• Journal of Power System Engineering
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• v.20 no.2
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• pp.43-50
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• 2016

This research proposal is based on a novel non-contact technique of micro-sized bead injection process for fabrication of electronic paper display. This non-contact injection process is based on the principle of electrostatic force and uses micro-sized metal-coated beads dispersed in a solution. The dispersion retention times of three different solutions with viscosities of 10 cps, 100 cps, and 1000 cps were measured by optical equipment showing the retention times of 5 mins, 10 mins, and 30 mins respectively. The dispersion retention rate dropped as the time passed. The dispersion retention characteristic of 1000 cps solution was more stable as compared to those of 10 cps and 100 cps meaning that higher viscosity has better retention properties. The experimental results of bead injection at different viscosity levels of the solution were also measured and a stable injection result was achieved by using 1000 cps solution. This results show that stable injection is dependent on solution viscosity and dispersion.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.109-116
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• 2017

In this study, the optimization of the overall dimensions of an automobile bumper was investigated through CAE and experiment using the Six Sigma method and design of experiment (DOE) method, respectively. Injection pressure, injection speed, injection time, cooling time, holding time, injection temperature, and holding pressure were selected as the vital parameters affecting the overall width of product through analysis of trivial many using CAE. The optimal values were determined using the DOE method, and we analyzed the improvement by applying the optimal conditions to the production process. As a result, the mean value of the overall width was close to the target value, with a deviation of 0.05mm, and the processability and I-MR control were remarkably improved. Finally, the dimension pass rate of the product improved by 20%.

• Design & Manufacturing
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• v.8 no.2
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• pp.12-17
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• 2014

Currently, injection molding process is a very useful technique that be applied to many field. And injection molding technology has been commercial based on many studies. However, there is no standard of surface roughness because there are few studies about surface technology of injection product. In addition, when designing the mold, changes of the core surface and the injection conditions are not considered. In this paper, change of surface according to the core and the injection conditions was compared with the surface of the injection product. Accumulation of these technologies will propose direction in mold design, manufacturing and injection molding technology.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.10a
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• pp.946-949
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• 2005

It is very a time-consuming and error-prone process to obtain the optimal injection condition, which can produce good injection molding products in some operational variation of facilities, from a seed injection condition. This study proposes a new approach to search the optimal injection molding condition using a neural network and a genetic algorithm. To estimate the defect type of unknown injection conditions, this study forces the neural network into learning iteratively from the injection molding conditions collected. Major two parameters of the injection molding condition - injection pressure and velocity are encoded in a binary value to apply to the genetic algorithm. The optimal injection condition is obtained through the selection, cross-over, and mutation process of the genetic algorithm. Finally, this study compares the optimal injection condition searched using the proposed approach. with the other ones obtained by heuristic algorithms and design of experiment technique. The comparison result shows the usability of the approach proposed.

• Design & Manufacturing
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• v.17 no.3
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• pp.34-40
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• 2023

In this study, real-time collection of mold vibration signals during injection molding processes was achieved through IoT devices installed on the mold surface. To analyze changes in the collected vibration signals, injection molding was performed under six different process conditions. Analysis of the mold vibration signals according to process conditions revealed distinct trends and patterns. Based on this result, cosine similarity was applied to compare pattern changes in the mold vibration signals. The similarity in time and acceleration vector space between the collected data was analyzed. The results showed that under identical conditions for all six process settings, the cosine similarity remained around 0.92±0.07. However, when different process conditions were applied, the cosine similarity decreased to the range of 0.47±0.07. Based on these results, a cosine similarity threshold of 0.60~0.70 was established. When applied to the analysis of mold vibration signals, it was possible to determine whether the molding process was stable or whether variations had occurred due to changes in process conditions. This establishes the potential use of cosine similarity based on mold vibration signals in future applications for real-time monitoring of molding process changes and anomaly detection.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.3
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• pp.338-344
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• 2016

The study is actively being performed to increase fuel injection pressure of common rail system among countermeasures to meet the emission regulation strengthen of the Diesel engine. The common rail fuel injection tube in such ultra high pressure common rail system has the weakest structural characteristics against vibration that is generated by fuel injection pressure and pulsation during engine operation and driving. Thus the extreme durability is required for common rail fuel injection tube, and the drawing process is being magnified as the most important technical fact for strength of seamless pipe that is the raw material of common rail tube. In this respect, we analyzed the characteristic of dimension and stress variation of the ultra high pressure common rail fuel injection tube by variation of Die and Plug angle in drawing process. Based on the analysis, we tried to obtain the raw material strength of common rail fuel injection tube for applying to the ultra high pressure common rail system. As a result, Plug angle is more important than entry angle of Die and we could obtain the target dimension and strength of the ultra high pressure common rail fuel injection tube through optimization of Plug angle.

• Design & Manufacturing
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• v.15 no.1
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• pp.48-56
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• 2021

The quality of products produced by injection molding process is greatly influenced by the process variables set on the injection molding machine during manufacturing. It is very difficult to predict the quality of injection molded product considering the stochastic nature of manufacturing process, because the process variables complexly affect the quality of the injection molded product. In the present study we predicted the quality of injection molded product using Artificial Neural Network (ANN) method specifically from Multiple Input Single Output (MISO) and Multiple Input Multiple Output (MIMO) perspectives. In order to train the ANN model a systematic plan was prepared based on a combination of orthogonal sampling and random sampling methods to represent various and robust patterns with small number of experiments. According to the plan the injection molding experiments were conducted to generate data that was separated into training, validation and test data groups to optimize the parameters of the ANN model and evaluate predicting performance of 4 structures (MISO1-2, MIMO1-2). Based on the predicting performance test, it was confirmed that as the number of output variables were decreased, the predicting performance was improved. The results indicated that it is effective to use single output model when we need to predict the quality of injection molded product with high accuracy.

• Transactions of Materials Processing
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• v.6 no.6
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• pp.489-499
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• 1997

The injection molding process is analyzed to get the information on the mold design parameters and the optimum process conditions for automotive plastic front fender. The gate position, runner size and cooling channel are determined by the estimation of the flow balance, packing time, uniform cooling and shrinkage and warpage in the injection molding analyses. The procedure can be used in the mold design in the early stage when developing plastic parts.

• Journal of the Korean Institute of Plant Engineering
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• v.23 no.4
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• pp.29-38
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• 2018

Through process quality information, the time required for process quality analysis has been drastically shortened, the process defect rate has been reduced, and the manufacturing lead time has been shortened and the on-time delivery rate has been improved. Therefore, The purpose of this study is to develop a quality information analysis system model that effectively shortens the time required for process quality analysis in automobile safety belt parts manufacturing process. As a result of experiments on communication operation between manufacturing execution system (MES) quality server, injection machine control computer, injection machine programmable logic controller (PLC) and terminal, in analyzing quality information, the conventional handwriting input method took an average of 20 minutes, but the new multi-network method took about 2 minutes on average. In addition, the process defect rate was reduced by 13% and the manufacturing lead time was shortened from 28 hours to 20 hours. The delivery compliance rate improved from 96 to 99%.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.6
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• pp.55-60
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• 2020

The injection molding technique is a processing method widely used for the production of plastic parts. In this study, the gate position, gate size, packing time, and melt temperature were optimized to minimize both the stress and deformation that occur during the injection molding process of medical suction device components. We used a central composite design and Latin hypercube sampling to acquire the data and adopted the response surface method as an approximation method. The efficiency of the optimization of the injection molding problem was determined by comparing the results of a genetic algorithm, sequential quadratic programming, and a non-dominant classification genetic algorithm.