In this study, a spray dryer is used to make granules of Y2O3 and YF3, and then Y5O4F7 is synthesized following heat treatment of them under Ar gas atmosphere at 600 oC. Single and binary monomer mixtures are compared and analyzed to optimize photocurable monomer system for DLP 3D printing. The mixture of HEA and TMPTA at 8:2 ratio exhibits the highest photocuring properties and low viscosity with shear thinning behavior. The optimized photocurable monomer and synthesized Y5O4F7 are therefore mixed and applied to printing process at variable solid contents (60, 70, 80, & 85 wt.%) and light exposure times. Under optimal light exposure conditions (initial exposure time: 1.2 s, basic exposure time: 5 s), YOF composites at 60, 70 & 80 wt.% solid contents are successfully printed. As a result of measuring the size of the printed samples compared to the dimensions of the designed bar type specimen, the deviation is found to increase as the YOF solid content increases. This shows that it is necessary to maximize the photocuring activity of the monomer system and to optimize the exposure time when printing using a high-solids ceramic slurry.

목적 : 3D 시뮬레이션을 통해 구현된 Gullstrand 모형안에서 눈의 광학적 특성을 분석하였다. 방법 : 시뮬레이션 프로그램인 SPEOS를 이용하여 Gullstrand 모형안을 모델링하였다. 결과 : 모델링된 모형안으로 입사된 평행광선은 모두 망막 앞에 결상하는 것으로 나타났으며, 이는 근사 없이 계산한 결과 및 일반적으로 잘 알려진 결과와 일치하였다. 평행광선의 입사 높이에 따른 초점심도를 분석한 결과 입사 높이에 따라 지수 함수 형태로 급격히 감소하였다. 또한 구면수차는 입사광선의 높이에 따라 비선형적으로 증가하였다. 결론 : 본 연구와 같이 생체적으로 접근이 어려운 눈의 광학적 현상은 시뮬레이션에 따른 결과 분석을 통해 이 해도를 좀 더 높일 수 있고, 눈의 다양한 광학적 현상에 적용될 수 있을 것으로 판단된다.

Recently, the demand for atypical structures with functions and sculptural beauty is increasing in the construction industry. Existing mold-based structure production methods have many advantages, but building complex atypical structures represents limitations due to the cost and technical characteristics. Production methods using molding are suitable for mass production systems, but production cost, construction period, construction cost, and environmental pollution can occur in small quantity batch production. The recent trend in the construction industry calls for new construction methods of customized small quantity batch production methods that can produce various types of sophisticated structures. In addition to the economic effects of developing related technologies of 3D Concrete Printers (3DCP), it can enhance national image through the image of future technology, the international status of the construction civil engineering industry, self-reliance, and technology export. Until now, 3DCP technology has been carried out in producing and utilizing residential houses, structures, etc., on land or manufacturing on land and installing them underwater. The final purpose of this research project is to produce marine structures by directly printing various marine structures underwater with 3DCP equipment. Compared to current underwater structure construction techniques, constructing structures directly underwater using 3DCP equipment has the following advantages: 1) cost reduction effects: 2) reduction of construct time, 3) ease of manufacturing amorphous underwater structures, 4) disaster prevention effects. The core element technology of the 3DCP equipment is to extrude the transferred composite materials at a constant quantitative speed and control the printing flow of the materials smoothly while printing the output. In this study, the extruding module of the 3DCP equipment operates underwater while developing an extruding module that can control the printing flow of the material while extruding it at a constant quantitative speed and minimizing the external force that can occur during underwater printing. The research on the development of 3DCP equipment for printing concrete structures underwater and the preliminary experiment of printing concrete structures using high viscosity low-flow concrete composite materials is explained.

In this study, the MoS2 nanoparticles grown on crumpled 3D graphene microball (3D GM–MoS2) was synthesized using a microfluidic droplet generator with thermal evaporation-driven capillary compression and hydrothermal reaction. The morphology and size of 3D GM–MoS2 are controlled by the concentration of nano-sized graphene oxide (GO) and the flow rate of oil phase on the droplet generator. The 3D GM–MoS2 with fully sphere-shape and uniform size (~ 5 μm), and homogeneous growth of MoS2 nanoparticles could be synthesized at the flow rate of the oil phase of 60 μL/min with the optimized GO concentration of 1.0 mg/mL, and ( NH4)2MoS4 concentration of 2.0 mg/mL.

Fused Deposition Modeling (FDM), also known as Fused Deposition Modeling (FFF), is the most widely used type of 3D printing at the consumer level. The FDM 3D printer extracts thermoplastic filaments such as ABS (Acrylonitrile Butadiene Styrene) and Polyactive Acid (PLA) through heated nozzles to dissolve the material. It works by applying layers of plastic to build platforms. Various demands for 3D printers increased, and among these demands, there was also a demand for various filament colors. ABS, one of the main filamentous materials for 3D printers, is easy to color in a variety of colors and has been studied to meet the needs of these users. Through quantitative measurements in this work, we confirm that color differences remain depending on the difference in placement on the 3D printer bad. In addition, the temperature of the specimen was measured at the start of 3D printing, during manufacturing, and at the completion of manufacturing, and the inner and central sides remained similar, but the outer sides were 5 degrees lower. These temperature differences accumulate as layers pile up, resulting in differences in weight or color, which in turn meet consumer and producer needs in the 3D printer industry.

Metal three-dimensional (3D) printing is an important emerging processing method in powder metallurgy. There are many successful applications of additive manufacturing. However, processing parameters such as laser power and scan speed must be manually optimized despite the development of artificial intelligence. Automatic calibration using information in an additive manufacturing database is desirable. In this study, 15 commercial pure titanium samples are processed under different conditions, and the 3D pore structures are characterized by X-ray tomography. These samples are easily classified into three categories, unmelted, well melted, or overmelted, depending on the laser energy density. Using more than 10,000 projected images for each category, convolutional neural networks are applied, and almost perfect classification of these samples is obtained. This result demonstrates that machine learning methods based on X-ray tomography can be helpful to automatically identify more suitable processing parameters.