• Transactions of Materials Processing
• /
• v.15 no.5 s.86
• /
• pp.347-353
• /
• 2006

The delivery system such as sprue, runner and gate is a waste of resin in injection molding operation. In this study the reduction of runner size has been investigated using injection molding CAE softwares, Moldflow and Moldex3D, and commercial CFD Softwares, Fluent and Polyflow. To verify the computational results experiment was performed. There were three considerations in deciding optimal runner size in this study: minimum pressure at the gate that makes resin fully filled in the cavity, minimum runner size that compensates shrinkage of resin in the cavity, and frozen layer thickness formed in the runner during injection. Through the computer simulations the optimal runner size that satisfies those three considerations has been decided. Although the computational results among the softwares were slightly different, it was enough to predict the optimal runner size. The previous runner diameter was 8 mm and predicted optimal size was 5 mm. This was verified by injection molding experiment. Thus, the way of CAE application in deciding optimal runner size adapted in this study would be appropriated.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2006.05a
• /
• pp.363-366
• /
• 2006

The delivery system such as sprue, runner and gate is a waste of resin in injection molding operation. In this study the reduction of runner size has been investigated using injection molding CAE softwares, Moldflow and Moldex, and commercial CFD Softwares, Fluent and Polyflow. To verify the computational results experiment was performed. There were three considerations in deciding optimal runner size in this study: minimum pressure at the gate that makes resin fully filled in the cavity, minimum runner size that compensates shrinkage of resin in the cavity, and frozen layer thickness formed in the runner during injection. Through the computer simulations the optimal runner size that satisfies those three considerations has been decided. Although the computational results among the softwares were slightly different, it was enough to predict, the optimal runner size. The previous runner diameter was 8 mm and predicted optimal size was 5 mm. This was verified by injection molding experiment. Thus, the way of CAE application in deciding optimal runner size adapted in this study would be appropriated.

• Design & Manufacturing
• /
• v.15 no.4
• /
• pp.24-31
• /
• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2007.10a
• /
• pp.271-274
• /
• 2007

Housing and insulation of electrical connectors are made of plastic resin by injection molding process. The metallic inner tube is easily deformed by high pressure during the injection process. In order to prevent deformation of the inner tube, it is desirable to simulate it by structural CAE analysis. However, it takes a long time to calculate the stress- of the part by commercially available injection molding CAE software with sufficient accuracy. In this study, structural analysis in conjunction with injection molding analysis is proposed to improve accuracy of the structural analysis. Pressure distribution on the inner tube is predicted by the injection molding CAE analysis, and then mapped onto the mesh of structural analysis by a mapping algorithm developed in this study. As a result reliable result is obtained in shorter time than the conventional method. The predicted deformation of the inner tube is compared with the actual part after experiment.

• Design & Manufacturing
• /
• v.17 no.1
• /
• pp.27-32
• /
• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• Design & Manufacturing
• /
• v.2 no.3
• /
• pp.16-21
• /
• 2008

In recent, the demands of household cases and disposable products is increased significantly because a living standard of newly-emerging nations was risen. Therefore, multi-cavity mold and stack mold for the realization of high-productivity have been researched in forefront nations. In this paper, CAE analysis for minimizing the mold core deformation was performed. Finally, 64 cavities injection mold for molding cap which has the insert-type core was fabricated according to the result of CAE analysis.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1999.03b
• /
• pp.213-217
• /
• 1999

An expert system is developed for rational and efficient design of multi-component injection molding which is a fairly new manufacturing technique to produce plastic parts by injecting two or more materials sequentially using multiple injection units in a single machine into a single rotary mold. The knowledge base used in the present design system is primarily composed of two parts ; knowledge from domain expert and knowledge from CAE analysis. The present expert system has hour main modules ; general design guidelines for injection molding specific guidelines for multi-component injection molding redesign guidelines from the result of the CAE analysis and finally troubleshooting for multi-component injection molding. To show the validity of the present design methodology two shop floor design problems were tested ; design and fabrication of timing belt cover and power window's assist knob by using multi-component injection molding.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2010.11a
• /
• pp.445-446
• /
• 2010

• Elastomers and Composites
• /
• v.47 no.4
• /
• pp.347-354
• /
• 2012

There exist many merits in designing products and setting operational condition when computer aided engineering (CAE) is adopted in injection molding process. CAE also gives increasing efficient of molding, reducing developing time of product, and maintaining high quality products. Specially, it suggests design guidelines for new products and reducing wasting time to get steady state. Two and three dimensional computer simulations are available in injection molding and those results are somewhat different. However there are no guidelines for 2D and 3D computer simulations in using CAE in injection molding even though it is widely used in plastic industry. In this study, two and three dimensional computation results were compared for various part thickness, part shape, and number of finite element. Subsequently computational results were compared with experimental data such as pressure and temperature. The guidelines in two and three dimensional CAE analysis have been suggested through this study.

• 한국금형공학회:학술대회논문집
• /
• 2008.06a
• /
• pp.175-181
• /
• 2008

The technologies of mold design, manufacturing, injection molding process and computer aided engineering(CAE) are developed rapidly with the growth of plastic product market. Injection molding process optimum design can not be easily determined. This study was determined factors and levels which carried out to analyze an influence of narrow pitch BGA socket warpage and performed investigating the main effect and interaction effect between factors using design of experiment. The result of this paper is injection time and packing pressure are affect on narrow pitch BGA socket warpage at injection molding.