• 한국기계가공학회지
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• 제9권5호
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• pp.28-33
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• 2010

A computer code was developed to simulate all three stages of the injection molding process: filling, packing and cooling by finite element method. The constitutive equation used here was compressible Leonov model. The PVT relationship was assumed to follow the Tait equation. The flow-induced birefringence was related to the calculated flow stresses through the linear stress-optical law. Based on the simulation, the Taguchi method was used to investigate the influences of injection molding conditions on the birefringence of a center gate disk. In addition, the optimal processing conditions were selected to minimize the birefringence and the birefringence difference along the positions of the disk.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2005년도 춘계학술대회 논문집
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• pp.109-114
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• 2005

The large injection molded parts technology such as instrument panel, front and rear bumper are presented for a precision molding. Some lead time and cost are required to product these part from design to mass product. Recently, CAE is widely used in product design, mold design and analysis of molding conditions to reduce time and cost. The optimal molding conditions can be obtained by DOE(Design of Experiment). The optimal design applications with CAE and DOE have been used in small molded parts. However, application to the large molded body is not reported. In this paper, optimization of injection molding process is studied for quality control in mass production of automobile bumper. Mold temperature difference is chosen through robust design of injection molding process, the molding process being optimized in term of shrinkage and deflection. The optimal conditions through DOE are validated by using injection molding analysis.

• 대한안전경영과학회지
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• 제5권4호
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• pp.209-217
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• 2003

The traditional Kanban needs a lot of preconditions for fitting conditions of dynamic production processing environment. The traditional Kanban isn't suitable conditions of dynamic production processing environment. Therefore conditions of dynamic production processing environment is needed more stable system. This study is describe CONWIP system such as suitable in dynamic production processing environment. Most Pull system is a Kanban system than use Kanban cards or signal for production management and inventory control. The object of Kanban system is reducing inventory between shop-floor that can reduce inventiry cost. If the system reduce the number of Kanban cards would be reduce the working process WIP, can be reduced and can be found all potential problem of production between shop-floors. This study apply to CONWIP system model for Korean industrial companies.

• 한국물환경학회지
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• 제27권5호
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• pp.617-624
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• 2011

In order to confirm the creation of the OH radical which influences to RNO bleaching processes, it experimented using laboratory reactor of dielectric barrier discharge plasma (DBDP). The experiments performed in about 4 kind process variables (diameter of ground electrode, diameter of discharge electrode, diameter of quartz tube and effect of air flow rate) which influence to process. In order to examine optimum conditions of design factors as shown in Box-Behnken experiment design, ANOVA analysis was conducted against four factors. The actual RNO removal at optimized conditions under real design constraints were obtained, confirming Box-Behnken results. Optimized conditions under real design constraints were obtained for the highest desirability at 1, 1 mm diameter of ground and discharge electrode, 6 mm diameter of quartz tube and 5.05 L/min air flow rate, respectively.

• Korean Chemical Engineering Research
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• 제60권1호
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• pp.51-61
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• 2022

Gas separation via hollow fibre membrane modules (HFMM) is deemed to be a promising technology for natural gas sweetening, particularly for lowering the level of carbon dioxide (CO₂) in natural gas, which can cause various problems during transportation and process operation. Separation performance via HFMM is affected by membrane properties, module specifications and operating conditions. In this study, a mathematical model for HFMM is developed, which can be used to assess the effects of the aforementioned variables on separation performance. Appropriate boundary conditions are imposed to resolve steady-state values of permeate variables and incorporated in the model equations via an iterative numerical procedure. The developed model is proven to be reliable via model validation against experimental data in the literature. Also, the model is capable of capturing axial variations of process variables as well as predicting key performance indicators. It can be extended to simulate a large-scale plant and identify an optimal process design and operating conditions for improved separation efficiency and reduced cost.

• 한국환경보건학회지
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• 제39권3호
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• pp.289-299
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• 2013

Objectives: For the field application of the dielectric barrier discharge plasma reactor, scale-up of the plasma reactor is needed. This study investigated the possibility of inactivation of microorganisms in sewage using pilot multi-plasma reactor. We also considered the possibility of degradation of total organic carbon (TOC) and nonbiodegradable matter ($UV_{254}$) in sewage. Methods: The pilot plasma reactor consists of plasma reactor with three plasma modules (discharge electrode and quartz dielectric tube), liquid-gas mixer, high voltage transformers, gas supply equipment and a liquid circulation system. In order to determine the operating conditions of the pilot plasma reactor, we performed experiments on the operation parameters such as gas and liquid flow rate and electric discharge voltage. Results: The experimental results showed that optimum operation conditions for the pilot plasma reactor in batch experiments were 1 L/min air flow rate), 4 L/min liquid circulation rate, and 13 kV electric discharge voltage, respectively. The main operation factor of the pilot plasma process was the high voltage. In continuous operation of the air plasma process, residual microorganisms, $UV_{254}$ absorbance and TOC removal rate at optimal condition of 13 kV were $10^{2.24}$ CFU/mL, 56.5% and 8.6%, respectively, while in oxygen plasma process at 10 kV, residual microorganisms, $UV_{254}$ absorbance and TOC removal rate at optimal conditions were $10^{1.0}$ CFU/mL, 73.3% and 24.4%, respectively. Electric power was increased exponentially with the increase in high voltage ($R^2$ = 0.9964). Electric power = $0.0492{\times}\exp^{(0.6027{\times}lectric\;discharge\;voltage)}$ Conclusions: Inactivation of microorganisms in sewage effluent using the pilot plasma process was done. The performance of oxygen plasma process was superior to air plasma process. The power consumption of oxygen plasma process was less than that of air plasma process. However, it was considered that the final evaluation of air and oxygen plasma must be evaluated by considering low power consumption, high process performance, operating costs and facility expenses of an oxygen generator.

