• Advances in materials Research
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• v.6 no.2
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• pp.169-184
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• 2017

Electro-Discharge machining (EDM) is a thermal process comprising a complex metal removal mechanism. This method works by forming of a plasma channel between the tool and the workpiece electrodes leading to the melting and evaporation of the material to be removed. EDM is considered especially suitable for machining complex contours with high accuracy, as well as for materials that are not amenable to conventional removal methods. However, several phenomena can arise and adversely affect the surface integrity of EDMed workpieces. These have to be taken into account and studied in order to optimize the process. Recently, artificial neural networks (ANN) have emerged as a novel modeling technique that can provide reliable results and readily, be integrated into several technological areas. In this paper, we use an ANN, namely, the multi-layer perceptron and the back propagation network (BPNN) to predict the mean surface roughness of electro-discharge machined surfaces. The comparison of the derived results with experimental findings demonstrates the promising potential of using back propagation neural networks (BPNNs) for getting a reliable and robust approximation of the Surface Roughness of Electro-discharge Machined Components.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.159-162
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• 2004

플라즈마 공정 모델 개발에 역전파 신경망이 가장 많이 응용되고 있으나, 관여하는 다수의 학습인자로 인해 그 최적화가 매우 어렵다. Radial basis function network (RBFN)은 관여하는 학습인자의 수가 적어 그 최적화가 상대적으로 용이하지만, 두인자의 다양한 조합에 의해 RBFN의 예측성능이 상당히 영향을 받을 수 있다. 본 연구에서는 학습인자 상호간의 작용을 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 최적화하는 기법을 소개한다. 제안하는 알고리즘을 광도파로 제작을 위해 수행한 실리카 식각공정 데이터에 적용하여 평가하였다. 평가에 이용된 식각 응답은, 실리카 식각률, aluminum (Al) 식각률, Al 선택비, 그리고 실리카 프로파일 각도이다. 최적화한 모델은 종래의 모델과 비교하였으며, 그 향상도는 실리카 식각률, Al 식각률, Al 선택비, 그리고 실리카 프로파일 각도에 대해서 각 기 0.8%, 32.4%, 20.3%, 1.3% 등이었다. Al 식각률과 선택비에 대해서 예측성능은 상당이 향상되었다.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.272-275
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• 2004

플라즈마 식각공정은 소자제조를 위한 미세 패턴닝 제작에 이용되고 있다. 공정 메커니즘의 정성적 해석, 최적화, 그리고 제어를 위해서는 컴퓨터 예측모델의 개발이 요구된다. 역전파 신경망 (backpropagation neural network-BPNN) 모델을 개발하는 데에는 다수의 학습인자가 관여하고 있으며, 가장 그 최적화가 어려운 학습인자는 초기웨이트이다. 모델개발시, 초기웨이트는 random 값으로 설정이 되며, 이로 인해 초기웨이트의 최적화가 어렵다. 본 연구에서는 유전자 알고리즘 (genetic algorithm-GA)을 이용하여 BPNN의 초기웨이트를 최적화하였으며, 이를 식각공정 모델링에 적용하여 평가하였다. 실리카 식각공정 데이터는 $2^3$ 인자 실험계획법을 이용하여 수집하였으며, GA에 관여하는 두 확률인자의 영향을 42 인자 실험계획법을 이용하여 최적화 하였다. 종래의 모델에 비해, 최적화된 모델은 실리카 식각률, Al 식각률, Al 선택비, 그리고 프로파일 응답에 대해서 각 기 24%, 13%,, 16%, 그리고 17%의 향상률을 보였다. 이는 제안된 최적화 기법이 플라즈마 모델의 예측성능을 증진하는데 효과적으로 응용될 수 있음을 의미한다.

• The Korean Journal of Pharmacology
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• v.32 no.2
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• pp.269-281
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• 1996

