• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.958-962
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• 2003

We acquired data of injection molding machine in operation and stored the data in database. We acquired the data of injection molding machine for fault detection and diagnosis (FDD) continuously and estimated the fault results with a fuzzy algorithm. Many of FDD are applied to a huge system, nuclear power plant and a computer numerical control(CNC) machine for processing machinery. But, the research of FDD is rare in injection molding machine compare with computer numerical control machine. We appraise the accuracy of the FDD and the limit of the application to the injection molding machine. We construct the fault detection and diagnosis system based on fuzzy algorithm in the injection molding machine. Data of operating injection molding machine are acquired in order to improve the reliability of detection and diagnosis.

• Journal of Drive and Control
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• v.17 no.3
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• pp.15-25
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• 2020

The cycle time and power saving effect of a hydraulic hybrid injection molding machine using a servo motor are considered in this paper. In order to verify control characteristics, such as pressure and speed, experiments were performed with the hydraulic hybrid injection molding machine, clamping force of 110 ton. The power consumption and production cycle time of a conventional hydraulic injection molding machine were measured to compare its performances with the hydraulic hybrid injection molding machine. An injection molding machine with a clamping force of 1300 ton was used as the conventional machine, the hybrid machine was implemented by replacing its induction motors with servo motors. In the remodeled hybrid machine, experiments were performed to investigate how the displacement of the mold clamping pump affects the power consumption and production cycle time. The results showed that the production cycle time of the hybrid injection molding is similar to a conventional hydraulic injection molding machine but with a significant energy saving of about 40%.

• Transactions of Materials Processing
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• v.17 no.8
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• pp.662-667
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• 2008

Because of steep rise of international energy cost in recent years, high efficiency has been emphasized in energy policy. As far as injection molding machine is concerned, hybrid method using hydraulic and electric systems became the key to this energy saving. The energy saving and molding reproducibility of hybrid injection molding process were shown experimentally. The power consumption of hybrid injection molding machine is reduced to 38% as compared with that of hydraulic machine. Furthermore, the molding reproducibility was shown for both methods.

• Design & Manufacturing
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• v.17 no.1
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• pp.27-32
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• 2023

In this paper, we intend to predict the mass of the spiral using CAE and machine learning. First, We generated 125 data for the experiment through a complete factor design of 3 factors and 5 levels. Next, the data were derived by performing a molding analysis through CAE, and the machine learning process was performed using a machine learning tool. To select the optimal model among the models learned using the learning data, accuracy was evaluated using RMSE. The evaluation results confirmed that the Support Vector Machine had a good predictive performance. To evaluate the predictive performance of the predictive model, We randomly generated 10 non-overlapping data within the existing injection molding condition level. We compared the CAE and support vector machine results by applying random data. As a result, good performance was confirmed with a MAPE value of 0.48%.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2008.10a
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• pp.418-421
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• 2008

Because of steep rise of international energy cost in recent years high efficiency has been emphasized in energy policy. As far as injection molding machine is concerned, hybrid method using hydraulic and electric systems became the key to this energy saving. the energy saving and molding reproducibility of hybrid injection molding process were shown experimentally, power consumption of hybrid machine is 38% as compared with that of hydraulic machine. Furthermore, the molding reproducibility showed between two methods.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.10
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• pp.768-773
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• 2003

Injection molding has been widely used for the mass production of a plastic product. With the development of the relative technique, various injection molding techniques have been developed and we could get more precise plastic product. The temperature of a melting resin is an important factor in the injection molding and this temperature has direct influence on the quality of a plastic product. In the present injection molding machine, the deriation of a temperature controlled by PID control method is within 2$^{\circ}C$ in the injection molding machine but PID control method takes too much time to stabilize after preheating and its overshoot is so big. We applied fuzzy control to alleriate the problem. In this research, we experimented the fuzzy temperature control with the usage of PC based PLC.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.4
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• pp.1-8
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• 2011

Injection molding machine is the assembly of many kinds of mechanical and fluid power part and electro-electronic control system. From in these, fluid power is a part where becomes the first core of this machine. Fluid power systems of injection molding machine are modelled and analyzed using a commercial program AMESim. The analysis model which is detailed about the parts applied a publishing catalog data. Sub system models which is divided according to functional operation are made and its analysis results shows how design parameters work on operational characteristics like displacement, pressure, flow rates at each node and so on. Total fluid power circuit model is also made and analyzed. The results made by analysis will be used design of fluid power circuit of injection molding machine.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.4
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• pp.24-31
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• 2011

Double-color Injection molding machine is the assembly of many kinds of mechanical, fluid power part and electric electronic control system. From in these, fluid power is a part where becomes the first core of this machine. Fluid power systems of double-color injection molding machine are modelled and analyzed using a commercial program AMESim. Partial system models which is divided according to functional operation are made and its analysis results shows how design parameters work on operational characteristics like pressure, flow rates, displacement at each node and so on. Analysis modeling and compared the data which gets from experiment and the analysis result which has a reliability got data. The results made by analysis will be used design of fluid power circuit for developing a double-color injection molding machine.

• Design & Manufacturing
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• v.18 no.2
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• pp.64-73
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• 2024

The purpose of this paper is to introduce a fusion method that combines the design of experiments (DOE) and machine learning to optimize the bias of plastic products. The study focuses on the plastic motor housing used in automobiles, which is manufactured through plastic injection molding. Achieving optimal molding for the motor housing involves the optimization of various molding conditions, including injection pressure, injection time, holding pressure, mold temperature, and cooling time. Failure to optimize these conditions can lead to increased product deformation. To minimize the deformation of the motor housing, the widely used Taguchi method, which is one of the design of experiment techniques, was employed to identify the injection molding conditions that affect deformation. Machine learning was then applied to various models based on the identified molding conditions. Among the models, the Random Forest model emerged as the most effective in predicting deformation amounts. The validity of the Random Forest model was also confirmed through verification. The verification results demonstrated the excellent prediction accuracy of the trained Random Forest model. By utilizing the validated model, molding conditions that minimize deformation were determined. Implementation of these optimal molding conditions led to a reduction of approximately 5.3% in deformation compared to the conditions before optimization. It is noteworthy that all injection molding outcomes presented in this paper were obtained through robust injection molding simulations, ensuring both research objectivity and speed.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.452-457
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• 2003

As a fundamental study on developing elements with micro shape, micro mold parts machining and experiment of injection molding using it were performed. The ultra precision micro machining system with high functionality was fabricated, and utilized in the machining of micro parts. By using this machining system and micro end-mill tool, a micro circle column structure of high aspect ratio, diameter 60 $\mu\textrm{m}$, height 500 $\mu\textrm{m}$, was fabricated. And a micro lens molds were fabricated by using ball end-mill tool of 300 $\mu\textrm{m}$ diameter and diamond fly-cut tool of 150 $\mu\textrm{m}$ radius. A micro injection molding machine, which is clamping force 1.75 ton, injection capacity 2.8cc, was fabricated for injection molding experiment using micro molds. The injection molding experiment was performed by using the injection molding machine, micro cylinder structures and lens molds. This paper introduces these micro machining system and injection molding machine and demonstrates examples of injection molding using fabricated molds.