• Design & Manufacturing
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• v.1 no.1
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• pp.1-5
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• 2007

Double injection molding process is very efficient molding-method for molding the products which is consist of multi-materials. Fuel-tube holder which is necessary for automobil power train and circulation systems is composed of plastic and rubber materials to minimize the vibration and pulsation noises. In existing process, fuel-tube holder was made by the insert molding process or assembly process after molding. If fuel-tube holder is manufactured by double injection molding process, it may be realize to improve the product quality, efficiency of molding-process and retrenchment of manufacturing cost. In this study, for manufacturing fuel-tube holder by double injection molding process, the analysis of joining characteristics between PA6(polyamide 6) and TPE(thermoplastic elastomer) was executed and the double injectin mold for molding fuel-tube holder with core toggle mechanism was fabricated. Finally, fuel-tube holder was molding using fabricated double injection mold.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.4
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• pp.50-56
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• 2011

Injection molding process is one of the popular manufacturing methods to produce plastic parts with high efficiency and low cost. Ball seat for automobile suspension is made by an injection molding process as a part to support pivot function of ball joint consisted of ball stud and housing. It is necessary for a ball seat to have a dimensional stability in the three dimensional inner area to be contacted with ball stud. In this paper, the dimensional stability of inner surface is indirectly analyzed by checking the difference of inner diameter around the circumferential direction and the thickness variation at the top part of ball seat. Measurement was performed by using the coordinate measuring machine and the fixture to hold ball seat. Optimization of injection molding processes such as injection time, cooling time and temperatures of cylinder barrel was derived to reduce the difference of inner diameter and the thickness variation at the top part of ball seat based on the Taguchi method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.3
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• pp.1-6
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• 2012

This study is focused on simulation technology in injection molding process for plastic optic lenses. The CAE program, $3D-TIMON^{TM}$ is used for the injection molding simulation with O-PET resin material. The design for different gate shape and runner layout has been under review by CAE simulation results. Moreover, the prediction of birefringence and polarized light in injection molded optic lenses has been tested by the CAE Program. The simulation results have been expected to effectively use in the design of injection molding mould.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.33 no.3
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• pp.201-206
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• 2009

The focus of this work is placed on the analysis of the mixture formation process under the evaporative diesel-free spray conditions. In order to examine homogeneity of mixture within the vapor phase region of the injected spray, image analysis was carried out based on the entropy of statistical thermodynamics. As an experimental parameter, the injection pressure and ambient gas density were selected, and effects of the injection pressure and density variation of ambient gas on the mixture formation process in the evaporative diesel spray were investigated. In the case of application of the thermodynamic entropy analysis to evaporative diesel spray, the value of the dimensionless entropy always increases with increase in time from injection start. Consequently, the dimensionless entropy in the case of the higher injection pressure is higher than that of lower injection pressure during initial injection period.

• Design & Manufacturing
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• v.16 no.3
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• pp.1-8
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• 2022

In this study, an artificial neural network(ANN) was constructed to establish the relationship between process condition prameters and the qualities of the injection-molded product in the injection molding process. Six process parmeters were set as input parameter for ANN: melt temperature, mold temperature, injection speed, packing pressure, packing time, and cooling time. As output parameters, the mass, nominal diameter, and height of the injection-molded product were set. Two learning structures were applied to the ANN. The single-task learning, in which all output parameters are learned in correlation with each other, and the multi-task learning structure in which each output parameters is individually learned according to the characteristics, were constructed. As a result of constructing an artificial neural network with two learning structures and evaluating the prediction performance, it was confirmed that the predicted value of the ANN to which the multi-task learning structure was applied had a low RMSE compared with the single-task learning structure. In addition, when comparing the quality specifications of injection molded products with the prediction values of the ANN, it was confirmed that the ANN of the multi-task learning structure satisfies the quality specifications for all of the mass, diameter, and height.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.10b
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• pp.333-334
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• 2002

Injection-compression molding parts are many cases with complicated boundary condition which is difficult to analysis of mold characteristics precisely. In this study, the effects of various process parameters such as multi-point gate location, initial charge volume, injection time and pressure have been investigated using finite element method to fomulate the melt front advancement during the mold filling process. A general governing equation for tracking the filling process during injection-compression molding is applied to volume of fluid method. To verify the results of present analysis, they are compared with those of the other paper. The results show a strong effect of processing conditions as a result of variations in the three-dimensional complex geometry model.

