• Journal of Powder Materials
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• v.9 no.4
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• pp.245-250
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• 2002

In this paper we presented numerical method for the simulation of powder injection molding filling process, which is one of the key processes in powder injection molding. Rheological properties of powder binder mixture such as slip phenomena and yield stress were introduced into the numerical analysis model of powder injection molding filling simulation. Numerical model can be classified into two types. One is 2.5D model which can be introduced to a arbitrary thin geometry and the other is full 3D model which can be applied to a general 3D shape. For 2.5D model we showed the validity of our CAE system with several verification examples. Finally we suggested flow analysis model for 3D powder injection molding filling simulation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.543-547
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• 2004

The molding characteristics of large-sized orthogonal stiffened plastic plates were investigated in the present study. Models with the geometry of 1800$\times$600$\times$12mm and 1200$\times$600$\times$12mm were designed for injection molding(IM) and injection-compression molding(ICM), respectively. To determine a mold system and reduce the warpage of the presented model after molding process, IM and ICM analyses using MOLDFLOW$^{TM}$ were performed. Also, the experiments were performed to verify the suggested mold system. It was shown that the change of molding method could significant effect on the warpage of designed model.l.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.765-774
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• 1996

Computer Integrated Manufacturing(CIM) requires models of manufacturing processes to be implemented on the computer. These models are typically used for determining optimal process control parameters or designing adaptive control systems. In spite of the progress made in the mechanistic modeling, however, empirical models derived from experimental data play a maior role in manufacturing process modeling. This paper describes the development of a meural metwork medel for injection molding. This paper describes the development of a nueral network model for injection molding process. The model uses the CAE analysis data based on Taguchi method. The developed model is, then, compared with the traditional polynomial regression model to assess the applicabilit in practice.

• Transactions of Materials Processing
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• v.15 no.6 s.87
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• pp.436-440
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• 2006

A rapid tooling (RT) method fur the resin transfer molding (RTM) have been investigated. We fabricated a curved I-beam to verify the method. After creating a three-dimensional CAD model of the beam we fabricated a prototype of the model using a rapid prototyping (RP) machine. A soft mold was made using the prototype by the conventional silicone mold technique. The procedure and method of mold fabrication is described. The mold was cut into several parts to allow easier placement of the fiber preform. We conducted the resin transfer molding process and manufactured a composite beam with the mold. The preform was built by stacking up eight layers of delicately cut carbon fabrics. The fabrics were properly stitched to maintain the shape while placement. The manufactured composites beam was inspected and found well-impregnated. The fiber volume ratio of the fabricated beam was 16.85%.

• Design & Manufacturing
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• v.15 no.4
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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.

• Transactions of Materials Processing
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• v.13 no.2
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• pp.180-187
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• 2004

A computer code was developed to simulate the filling stage of the injection/compression molding process by a finite element method. The constitutive equation used here was the compressible Leonov model. The PVT relationship was assumed to follow the Tait equation. The flow-induced birefringence was related to the calculated flow stresses through the linear stress-optical law. Simulations of a disk part under different process conditions including the variation of compression stroke and compression speed were carried out to understand their effects on birefringence variation. The simulated results were also compared with those by conventional injection molding.

• Design & Manufacturing
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• v.17 no.3
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• pp.1-7
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• 2023

Blow molding is a manufacturing process in which thermoplastic preforms are preheated and then pneumatically expanded within a mold to produce hollow products of various shapes. The two-step process, a type of blow molding method, requires the output of multiple infrared lamps to be adjusted individually, so the process of finding initial conditions hinders productivity. In this study, digital twin technology was applied to solve this problem. A blow molding simulation technique was established and simulation-based metadata was generated. A response surface ROM (Reduced Order Model) was built using the generated metadata. Then, a dynamic ROM was constructed using the results of 3D heat transfer analysis. Through this, users can quickly check the product wall thickness uniformity according to changes in the control value of the heating lamp for products of various shapes, and at the same time, check the temperature distribution of the preform in real time.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.362-365
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• 2000

Rapid Prototyping(RP) techniques have revolutionized traditional manufacturing methods. These techniques allow the user to fabricate a part directly from a conceptual model before investing in production tooling and help develop new models with significant short time. This paper suggests to new process to manufacture large size hollow shape parts for prototype-car using Rapid Prototyping technology and Vacuum Molding with the reduction of delivery time. In addition, This paper introduces the dividing and combining method to make large size RP master model in spite of the limit of the build chamber dimensions of commercialized RP system and post-processing method to achieve sufficient surface quality.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.11
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• pp.1031-1040
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• 2014

In this paper, a new condition diagnosis algorithm for the lens injection molding process using various features extracted from cavity pressure, nozzle pressure and screw position signals is developed with the aid of probability neural network (PNN) method. A new feature extraction method is developed for identifying five (5), seven (7) and two (2) critical features from cavity pressure, nozzle pressure and screw position signals, respectively. The node energies extracted from cavity and nozzle pressure signals are also considered based on wavelet packet decomposition (WPD). The PNN method is introduced to build the condition diagnosis model by considering the extracted features and node energies. A series of the lens injection molding experiments are conducted to validate the model, and it is demonstrated that the proposed condition diagnosis model is useful with high diagnosis accuracy.

• Journal of the Korean Society for Precision Engineering
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• v.21 no.2
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• pp.27-34
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• 2004

In the injection molding of plastic optical lenses, the molding conditions have critical effects on the quality of the molded lenses. Since there are many molding parameters involved in injection molding process, determination of the molding conditions for lens molding is very important in order to precisely control the surface contours of an optical lens. Therefore this paper presents the application of neural network in suggesting the optimized molding conditions for improving the quality of molded parts based on data of FE Analysis carried out through CAE software, Timon-3D. Suggested model in this paper, which serves to learn from the data of FE Analysis and induce the values for optimized molding conditions. has been implemented for searching the molding conditions without void and with minimized thickness shrinkage at lens center of injection molding optical lens. As the result of this study. we have confirmed that void creation at the inside of lens is primarily determined by mold temperature and thickness shrinkage at center of lens is primarily determined by the parameters such as holding pressure and mold temperature.