• Journal of the Korean Society of Visualization
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• v.16 no.1
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• pp.42-47
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• 2018

A needle-free injector is one of the new non-invasive players in impregnating the biological barriers. It is considered as the next phase in drug delivery and therapeutic applications. One of the major fields of application is in skin remodeling procedures. Although many studies were carried out in understanding the principle in the needle-free injection procedure, fewer studies were done with the aim of therapeutic applications. In the present study, we tried to identify key parameters that affect the jet divergence and peak stagnation pressure on the skin surface in a conventional needle-free injector for skin treatment. A summary of the working principle and effect of the key parameters are presented.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.6
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• pp.9-15
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• 2004

The characteristics of combustion and emissions were investigated in a single cylinder DI diesel engine equipped with a common rail injection system. This study presents an experimental study of the effect of engine speed, injection timing, injection pressure and pilot injection timing on the combustion and exhaust emissions. The engine speeds were 1000 and 2000rpm and the corresponding injection pressures were 50 and 100MPa. Experimental results show that NOx emissions decrease with retarded injection timing, while HC and CO emissions increases. Higher injection pressure increases NOx with lower soot emissions. For the case with the pilot injection prior to main injection, the ignition delay is shortened and the premixed combustion ratio decreases. Also NOx and soot emissions are decreased with increase of pilot injection advance.

• Journal of Power System Engineering
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• v.9 no.4
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• pp.83-89
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• 2005

Molding shrinkage is one of the problems to be solved in conventional injection molding. Despite many trying-out has been to solve it, intrinsic cause of shrinkage such as orientation and thermal exchange between melt and mold has not been solved yet. For reducing shrinkage and residual stress on molding, injection compression molding process was invented. In this study, experiments about effects of injection compression molding's parameters on shrinkage of PMMA molding were conducted and compared with conventional injection molding's shrinkage. Before the injection compression molding experiment, molding shrinkage rate was predicted by analyzing pvT diagram and was compared with the results of experiment. The shrinkage rate of injection compression molding was lower than convention injection molding's one which was different from the predicted shrinkage. The reason was observed that the experimental mold was not a proper type for injection compression, flowing backward of melt into nozzle and unreasonable mechanism of injection molding machine.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.7
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• pp.1348-1356
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• 2002

The present work concerns the optimal design for injection molding processes by using the design of experiments (DOE) and numerical analysis. The DOE approaches is planned to be able to consider two-way interaction, and have been applied progressively for both mold design and process design. Numerical analyses have been carried out as a design of experiments for mold parameters such as runner specifications and cooling channel configurations. In order to determine optimal process parameters, experiments have been performed for various process conditions with the DOE scheduling. As a result, the quality and productivity of the product have been improved, and the proposed approach can be successfully reflected on the industrial injection molding process of precision electronics parts.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.3
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• pp.224-229
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• 2016

Recently, polymeric micro-fluidic biochips with numerous micro patterns on the surface were fabricated by injection molding for realizing low-cost mass production of devices. To evaluate the effects of process parameters on large-scale micro-structure replication, a $50{\times}50mm^2$ tool insert with surface structures having a patterns of trapezoidal shapes (height: $30{\mu}m$) was employed. During injection molding, PMMA was used; packing phase parameters and mold temperature were investigated. The replicated surface textures were quantitatively characterized by confocal laser microscopy with 10-nm resolution. The degree of replication at low mold temperatures was found to be higher than that at high mold temperature at the beginning of the packing stage. Thereafter, the degree of replication increased to a greater extent at higher mold temperatures; application of higher mold temperatures improved the degree of replication.

• Journal of ILASS-Korea
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• v.28 no.1
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• pp.1-9
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• 2023

In the cylinder of gasoline direct injection engines, the spray targeting from injectors is of great significance for fuel consumption and pollutant emissions. The automotive industry is putting a lot of effort into improving injector targeting accuracy. To improve the targeting accuracy of injectors, it is necessary to develop models that can predict the spray targeting positions. When developing spray targeting models, the most used technique is computational fluid dynamics (CFD). Recently, due to the superiority of machine learning in prediction accuracy, the application of machine learning in this field is also receiving constant attention. The purpose of this study is to build a machine learning model that can accurately predict spray targeting based on the design parameters of injectors. To achieve this goal, this study firstly used laser sheet beam visualization equipment to obtain many spray cross-sectional images of injectors with different parameters at different injection pressures and measurement planes. The spray images were processed by MATLAB code to get the targeting coordinates of sprays. A total of four models were used for the prediction of spray targeting coordinates, namely ANN, LSTM, Conv1D and Conv1D & LSTM. Features fed into the machine learning model include injector design parameters, injection conditions, and measurement planes. Labels to be output from the model are spray targeting coordinates. In addition, the spray data of 7 injectors were used for model training, and the spray data of the remaining one injector were used for model performance verification. Finally, the prediction performance of the model was evaluated by R² and RMSE. It is found that the Conv1D&LSTM model has the highest accuracy in predicting the spray targeting coordinates, which can reach 98%. In addition, the prediction bias of the model becomes larger as the distance from the injector tip increases.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.49 no.8
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• pp.463-467
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• 2000

In this paper, the new LIGBT structures with trap injection are proposed to improve switching characteristics of the conventional SOI LIGBT. The Simulations are performed in order to investigate the effects of the positiion, whidth and concentration of trap injection region with a reduced minority carrier lifetime using 2D device simulator MEDICI. Their electrical characteristics are analyzed and the optimum design parameters are extracted. As a result of simulation, the turn off time for the model A with the trap injection is $0.78\mus$. These results indicate the improvement of about 2 times compared with the conventional SOI LIGBT because trap injection prevents minority carriers which is stored in the n-drift region during turn off switching. The latching current is $1.5\times10^{-4}A/\mum$ and forward blocking voltage is 168V which are superior to those of conventional structure. It is shown that the trap injection is very effective to reduce the turn off time with a little increasing of on-state voltage drop if its design and process parameters are optimized.

• Journal of the Korean Society for Precision Engineering
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• v.31 no.7
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• pp.585-590
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• 2014

Recently, monitoring systems, such as POP, take a core role in scheduling or planning of manufacturing facilities for production, maintenance, and so on. Such monitoring systems require functionalities for real-time parameter monitoring and controlling to maximize efficiency of facilities. However, vendors usually do not provide internal communication protocols or interface to access the machine controller. Therefore, the values of parameters related to machine operations and controls cannot be easily accessed from external devices. In this paper, we propose an interface methodology for a real-time monitoring and controlling of injection molding machine parameters such as user input parameters, embedded sensor data and injection molding status information.

• Journal of Computational Design and Engineering
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• v.1 no.4
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.