• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.575-579
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• 2005

In this paper, the development of an IMM(Injection Molding Machine) controller is discussed. Presently, the Mold Close and Open Control Method of a toggle-type IMM is open-loop control. Through the development, a PC based control system was built instead of an existing controller and a closed-loop control replaced the previous control method by using PC based PLC. To control the nonlinear system of toggle type clamping unit, a fuzzy PI control algorithm was selected and it was programmed by an IL(Instruction List) and a LD(Ladder Diagram) on a PC based PLC. The application of fuzzy algorithm as the control method was also considered to change a control object like a mold replacement or an additional apparatus. For the development of an IMM controller, PC based PLC of PCI card type, distributed I/O modules with CANopen and Industrial PC and HMI (Human Machine Interface) software were used.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2004년도 춘계학술대회 논문집
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• pp.70-75
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• 2004

In this study, the pattern of lens aberration was studied at different injection molding conditions such as injection speed, holding pressure, holding pressure time, mold temperature and cylinder temperature and, then, the results were analyzed with a laser interferometer. It was demonstrated that optimal condition of lens aberration could be achieved by adjustment of injection molding conditions.

• 한국기계가공학회지
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• 제17권4호
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• pp.160-168
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• 2018

Among the components of rotary hooks, a core component of an embroidery sewing system, a study was conducted to apply metal injection molding to the manufacture of a hook body and a housing that was very difficult to mechanical working. The correlation of feedstock, a mixture of binder and SCM 415 metal powder, and properties of the pressure-volume-temperature interrelationship, viscosity, specific heat, and thermal conductivity were measured. Injection molds for the hook body and the housing were developed through injection molding analysis using these properties and conducted injection tests. Optimal injection gate position and number, injection pressure, and injection time were obtained through a comparison of analysis results with the experiment results.

• Design & Manufacturing
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• 제13권3호
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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.

• 품질경영학회지
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• 제52권1호
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• pp.43-56
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• 2024

Purpose: The injection molding process, crucial for plastic shaping, encounters difficulties in sustaining product quality when replacing injection machines. Variations in machine types and outputs between different production lines or factories increase the risk of quality deterioration. In response, the study aims to develop a system that optimally adjusts conditions during the replacement of injection machines linked to molds. Methods: Utilizing a dataset of 12 injection process variables and 52 corresponding sensor variables, a predictive model is crafted using Decision Tree, Random Forest, and XGBoost. Model evaluation is conducted using an 80% training data and a 20% test data split. The dependent variable, classified into five characteristics based on temperature and pressure, guides the prediction model. Bayesian optimization, integrated into the selected model, determines optimal values for process variables during the replacement of injection machines. The iterative convergence of sensor prediction values to the optimum range is visually confirmed, aligning them with the target range. Experimental results validate the proposed approach. Results: Post-experiment analysis indicates the superiority of the XGBoost model across all five characteristics, achieving a combined high performance of 0.81 and a Mean Absolute Error (MAE) of 0.77. The study introduces a method for optimizing initial conditions in the injection process during machine replacement, utilizing Bayesian optimization. This streamlined approach reduces both time and costs, thereby enhancing process efficiency. Conclusion: This research contributes practical insights to the optimization literature, offering valuable guidance for industries seeking streamlined and cost-effective methods for machine replacement in injection molding.

• 한국소성가공학회:학술대회논문집
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• 한국소성가공학회 2002년도 금형가공 심포지엄
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• pp.76-83
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• 2002

The injection molding of the plastic optics is basically same as the conventional molding except it requires very intricate control of all the molding processing parameters. In the plastic optics, the problem of injection molding is the shrinkage. The shrinkage must be removed and predicted. This shrinkage is becoming more important than any other problems in precision molding because it can affect the focal length of a lens or the total performance of the optical system. This study focused on avoiding the shrinkage that the mold design allows for the optics. In making mold, the surface accuracy(P-V) of the lower and lower mold are $0.201{\mu}m\;and\;0.434{\mu}m$ respectively. A surface roughness(Ra) is below $0.02{\mu}m$ due to selecting the appropriate tools and using the injection molding machine in high degree. In injection molding of the plastic lens, mold temperature, resine temperature and injecting pressure are important process parameters. Injection molding process is carried out according to varying mold temperature and injecting pressure. As a result P-V(peak to valley) of spheric lens is $3.478{\mu}m$ and that of aspheric lens is $1.786{\mu}m$.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2012년도 추계학술대회 논문집
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• pp.431-432
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• 2012

• 소성∙가공
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• 제15권1호
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• pp.76-81
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• 2006

In this study, warpage characteristics of mobile phone cover through injection molding process were investigated using design of experiments in injection molding process. Warpage in plastic injection molding has a significant effect on quality of product. Effects of injection time, packing pressure, packing time, mold temperature and melt temperature on the warpage of mobile phone cover were considered by numerical analysis and experiment with Taguchi method. The degree of warpage for the injection molded part was measured by using three dimensional coordinate measurement machine. It was shown that temperature control factor has more significant effect on the warpage of mobile phone cover than pressure control factor.

• 한국정밀공학회지
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• 제12권3호
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• pp.110-118
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• 1995

The filling phase analysis of the injection molding process for thermoplastics was applied to predict pressure, themperature and shear stress in the test mold, and the results were compared with the experiment using 30% glass fiber added ABS resin. The finite difference method was used in the analysis considering the effects of heat transfer between molten polymer and mold wall, and also frictional heating by shear flow. The analysis results were considered as a method to improve the quality and the productivity of injection molding process. Using the analysis results, the molding factors such as mold-ability of polymers, performance of injection molding machine, positioning of gate and dimendsioning of runner in the injection molding process can be estimated at the design stage of mold for good quality and productivity.

• 한국공작기계학회논문집
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• 제15권1호
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• pp.108-112
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• 2006

A Study of birefringence in an aspheric lens of injection molding became more important to improve its optical properties. In the present study, the experimental study was carried out to investigate the relationship between birefringence and injection molded conditions. The processing factors are conditions include packing pressure, packing time, injection speed, melt temperature of optical resin and wall temperature. Birefringence was observed figures by using a polarizer in light. This experiments were carried out using the simulation software and injection molding machine.