• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.10a
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• pp.46-50
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• 2001

Due to its ability in producing a net-shape product to high precision in a very shot cycle time, injection molding has become one of the most important polymer-processings in the industry today. Recently the CAE applications in the field of injection molding have provided significant contributions to the mold design and process optimization. As a part of such an application the packing process has been studied using C-PARK. The prediction of pressure variations during post-filling stage for amorphous material has been compared with an experimental observation for a simple rectangular geometry of uniform thickness. And the optimal packing processes were calculated using the cavity pressure curve near the gate. As a case study, a warpage simulation was carried out for a DY-HOLDER with the variable number of gates.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.425-428
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• 2009

Silicone is recently used for LED chip encapsulment due to its good thermal stability and optical transmittance. To mold a solid-state silicone encapsulment, curing by mixing at elevated temperatures followed by cooling is necessary. As the silicone molding process is involved in healing and subsequent cooling, the thermal residual stress, which causes mechanical warpage or optical birefringence in the final silicone encapsulment, may be induced if there are non-uniformities in cured silicone material properties or encapsulment shape design. The prediction of residual stress is necessary to design a high-quality silicone molding process. Therefore, in the present paper, a numerical parametric study was attempted to evaluate the heating and cooling effects on the thermal residual stress induced in the cured silicone.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.1
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• pp.1-7
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• 2015

This paper predicts the spring-in effect of curved laminated composite structure for various stacking sequence using finite element analysis(ABAQUS). In composite manufacturing process, large temperature difference, different coefficient of thermal expansion and chemical shrinkage effect cause distortion of composite parts such as spring-in and warpage. Distortion of composite structure is important issue on quality of product, and it should be considered in manufacturing process. In finite element analysis, a CHILE(Cure Hardening Instantaneously Linear Elastic) model and chemical shrinkage effects are considered developing user subroutine in ABAQUS and some cases are simulated.

• Journal of the Microelectronics and Packaging Society
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• v.20 no.1
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• pp.21-26
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• 2013

This study presents new analysis method to predict bending behavior of packaging substrate structure by comparing finite element method simulation and measured curvature using 3D scanner. Packaging substrate is easily bent and deflected while undergoing various processes such as curing of prepreg and copper pattern plating. We prepare specimens with various conditions and measure contours of each specimen and compute the residual stresses on deposited films using analytical solution to find the principle of bending. Core and prepreg in packaging substrate are made up of resin and bundles of fiber which exist orthogonally each other. Anisotropic material properties cause peculiar bending behavior of packaging substrate. We simulate the bending deflection with finite element method and verify the simulated deflection with measured data. The plating stress of electrodeposited copper is about 58 MPa. The curing stresses of solder resist and prepreg are about 13 MPa and 6.4 MPa respectively in room temperature.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.580-586
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• 2021

The CAT methodology is a numerical analysis technique using CAE. Recently, a methodology of applying artificial intelligence techniques to a simulation has been studied. A previous study compared the deformation results according to the injection molding process using a machine learning technique. Although MLP has excellent prediction performance, it lacks an explanation of the decision process and is like a black box. In this study, data was generated using Autodesk Moldflow 2018, an injection molding analysis software. Several Machine Learning Algorithms models were developed using RapidMiner version 9.5, a machine learning platform software, and the root mean square error was compared. The decision-tree showed better prediction performance than other machine learning techniques with the RMSE values. The classification criterion can be increased according to the Maximal Depth that determines the size of the Decision-tree, but the complexity also increases. The simulation showed that by selecting an intermediate value that satisfies the constraint based on the changed position, there was 7.7% improvement compared to the previous simulation.

• Korean Chemical Engineering Research
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• v.46 no.3
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• pp.486-491
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• 2008

This paper describes a model identification method that has been applied to a commercial 12-inch RTP (rapid thermal processing) equipment with an ultimate aim to develop a high-performance advanced controller. Seven thermocouples are attached on the wafer surface and twelve tungsten-halogen lamp groups are used to heat up the wafer. To obtain a MIMO balanced state space model, multiple SIMO (single-input multiple-output) identification with highorder ARX models have been conducted and the resulting models have been combined, transformed and reduced to a MIMO balanced state space model through a balanced truncation technique. The identification experiments were designed to minimize the wafer warpage and an output linearization block has been proposed for compensation of the nonlinearity from the radiation-dominant heat transfer. As a result from the identification at around 600, 700, and $800^{\circ}C$, respectively, it was found that $y=T(K)^2$ and the state dimension of 80-100 are most desirable. With this choice the root-mean-square value of the one-step-ahead temperature prediction error was found to be in the range of 0.125-0.135 K.