• Proceedings of the Korean Society of Precision Engineering Conference
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• 2008.11a
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• pp.475-476
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• 2008

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.569-570
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• 2009

• International Journal of Precision Engineering and Manufacturing
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• v.4 no.5
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• pp.34-40
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• 2003

This paper presents the inertial force problem of ultrahigh-speed injection molding machine using linear motors, and presents its solutions. To make very thin products by injection molding, very high injection speed is required, and linear motors are used for this purpose. However, direct drive by linear motors may cause brief nozzle separation from the sprue bushing because of the inertia force which is as large as the total output thrust of the linear motors, and this momentary separation can cause molten plastic to leak. In this paper, two solutions are proposed for this inertia force problem. One is the mechanical cancellation of the inertia force, and the other is to increase the nozzle contact force. With the latter solution, the stationary platen bending worsens, so a new nozzle contact mechanism is also proposed, which can prevent the stationary platen bending.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.171-177
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• 2002

This paper presents the inertial force problem of ultrahigh-speed injection molding machine using linear motors, and presents its solutions. To make very thin products by injection molding, very high injection speed is required, and linear motors are used for this purpose. But direct drive by linear motors may cause brief nozzle separation from the sprue bushing because of the inertia force as large as the total output thrust of the linear motors, and this momentary separation can cause molten plastic leakage. In this paper, two solutions are proposed for this inertia force problem. One is the mechanical cancellation of the inertia force, and the other to increase the nozzle contact force. With the latter solution, the stationary platen bending worsens, so a new nozzle contact mechanism is also proposed, which can prevent the stationary platen bending.

• 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 the Korean Society of Industry Convergence
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• v.23 no.3
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• pp.507-513
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• 2020

Liquid filling machines are frequently used in packaging fields; however, there exists a problem in precisely measuring the quantity of the liquid. In the case where the liquid filling machine is not properly metered, there may be issues, such as the fluid exceeding the capacity or chemicals being exposed outside. In this paper, we propose a smart injection nozzle device that can solve the issues stated above. The proposed smart injection nozzle can raise the nozzle according to the water level to remove bubbles and inject the accurate amount of fluid. In addition, the efficiency of the logistics process is enhanced by the smart QR code. Through experiments using the developed smart injection nozzle device, we have noticed that the accuracy of injection capacity, nozzle position, reaction time and building data exceeded the target value. Therefore, it expected that this machine will give more production and save a lot of manpower for packaging industry.

• 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.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.4
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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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• 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.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.15 no.1
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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.