• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.05a
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• pp.882-885
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• 2002

Magnesium alloys are very attractive materials for appling to the development of autemobile parts or electric goods where light weight and higher stiffness. Due to higher ratio of strength vs. weight and stillness vs. weight, various magnesium alloys are well applied in much weight saving design applications though extrusion or die-casting process. However for the requisites of higher strength and weight savings, some new fabrication processes has been and it can be realized though the aid of injection modeling technology. To obtain the parametric data base for the injection molding process, various experiments were executed for AZ91D magnesium alloy. This paper propose the optimum condition of injection temperature, first and second pressure. the process was lined-up successfully often changing the injection unit. fluid pressure system from the conventional plastic injection molding process.

• Proceedings of the KSR Conference
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• 2005.11a
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• pp.1235-1240
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• 2005

In this status no inspection standard of quality in repair of present concrete structure has a problem to repair for simple experience. In this paper for this problem improvement, it made an analysis of relation to injection quantity of crack width, injection time of crack width, injection pressure of crack width, injection pressure and time, injection quantity of structural size, injection quantity of structural individual crack position, injection time about crack width. and structural thickness. The data gained in analysis result be judged that it will help in systematic quality control about concrete structural repair.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.9
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• pp.52-59
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• 2007

Fuel injection system greatly affects the performance of a direct injection diesel engine. A common rail injection system was introduced to satisfy the stringent emission standards, low fuel consumption, and low noise in recent years. The performance of a common-rail fuel injection system is strongly influenced by the injector characteristics. The common rail injector has evolved in order to improve its injection performance. The piezo-actuated injector is more suitable for common rail injection system due to its fast response and is expected to replace current solenoid-operated injector. In this study, nonlinear mathematical models are proposed for the solenoid-operated and the piezo-actuated injectors for control applications. Based on these models, the injection rate, which is one of the most important factors for the injection characteristics, is estimated using sliding mode observer. The simulation results and the experimental data show that the proposed sliding mode observers can effectively estimate the injection timing and the injection rate for both common-rail injectors.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.12
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• pp.635-640
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• 2016

It is important factors for a production system to get a profitable result in quality and reliability process. For this reason, there's are various type of research papers in a certain type of data acquisition and application to reliability and quality of the level of M2M devices. In general, a classification problem of slightly different signal such as sensing data is difficult to do with classical statistical methods. There's required real-time and instantaneous calculation properties in machine process. Especially a type of injection molding machine which has a property to be decided in accordance with short-term cycle process needs a solution that can be done a certain type of decision like as good or bad quality immediately. This paper presents a simple application of Kalman Filtering by single sensing data to injection molding process in order to get a correct answer from the real time sensing data.

• 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.

• Journal of Semiconductor Engineering
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• v.1 no.1
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• pp.1-12
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• 2020

Handling precise timing in high-speed transceivers has always been a primary design target to achieve better performance. Many different approaches have been tried, and one of those is utilizing the beneficial nature of injection locking. Though the phenomenon was not intended for building integrated circuits at first, its coupling effect between neighboring oscillators has been utilized deliberately. Consequently, the dynamics of the injection-locked oscillator (ILO) have been explored, starting from R. Adler. As many aspects of the ILO were revealed, further studies followed to utilize the technique in practice, suggesting alternatives to the conventional frequency syntheses, which tend to be complicated and expensive. In this review, the historical analysis techniques from R. Adler are studied for better comprehension with proper notation of the variables, resulting in numerical results. In addition, how the timing jitter or phase noise in the ILO is attenuated from noise sources is presented in contrast to the clock generators based on the phase-locked loop (PLL). Although the ILO is very promising with higher cost effectiveness and better noise immunity than other schemes, unless correctly controlled or tuned, the promises above might not be realized. In order to present the favorable conditions, several strategies have been explored in diverse applications like frequency multiplication, data recovery, frequency division, clock distribution, etc. This paper reviews those research results for clock multiplication and data recovery in detail with their advantages and disadvantages they are referring to. Through this review, the readers will hopefully grasp the overall insight of the ILO, as well as its practical issues, in order to incorporate it on silicon successfully.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.270-273
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• 2017

