is one of the core parts in the display product, which supports and protects a light guide plate and back light unit from external environmental conditions. It globally has the shape of rectangular and surrounds the light guide plate and back light unit for each class of inch, however, the cross section of the part is not clear to define the thickness. This causes the difficult problem of injection molding itself for the part. Moreover, a metal outsell part makes a difficult problem in injection molding over it. Because the mold temperature control of the parts are not uniform in thickness direction due to the metal part. A careful injection melding analysis and injection mold design from the analysis results have to be proceeded to obtain a production of precision moldframe. Therefore, optimization for injection molding process and analysis of warpage characteristics were studied. Consequently, it was possible from the presented virtual manufacturing process that the manufacturing of precision thin-walled outsell moldframe.

• 한국정밀공학회지
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• 제25권12호
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• pp.100-106
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• 2008

In this paper, the injection molding process of automotive seat-back cover is analyzed in terms of simulation and of experiment. FE analysis was used to obtain molding conditions such as injection pressure, filling pattern, packing, shrinkage. Vacuum system for low pressure injection molding is developed in the experiment. Low pressure injection molded parts have been compared with conventional molded parts in terms of molding quality and mechanical properties. Based on the results, good product and the productivity improvement can be obtained in low pressure injection molding for automotive seat-back cover.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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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.