Pharmacokinetics of prednisolone and prednisone undergoing reversible interconversion were analyzed from the model including this metabolic process. Blood samples were drawn serially upto 12 hours after I,V. bolus injection of 1 mg/kg prednisolone sodium phosphate and prednisone into 8 dogs as a crossover manner. Plasma concentrations of those two steroids were simultaneously measured with the method of HPLC. After injection, plasma concentrations of administered prednisolone and prednisone were declined with a biexponential pattern and their metabolic partner was rapidly formed. Plasma concentrations of those metaboite were decayed in parallel with their parent steroids throught the elimination phase. Apparent clearances of prednisolone and prednisone were $11.1{\pm}2.0\;ml/min/kg$ and $45.9{\pm}6.4\;ml/min/kg$, and they were underestimated by 29.4% and 33.6% compared to their real clearances$(15.7{\pm}4.4\;and\;69.2{\pm}17.7\;ml/min/kg)$ estimated using reversible interconversion model. Apparent volume of distribution of prednisolone$(1.32{\pm}0.43\;L/kg)$ and prednisone$(4.81{\pm}2.75\;L/kg)$ were overestimated by 53.5 and 52.7% and were compared to the real volumes $(0.86{\pm}0.30\;and\;3.15{\pm}2.13\;L/kg)$. Mean residence time of prednisolone$(2.0{\pm}0.61\;h)$ and prednisone$(1.74{\pm}0.74\;h)$ were much longer than the real sojourn time$(0.93{\pm}0.26\;and\;0.88{\pm}0.54\;h)$. Essential clearances In the reversible interconversion were greater as following orders: $Cl_{21}$(44.3 ml/min/kg) > $Cl_{20}$(24.2 ml/min/kg) > $Cl_{12}$ (7.9 ml/min/kg) > $Cl_{10}$(7.8 ml/min/kg). Estimated mean values of RF, EE, $%X^1_{ss}$ and $RHO^2_1$ were $0.31{\pm}0.10$, $1.49{\pm}0.23$, $69.3{\pm}16.7%$ and $0.65{\pm}0.10$, respectively. These results suggested that true pharmacokinetic parameters estimated from the model including reversible interconversion were significantly different from the apparent parameters estimated from the conventional mamillary model, and disposition of these two steroids seemed to be well explained by the model including reversible interconversion.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.11
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• pp.2075-2082
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• 2006

This paper presents modeling and optimization techniques for hish efficiency solar cell process on single-crystalline float zone (FZ) wafers. Among a sequence of multiple steps of fabrication, the followings are the most sensitive steps for the contact formation: 1) Emitter formation by diffusion; 2) Anti-reflection-coating (ARC) with silicon nitride using plasma-enhanced chemical vapor deposition (PECVD); 3) Screen-printing for front and back metalization; and 4) Contact formation by firing. In order to increase the performance of solar cells in terms of efficiency, the contact formation process is modeled and optimized using neural networks and genetic algorithms, respectively. This paper utilizes the design of experiments (DOE) in contact formation to reduce process time and fabrication costs. The experiments were designed by using central composite design which consists of 24 factorial design augmented by 8 axial points with three center points. After contact formation process, the efficiency of the fabricated solar cell is modeled using neural networks. Established efficiency model is then used for the analysis of the process characteristics and process optimization for more efficient solar cell fabrication.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.405-425
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• 2019

Objective: This study aimed to review the methodologies for evaluation of consumer spray products containing engineered nanomaterials (ENM), particularly focusing on inhalation exposure. Method: Literature on the evaluation methods for aerosolized ENM exposure from consumer spray products were collected through academic web searching. Common methodologies used in the literature, including research reports and academic articles, were also introduced. Results: The number of ENM-containing products have shown a considerable increase over recent years, from 54 in 2005 to 1,827 in 2018. Currently there is still discussion over the existing regulations with regard to product safety. Analysis of both ENM suspensions in the products and their aerosols is important for risk assessment. Comparison between the phases suggests how the size and concentration of particles change during the spray process. To analyze the ENM suspensions, dynamic light scattering, electron microscopy techniques, and inductively coupled plasma with mass spectrometry were used. In the aerosol monitoring, direct-reading instruments have been used to monitor the aerosols and conventional active sampling is used together to supplement the lack of real-time monitoring. There are also some models for estimating inhalation exposure. These models may be used to estimate mass exposure to nanomaterials contained in consumer products. Conclusion: Although there is no standardized method to evaluate ENM exposure from consumer products, many concerns about ENM have emerged. Every potential measure to reduce exposure to ENM from spray product use should be implemented through a precautionary recognition.

• Composites Research
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• v.19 no.2
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• pp.28-34
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• 2006

[ $Cu-TiB_2$ ] metal matrix composites with various weight fractions of $TiB_2$ were fabricated by combination of manufacturing process, SPS (self-propagating high-temperature synthesis) and SPS (spark plasma sintering). The feasibility of $Cu-TiB_2$ composites for welding electrodes and sliding contact material was investigated through experiments on the tensile properties, hardness and wear resistance. To obtain desired properties of composites, composites are designed according to reinforcement's shape, size and volume fraction. Thus proper modeling is essential to predict the effective material properties. The elastic moduli of composites obtained by FEM and tensile test were compared with effective properties from the original Eshelby model, Eshelby model with Mori-Tanaka theory and rule-of-mixture. FEM result showed almost the same value as the experimental modulus and it was found that Eshelby model with Mori-Tanaka theory predicted effective modulus the best among the models.