Productivity through reduced defects in plastic injection molding and reduced cycle times is a long-standing need in the injection industry. In particular, productivity is very urgent for the domestic injection industry, which is caught between the pursuit of latecomers such as China and technological gap with Germany and Japan which will not be narrowed down. Through 30 years of research and experience in the domestic injection industry, we have found that controlling the surface temperature of injection molds is the key of quality control. There have been various attempts to utilize advanced control techniques such as PID control, but the productivity against leading companies in Germany and Japan is still insufficient. Using Approximation Algorithm - "Knapsack" and "Minimum Makespan Scheduling", We want to show how to efficiently control objects with periodic repetitive data patterns that are difficult to solve with PID control. In addition, We want to propose that the control by Approximation Algorithm is effective enough to improve the productivity of the product by analyzing the data extracted from actual injection site.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.411-422
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• 2021

The autonomous driving system mounted on the unmanned vehicle recognizes the external environment through several sensors and derives the optimum control value through it. Recently, studies on physical level attacks that maliciously manipulate sensor data by performing signal-injection attacks have been published. signal-injection attacks are performed at the physical level and are difficult to detect at the software level because the sensor measures erroneous data by applying physical manipulations to the surrounding environment. In order to detect a signal-injection attack, it is necessary to verify the dependability of the data measured by the sensor. As so far, various methods have been proposed to attempt physical level attacks against sensors mounted on autonomous driving systems. However, it is still insufficient that methods for defending and detecting the physical level attacks. In this paper, we demonstrate signal-injection attacks targeting MEMS sensors that are widely used in unmanned vehicles, and propose a method to detect the attack. We present a signal-injection detection model to analyze the accuracy of the proposed method, and verify its effectiveness in a laboratory environment.

• Transactions of Materials Processing
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• v.29 no.4
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• pp.218-228
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• 2020

The prediction of final mass and optimized process conditions of injection molded products using Artificial Neural Network (ANN) were demonstrated. The ANN was modeled with 10 input parameters and one output parameter (mass). The input parameters, i.e.; melt temperature, mold temperature, injection speed, packing pressure, packing time, cooling time, back pressure, plastification speed, V/P switchover, and suck back were selected. To generate training data for the ANN model, 77 experiments based on the combination of orthogonal sampling and random sampling were performed. The collected training data were normalized to eliminate scale differences between factors to improve the prediction performance of the ANN model. Grid search and random search method were used to find the optimized hyper-parameter of the ANN model. After the training of ANN model, optimized process conditions that satisfied the target mass of 41.14 g were predicted. The predicted process conditions were verified through actual injection molding experiments. Through the verification, it was found that the average deviation in the optimized conditions was 0.15±0.07 g. This value confirms that our proposed procedure can successfully predict the optimized process conditions for the target mass of injection molded products.

• Nuclear Engineering and Technology
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• v.42 no.3
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• pp.297-304
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• 2010

In the design of advanced light water reactors (ALWRs) and in the safety assessment of currently operating nuclear power plants, it is necessary to evaluate the possibility of experiencing a degraded core accident and to develop innovative safety technologies in order to assure long-term debris cooling. The objective of this experimental study is to investigate the enhancement factors of dryout heat flux in debris beds by coolant injection from below. The experimental facility consists mainly of an induction heater, a double-wall quartz-tube test section containing a steel-particle bed and coolant injection and recovery condensing loop. A fairly uniform heating of the particle bed was achieved in the radial direction and the axial variation was within 20%. This paper reports the experimental data for 3.2 mm and 4.8 mm particle beds with a 300 mm bed height. The dryout heat density data were obtained for both the top-flooding and the forced coolant injection from below with an injection mass flux of up to $1.5\;kg/m^2s$. The dryout heat density increased as the rate of coolant injection increased. At a coolant injection mass flux of $1.0\;kg/m^2s$, the dryout heat density was ${\sim}6.5\;MW/m^3$ for the 4.8 mm particle bed and ${\sim}5.6\;MW/m^3$ for the 3.2 mm particle bed. The enhancement factors of the dryout heat density were 1.6-1.8.