• Transactions of Materials Processing
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• v.16 no.1 s.91
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• pp.54-60
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• 2007

Optimum injection molding condition for a box mold was searched by the Response Surface Analysis(RSA) with the aid of process monitoring system(PMS). Process variables on the control panel of the injection molding machine such as barrel temperatures, screw speed profile, holding pressures, etc. cannot guarantee the uniformity of the material variables directly related with the state of the product in the mold cavity. In order to make sure the state of the resin in the cavity, pressures and temperatures in the cavity, runner and nozzle were monitored in the experiment with the PMS. To accomplish the consistency of the molding process, dependent variables such as the switchover point and holding time were searched with the PMS. With a proper objective function about deflection of the box-type product, the optimum injection molding condition was obtained.

• Transactions of Materials Processing
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• v.10 no.5
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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.

• 한국금형공학회:학술대회논문집
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• 2008.06a
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• pp.89-94
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• 2008

The injection molding process is a predominant method for producing plastic parts. In order to maximize productivity and molding quality in a injection mold, it is important that each cavity in a multi-cavity injection mold is identical. This requires that cavity dimensions should be identical and delivery system of melt to each cavity have to be the same. Despite the geometrically balanced layout in multi-cavity injection mold more than 4 cavities, it has been observed that the filling in each cavity results in imbalances. Most of cases, this phenomenon of filling imbalances have a bad effect on dimension accuracy, warpage, molding appearance and strength of molding parts. In this study, experiment were conducted to investigate the effect of filling imbalances on the molding quality(surface gloss, shrinkage, tensile strength) in the multy-cavity injection mold.

• Design & Manufacturing
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• v.1 no.1
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• pp.1-5
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• 2007

Double injection molding process is very efficient molding-method for molding the products which is consist of multi-materials. Fuel-tube holder which is necessary for automobil power train and circulation systems is composed of plastic and rubber materials to minimize the vibration and pulsation noises. In existing process, fuel-tube holder was made by the insert molding process or assembly process after molding. If fuel-tube holder is manufactured by double injection molding process, it may be realize to improve the product quality, efficiency of molding-process and retrenchment of manufacturing cost. In this study, for manufacturing fuel-tube holder by double injection molding process, the analysis of joining characteristics between PA6(polyamide 6) and TPE(thermoplastic elastomer) was executed and the double injectin mold for molding fuel-tube holder with core toggle mechanism was fabricated. Finally, fuel-tube holder was molding using fabricated double injection mold.

• Design & Manufacturing
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• v.8 no.1
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• pp.1-4
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• 2014

Large injection molds commonly have molding defects such as flashes and variation of product thickness. In this study, we conducted injection molding CAE analysis to find out the cavity pressure and structural analysis to find out mold deflection as input load conditions injection pressure obtained from injection molding analysis. As the results from CAE analysis, we found which element is the most effective on the mold deflection and we suggested a mold design to minimize the mold deflection.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.579-580
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• 2006

In this study, an injection mold of tensile test specimen was manufactured by international standard. Pressure and temperature in the cavity of the injection mold was measured by sensors. Simulation of injection molding process was performed with the same condition of experiment and linear structural tensile analysis was also performed with the initial condition of the residual stress induced by injection molding analysis. Normalized elastic coefficient of tensile test was compared with that of structural analysis. It was shown that the residual stress induced by injection molding has an effect on both the experiment of tensile test and linear structural analysis.

• 한국금형공학회:학술대회논문집
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• 2008.06a
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• pp.99-102
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• 2008

Large injection molds commonly have molding defects such as flashes and variation of product thickness. In this study, we conducted injection molding CAE analysis to find out the cavity pressure and structural analysis to find out mold deflection as input load conditions injection pressure obtained from injection molding analysis. As the results from CAE analysis, we found which element is the most effective on the mold deflection and we suggested a mold design to minimize the mold deflection.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.252-257
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• 2005

Injection molding is a very important industrial process for the manufacturing of plastics objects. During an injection molding process of composites, the fiber-matrix separation and fiber orientation are caused by the flow of molten polymer/fiber mixture. As a result, the product tends to be nonhomogeneous and anisotropic. Hence, it is very important to clarify the relations between separation' orientation and injection molding conditions. So far, there is no research on the measurement of fiber orientation using image processing. In this study, the effects of fiber content ratio and molding condition on the fiber orientation-angle distributions are studied experimentally. Using the image processing method, the fiber orientation distribution of weld-line in injection-molded products is assessed. And the effects of fiber content and injection mold-gate conditions on the fiber orientation are also discussed.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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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.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.946-949
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• 2005

It is very a time-consuming and error-prone process to obtain the optimal injection condition, which can produce good injection molding products in some operational variation of facilities, from a seed injection condition. This study proposes a new approach to search the optimal injection molding condition using a neural network and a genetic algorithm. To estimate the defect type of unknown injection conditions, this study forces the neural network into learning iteratively from the injection molding conditions collected. Major two parameters of the injection molding condition - injection pressure and velocity are encoded in a binary value to apply to the genetic algorithm. The optimal injection condition is obtained through the selection, cross-over, and mutation process of the genetic algorithm. Finally, this study compares the optimal injection condition searched using the proposed approach. with the other ones obtained by heuristic algorithms and design of experiment technique. The comparison result shows the usability of the approach proposed.