• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.546-550
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• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method(LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.355-356
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• 2006

This paper proposes an Molding error compensation method that improves accuracy with geometry information of injected parts using three-dimensional measuring instrument. a traditional mold design has been conducted by an experience-based trial and error, whereby generally the mold designer would decide the gate location and processing conditions. as a natural consequence, almost all creats inferior goods. It's just a process of trial and error and caught in a vicious circle. Due to this reason, this paper uses a three-dimensional measuring instrument, a commercial analysis package of injection molding(Moldflow, MPI) to analysis a state of flux. In addition to that axiomatic approach.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.99-106
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• 2002

This paper proposes an error compensation method that improves accuracy with geometry information of injection molding parts. Geometric information can give an improved accuracy in reverse engineering. Measuring data can not lead to get accurate geometric model, including errors of physical parts and measuring machines. Measuring data include errors which can be classified into two types. One is molding error in product, the other is measuring error. Measuring error includes optical error of laser scanner, deformation by probe forces of CMM and machine error. It is important to compensate these in reverse engineering. Least square method (LSM) provides the cloud data with a geometry compensation, improving accuracy of geometry. Also, the functional shape of a part and design concept can be reconstructed by error compensation using geometry information.

• Design & Manufacturing
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• v.8 no.2
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• pp.1-6
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• 2014

The narrow-pitched connectors are of interest for small-scale devices such as smart phones because of theirs caling. We conducted an injection molding analysis and a warp analysis for 0.3mm and 0.5mm pitch FPC connectors. We obtained a volumetric shrinkage of 4.344%, a clamping force of 0.2529 tonne, a maximum injection pressure of 76.3 MPa as optimized molding conditions for the 0.3mm pitch FPC connector. We found that, compared with the traditional injection molding technique, the injection molding for narrow-pitched connectors comes with distinct features like low clamping force, high injection molding pressure, and narrow gate size. Adding to the optimization analysis, the deflection of 0.5mm pitch FPC connector was analyzed as well. A maximum deflection of 0.053mm was calculated, which the actual deflection of 0.062mm was compared to. The results deduced a relative error of 17%. We conclude that the deflection analysis along with the optimization analysis can be used as an effective tool to predict the behavior of narrow-pitch connectors although the relative error may need to improve.

• Design & Manufacturing
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• v.15 no.1
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• pp.21-25
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• 2021

When the product is removed from the mold after molding during the sheet metal molding process, elastic recovery causes a springback phenomenon. Much research has been done to minimize this phenomenon. In this study, V-bending experiments were conducted using galvanized steel sheets, stainless steel, and aluminum sheet materials, using a total of nine types of thin sheet materials of 1.0t, 1.5t, and 2.0t, respectively. Molding analysis and experimental data were compared and analyzed. In the case of galvanized steel sheets, it was considered that the springback phenomenon occurs more frequently in molding analysis than in experiments. It was considered that the springback phenomenon occurs greatly in the experiment, not the interpretation of the molding of the stainless steel plate and the aluminum plate. It was considered that the springback occurrence tendency of the molding analysis and the experiment was the same, and the springback occurrence error rate of the molding analysis and the experimental result was about 4.0%.

• Design & Manufacturing
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• v.2 no.3
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• pp.22-27
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• 2008

Forming of PET bottle was performed by injection-stretch blow molding. Blow molding is process of contacting the dies with air of materials by pressing. In this paper, the aim was to improve reliability of technical stabilization for the PET bottle that is last productive product and process technology which was able to do maximization by a preform performance enhancement of the uniform thickness that took temperature and a characteristic of materials. Preform design and dies manufacture were conducted using injection blow molding analysis results. Therefore thickness error of 5% for PET bottle can be obtained in this paper.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.773-787
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• 2020

In the injection molding process, the controlling stability of products quality is a very important factor in terms of productivity. Even when the optimum process conditions for the desired product quality are applied, uncontrollable external factors such as ambient temperature and humidity cause inevitable changes in the state of the melt resin, mold temperature. etc. Therefore, it is very difficult to maintain prodcut quality. In this study, a system that learns the correlation between process variables and product weight through artificial neural networks and predicts process conditions for the target weight was established. Then, when a disturbance occurs in the injection molding process and fluctuations in the weight of the product occur, the stability control of the product quality was performed by ANN predicting a new process condition for the change of weight. In order to artificially generate disturbance in the injection molding process, controllable factors were selected and changed among factors not learned in the ANN model. Initially, injection molding was performed with a polypropylene having a melt flow index of 10 g/10min, and then the resin was replaced with a polypropylene having a melt floiw index of 33 g/10min to apply disturbance. As a result, when the disturbance occurred, the deviation of the weight was -0.57 g, resulting in an error of -1.37%. Using the control method proposed in the study, through a total of 11 control processes, 41.57 g with an error of 0.00% in the range of 0.5% deviation of the target weight was measured, and the weight was stably maintained with 0.15±0.07% error afterwards.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2008.11a
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• pp.354-354
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• 2008

Recently, due to the tremendous growth of media technology, demands of the aspheric glass lens which is a high-performance and miniaturized is gradually increasing. Generally, the aspheric glass lens is manufactured by GMP(Grass Molding Press) method using WC(tungsten carbide) mold core. In this study, the thermal deformation which occurs in the cooling step of GMP was considered, and it was compensated the form of mold core. The lens which was molded by compensated mold core was satisfied that can be applied to the actual specifications.

• Transactions of Materials Processing
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• v.24 no.2
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• pp.121-129
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• 2015

A net-shape forming of small and complex-shaped metal parts by metal injection molding (MIM) has economic advantages in mass production, especially for STS 316L valve fitting. STS 316L offers excellent corrosion resistance, but it has poor machinability, which is a limitation in using it for a cost-effective production where both forging and machining are employed. Simulation and experimental analysis were performed to develop a MIM STS 316L 90° elbow fitting minimizing trial and error. A Taguchi method was used to determine which input parameter was the most sensitive to possible defects (e.g. sink mark depth) during the injection molding. The final prototype was successfully built. The results indicate that the simulation tool can be used during the design process to minimize trial and error, but the final adjustment of parameters based on field experience is essential.

• Design & Manufacturing
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• v.13 no.3
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• pp.53-58
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• 2019

This paper presents Artificial Neural Network(ANN) method to predict maximum injection pressure of injection molding machine and weights of injection molding products. 5 hidden layers with 10 neurons is used in the ANN. The ANN was conducted with 5 Input parameters and 2 response data. The input parameters, i.e., melt temperature, mold temperature, fill time, packing pressure, and packing time were selected. The combination of the orthogonal array L27 data set and 23 randomly generated data set were applied in order to train and test for ANN. According to the experimental result, error of the ANN for weights was $0.49{\pm}0.23%$. In case of maximum injection pressure, error of the ANN was $1.40{\pm}1.19%$. This value showed that ANN can be successfully predict the injection pressure and the weights of injection molding products.