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.37-42
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• 2007

We present a powder injection molding technique of fabricating cemented tungsten carbide(WC) balls for milling and dispersing nano-powder in this paper. The conventional powder metallurgy approach is investigated to reveal its drawbacks of density non-homogeneity. New procedures of powder injection molding for the homogeneous high-precision WC balls, involving the binding process, powder injection molding process and sintering process, are presented in detail. Each process is investigated empirically and numerically to obtain its engineering information, which can used for process optimization.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.7-13
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• 2015

The production processes were reviewed through the injection analysis of the shift lever as a core component of an auto lever installed in the automatic transmission of cars. The injection analysis was carried out for the shift lever and rod among the components in a shift lever module. The shift lever and rod are designed for injection molding with the insertion of a tube, a pin cable plate, and a steel rod for securing the strength of the product. The charging time, failure of injection molding, weld line, air trap, and deformation were reviewed according to this insert. Analyses on various gate positions were carried out for reviewing the cultivation and deformation of fiber around major components, such as the generation section of manipulation feeling and assembly section, so that optimal gate conditions might be reviewed and reflected in the mold design. Finally, we plan to compare the analysis results with the production of trial products.

• Design & Manufacturing
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• v.14 no.4
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• pp.25-39
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• 2020

In this study, an edge device that monitors the injection molding process by measuring the mold vibration(acceleration) signal and the mold surface temperature was developed and evaluated its performance. During injection molding, signals of the injection start, V/P switchover, and packing end sections were obtained through the measurement of the mold vibration and the injection time and packing time were calculated by using the difference between the times of the sections. Then, the mold closed and mold open signals were obtained using a magnetic hall sensor, and cycle time was calculated by using the time difference between the mold closed time each process. As a result of evaluating the performance by comparing the process data monitored by the edge device with the shot data recorded on the injection molding machine, the cycle time, injection time, and packing time showed very small error of 0.70±0.38%, 1.40±1.17%, and 0.69±0.82%, respectively, and the values close to the actual were monitored and the accuracy and reliability of the edge device were confirmed. In addition, it was confirmed that the mold surface temperature measured by the edge device was similar to the actual mold surface temperature.

• Journal of Korean Society for Quality Management
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• v.51 no.1
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• pp.55-65
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• 2023

Purpose: The purpose of this study is to propose an optimization process to improve product yield in the process using process data. Recently, research for low-cost and high-efficiency production in the manufacturing process using machine learning or deep learning has continued. Therefore, this study derives major variables that affect product defects in the manufacturing process using eXplainable Artificial Intelligence(XAI) method. After that, the optimal range of the variables is presented to propose a methodology for improving product yield. Methods: This study is conducted using the injection molding machine AI dataset released on the Korea AI Manufacturing Platform(KAMP) organized by KAIST. Using the XAI-based SHAP method, major variables affecting product defects are extracted from each process data. XGBoost and LightGBM were used as learning algorithms, 5-6 variables are extracted as the main process variables for the injection process. Subsequently, the optimal control range of each process variable is presented using the ICE method. Finally, the product yield improvement methodology of this study is proposed through a validation process using Test Data. Results: The results of this study are as follows. In the injection process data, it was confirmed that XGBoost had an improvement defect rate of 0.21% and LightGBM had an improvement defect rate of 0.29%, which were improved by 0.79%p and 0.71%p, respectively, compared to the existing defect rate of 1.00%. Conclusion: This study is a case study. A research methodology was proposed in the injection process, and it was confirmed that the product yield was improved through verification.