• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.217-220
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• 2007

The quality of product depends on a lots of injection molding conditions at each operation stage and mainly was effected by pressure and temperature. The objective of this study was to investigate the relationship between the product quality and the distribution of pressure in a cavity with operation condition change. Specimen having some ribs was designed and the mold was prepared and temperature and pressure sensors were installed in the mold. PP was used and pressure distribution was measured with operation condition change. The relationship between the sink mark and the pressure in a cavity was investigated by measurement of the level of sink mark. The level of sink mark was decreased at the cross of plane and rib by increasing of pressure in a cavity, so the pressure should be controlled properly in order to get the required quality.

• Design & Manufacturing
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• v.17 no.2
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• pp.62-69
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• 2023

In this study, we conducted a study on the optimization of injection molding conditions to minimize deformation of plastic product. The charging management system housing of the vehicle was selected as the research subject. Melting temperature, cooling temperature, packing time, and packing pressure were selected as the main factors expected to affect the deformation of molded products. Each main factor was divided into 5 levels. Optimization of injection molding conditions to minimize deformation was performed using the Taguchi Method. We performed an analysis of variance (ANOVA) to identify significant factors affecting the deformation of plastic product. In order to select injection molding conditions that minimize deformation of plastic products, injection molding analysis was additionally performed for insignificant factors. We then compared the deformation of the molded part before and after optimization. As a result of comparing the injection analysis results of the basic conditions and the injection analysis results of the optimal conditions, it was confirmed that the amount of deformation after optimization was improved by about 10.9%.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.11
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• pp.83-88
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• 2002

In order to predict the moldability of a injection molded part, a simulation of filling is needed. Short shot is one of the most frequent troubles encountered during injection molding process. The adjustment of process conditions is the most economic way to troubleshoot the problematic short shot in cost and time since the mold doesn't need to be modified at all. But it is difficult to adjust the process conditions appropriately in no times since it requires an empirical knowledge of injection molding. In this paper, the intelligent CAE system synergistically combines fuzzy-neural network (FNN) for heuristic knowledge with CAE programs for analytical knowledge. To evaluate the intelligent algorithms, a cellular phone flip has been chosen as a finite element model and filling analyses have been performed with a commercial CAE software. As the results, the intelligent CAE system drastically reduces the troubleshooting time of short shot in comparison with the experts' conventional methodology which is similar to the golden section search algorithm.

• Transactions of Materials Processing
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• v.16 no.1 s.91
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• pp.36-41
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• 2007

Thermoplastic elastomer(TPE) can be recycled and molded such as commercial thermoplastic. Therefore TPE has being widely applied on automobile, household and etc. in these days. This study shows the variation of mechanical properties and shrinkage on TPE moldings for variation of injection molding conditions such as injection pressure, holding pressure, melt temperature, mold temperature and etc. Mechanical properties in relation to tensile strength, hardness and shrinkage in connection with precision dimension of part are investigated. The tensile strength and shrinkage of the experimental TPEs are mainly influenced by injection pressure and melt temperature. All injection molding conditions scarcely affect on hardness. To verify the variation of tensile strength and shrinkage, morphology of TPE molding was scanned by the SEM. The morphology showed that as the melt temperature increased, the rubber particles on the TPE became smaller and widely were dispersed. This behavior of rubber particles influenced on the increase of tensile strength.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2005.10a
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• pp.265-268
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• 2005

Thermoplastic elastormer (TPE) has many advantages such as high flexibility, high elasticity and high elongation, etc. TPE is easily molded such as plastic materials, therefore, many TPE parts are applied as home appliances and mechanical parts. However, if TPE is once molded, its mechanical properties are changed by injection molding conditions such as melt temperature, mold temperature, injection pressure and holding pressure, etc. In this study, the influences of the injection molding condition on the mechanical properties of thermoplastic vulcanizates(TPVs), which is one of the TPE, were investigated. By the injection molding experiment, as increasing the melt temperature, the tensile strength, shrinkage and hardness decreased. By the scanning electron microscope (SEM) analyzing the TPVs' crystallization, the morphology was affected by the melt temperature.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.76-81
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• 2004

Injection molding is the most widely used process fir the industrial forming of plastic articles. 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 experientially. Using the image processing method, the fiber orientation distribution of welding pars in injection-molded products is assessed. And the effects of fiber content and injection mold shapes on the fiber orientation in case of fiber reinforced polymeric composites are studied experimentally.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.1
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• pp.73-80
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• 2005

Injection molding is the most widely used process for the industrial forming of plastic articles. 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 welding parts in injection-molded products is assessed. And the effects of fiber content and injection mold shapes on the fiber orientation in case of fiber reinforced polymeric composites are studied experimentally.

• Design & Manufacturing
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• v.16 no.3
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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.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.186-191
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• 2008

Injection molding is used to manufacture various kinds of products including very thin plate like frames. One such product is Door video phone system Panel which is our present object of investigation. During the molding process, large deflection problems in the final product always provokes for the several trials for the accuracy. To solve the issue we use Moldflow6.2 which is injection molding simulation software. It simulates the product exactly the way it happens in actual manufacturing environment. Differences in the geometrical parameters such as thickness changes will lead to the large deformation problems in the frame. However in our present study we simulate it in a well defined condition under several trials. Interesting observations are made in minimizing the deformation of the frame during the injection molding process. This way of approach is very useful in further works if any changes have to be made in the product.

• Design & Manufacturing
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• v.6 no.1
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• pp.67-72
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• 2012

Most of the plastics products have been manufactured by injection molding. Molding trouble in injection-molded parts is caused by changing a molding product and molding process condition, etc. In this study, warpage in the injection molded part have been studied. Specimens are rectangular flat shape with and without ribs. Non-crystalline resins (ABS+GF30%, PC+GF30%) and crystalline resins (PP+GF30%, PA66+GF30%) were used for material. Flat shape ribs showed higher warpage than flat shape without rib by 10 to 41%. the specimens with ribs that are located parallel to flow direction has higher warpage than the specimens with rib that are located perpendicular to flow direction by 11 to 50%. crystalline resins have higher warpage than non-crystalline resins by 22 to 78%. Warpage decreases as packing time increases as injection temperature increases.