• Title/Summary/Keyword: 광학물성치

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Analysis of Optical Characteristics of Oil Immersion Lens in Aqueous Environment (액상유체 오일 이멀전 렌즈의 광학 물성치 해석)

  • Choi, Hae Woon
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.18 no.11
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    • pp.18-23
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    • 2019
  • The oil immersion method can be used to create objective lenses with long working distances without sacrificing the focusing resolution for laser processing. In this study, a space in which air or oil can be filled was formed in the middle of a lens for analyzing the optical properties of a liquid-oil immersion lens. As the refractive media, air and oils of different refractive index values (1.2 and 1.5, respectively) were used. A simulation was conducted in the ZEMAX software environment using the ray-tracing technique, and the performance of the oil immersion lens was verified by determining its image distortion and focal length (FL) in each medium. In the case of air, the calculated FL was 0.813 mm, whereas the imaged FLs were 1.594 mm and 8.126 mm when the refraction indices were 1.2 and 1.5, respectively. The FL of an oil immersion lens could be increased considerably. In terms of image distortion, the oil immersion lens exhibited little distortion at the center in all cases, but different degrees of image distortion were observed at different points away from the center depending on the refraction index degree.

Laser Beam Scattering Analysis in Aqueous Environments (액상유체 환경하에서 레이저빔의 산란 해석)

  • Choi, Hae Woon
    • Journal of the Korean Society of Manufacturing Process Engineers
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    • v.18 no.2
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    • pp.91-95
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    • 2019
  • A new experimental approach is proposed to detect a specific polymer dissolved in a liquid. Distinctive optical properties were found using a laser scattering technique when there is a polymer compound with almost no difference in optical properties (index of refraction) in a liquid phase. The index of refraction, which determines the refraction of light, is obtained by dissolving PCL and PLA. The used samples are biodegradable materials with similar properties and dissolved in a mixture of Chloroform 7: Methanol 3. To predict the optical properties, a 632-nm diode laser was used as the light source of the device, and an integrating sphere was used as the light receiver. Although PCL and PLA had a similar index of refraction of 1.46-1.48, the dissolved PCL showed a relative transmittance of 43%, and the dissolved PLA had a relative transmittance of 34%. The difference in optical properties of the pure polymer compound in the solid state or liquid state is not significantly different, and the difference in the dissolved state in the specific solvent is remarkable because the solubility differs in a specific solution and is randomly distributed.

Fabrication of Radar Absorbing Shells Made of Hybrid Composites and Evaluation of Radar Cross Section (하이브리드 복합재를 이용한 레이더 흡수 쉘의 제작 및 레이더 단면적 평가)

  • Jung, Woo-Kyun;Ahn, Sung-Hoon;Ahn, Bierng-Chearl;Park, Seoung-Bae;Won, Myung-Shik
    • Composites Research
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    • v.19 no.1
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    • pp.29-35
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    • 2006
  • The avoidance of enemy's radar detection is very important issue in the modem electronic weapon system. Researchers have studied to minimize reflected signals of radar. In this research, two types of radar absorbing structure (RAS), 'C'-type shell and 'U'-type shell, were fabricated using fiber-reinforced composite materials and their radar cross section (RCS) were evaluated. The absorption layer was composed of glass fiber reinforced epoxy and nano size carbon-black, and the reflection layer was fabricated with carbon fiber reinforced epoxy. During their manufacturing process, undesired thermal deformation (so called spring-back) was observed. In order to reduce spring-back, the bending angle of mold was controlled by a series of experiments. The spring-back of parts fabricated by using compensated mold was predicted by finite element analysis (ANSYS). The RCS of RAS shells were measured by compact range and predicted by physical optics method. The measured RCS data was well matched with the predicted data.