• Design & Manufacturing
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• 제17권4호
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• pp.72-78
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• 2023

Injection molding is a process widely used in various industries because of its high production speed and ease of mass production during the plastic manufacturing process, and the product is molded by injecting molten plastic into the mold at high speed and pressure. Since process conditions such as resin and mold temperature mutually affect the process and the quality of the molded product, it is difficult to accurately predict quality through mathematical or statistical methods. Recently, studies to predict the quality of injection molded products by applying artificial neural networks, which are known to be very useful for analyzing nonlinear types of problems, are actively underway. In this study, structural optimization of neural networks was conducted by applying multi-task learning techniques according to the characteristics of the input and output parameters of the artificial neural network. A structure reflecting the characteristics of each process step was applied to the input parameters, and a structure reflecting the quality characteristics of the injection molded part was applied to the output parameters using multi-tasking learning. Building an artificial neural network to predict the three qualities (mass, diameter, height) of injection-molded product under six process conditions (melt temperature, mold temperature, injection speed, packing pressure, pacing time, cooling time) and comparing its performance with the existing neural network, we observed enhancements in prediction accuracy for mass, diameter, and height by approximately 69.38%, 24.87%, and 39.87%, respectively.

• 한국빅데이터학회지
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• 제4권2호
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• pp.61-71
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• 2019

반도체 웨이퍼의 표면을 연마하여 평탄화하는 Chemical Mechanical Planarization(CMP) 공정은 다양한 화학물질과 물리적인 기계장치에 의한 작용을 받기 때문에 공정을 안정적으로 관리하기 힘들다. CMP 공정에서 품질 지표로는 Material Removal Rate(MRR)를 많이 사용하고, CMP 공정의 안정적 관리를 위해서는 MRR을 예측하는 것이 중요하다. 본 연구에서는 머신러닝 기법들을 이용하여 CMP 공정에서 수집된 시계열 센서 데이터를 분석하여 MRR을 예측하는 모형과 공정 품질을 해석하기 위한 분류 모형을 개발한다. 나아가 분류 결과를 분석하여, CMP 공정 품질에 영향을 미치는 유의미한 변수를 파악하고 고품질을 유지하기 위한 공정 조건을 설명한다.

• 산업공학
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• 제21권2호
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• pp.221-228
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• 2008

The injection molding process is able to produce high precision manufactures as a single process with fast speed. However, the prices of both the mold and the molding machine are expensive, and the single process is very complex and difficult to compose of the exact relationship between the process setting conditions and the product quality. Therefore, the quality of a molded product often depends on a skillful engineer's operations in the design of both parts and molds. In this paper, the relationship between the process conditions and the defectiveness is built for better manufactures under settings of the appropriate parameters, and so it can reduce the setup time in the injection molding process. Quality Function Deployment (QFD) provides severe defectiveness factors along with the related process parameters. Also, neural networks estimate the relationship between defective factors and process setting parameters, and lead to reduce the defectiveness of molded parts.

• 말소리와 음성과학
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• 제1권4호
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• pp.61-71
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• 2009

This study aims to investigate whether the misalignment between syllable and word onsets due to the process of resyllabification affects Korean-English late bilinguals perceiving English continuous speech. Two word-spotting experiments were conducted. In Experiment 1, misalignment conditions (resyllabified conditions) were created by adding CVC contexts at the beginning of vowel-initial words and alignment conditions (non-resyllabified conditions) were made by putting the same CVC contexts at the beginning of consonant-initial words. The results of Experiment 1 showed that detections of targets in alignment conditions were faster and more correct than in misalignment conditions. Experiment 2 was conducted in order to avoid any possibilities that the results of Experiment 1 were due to consonant-initial words being easier to recognize than vowel-initial words. For this reason, all the experimental stimuli of Experiment 2 were vowel-initial words preceded by CVC contexts or CV contexts. Experiment 2 also showed misalignment cost when recognizing words in resyllabified conditions. These results indicate that Korean listeners are influenced by misalignment between syllable and word onsets triggered by a resyllabification process when recognizing words in English connected speech.