• Journal of the Society of Cosmetic Scientists of Korea
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• v.37 no.1
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• pp.43-53
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• 2011

The conditions in fluid-bed processor for nanoencapsulation of azelaic acid-milk nano powder for acne nanocosmetics were optimized by response surface methodology (RSM). The maximum value of yield was 70.97 %. The yield was appreciably influenced by inlet air temperature, atomizing pressure, and feeding speed. The particle size increased with an increase in the feeding speed and a decrease in the atomizing pressure. The elution rate in saline solutions was appreciably influenced by inlet air temperature and atomizing pressure. The moisture content increased with higher atomizing pressure, which was demonstrated to be similar to the nanoencapsulation characteristics related to water activity. The Hunter's L and b values increased with an increase in the inlet air temperature. The optimum conditions estimated by RSM for the maximized values of yield, moisture content, particle size and elution rate in skin suitability were $67{\sim}73^{\circ}C$ of inlet air temperature, 0.6 ~ 0.8 mL/min feeding speed and 1.8 ~ 2.0 kg/$cm^2$ of atomizing pressure, respectively. These estimated values were in agreement with those measured by real experiments.

• Journal of Pharmaceutical Investigation
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• v.39 no.1
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• pp.29-36
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• 2009

Since the quality of final products is significantly affected by the homogeneity of powder mixture, the powder blending process has been regarded as one of the critical pharmaceutical unit processes, especially for solid dosage forms. Accordingly, the monitoring to determine a blending process' end-point based on a faster and more accurate in-line/on-line analysis has attracted enormous attentions recently. Among various analytical tools, NIR (near-infrared) spectroscopy has been extensively studied for PAT (process analytical technology) system due to its many advantages. In this study, NIR spectroscopy was employed with an optical fiber probe for the in-line monitoring of fluid-bed blending process. The position of the probe, the ratio of binary powder mixture, the powder size differential and the back-flush period of the shaking bag were examined as principal process parameters. During the blending process of lactose and mannitol powders, NIR spectra were collected, corrected, calibrated and analyzed using MSC and PLS method, respectively. The probe position was optimized. A reasonable end-point was predicted as 1,500 seconds based on 5% RSD value. As a consequence, it was demonstrated that the blending process using a fluid-bed processor has several advantages over other methods, and the application of NIRS with an optical fiber probe as PAT system for a fluid-bed blending process could be high feasible.

• Journal of Drive and Control
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• v.16 no.3
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• pp.42-50
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• 2019

3D printing AM processes have advantages in complex shapes, customized fabrication and prototype development stage. However, due to various parameters based on both the machine and the material, the AM process can produce finished output after several trials and errors in the initial stage. As such, minimizing or optimizing negative factors for various parameters of the 3D printing AM process could be a solution to reduce the trial-and-error failures in the early stages of such an AM process. In addition, this can be largely solved through software simulation in the preprocessing process of 3D printing AM process. Therefore, the objective of this study was to investigate a simulation technology for the AM software, especially Ansys Inc. The metal 3D printing AM process, the AM process simulation software, and the AM process simulation processor were examined. Through this study, it will be helpful to understand 3D printing AM process and AM process simulation processor.

• Journal of Drive and Control
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• v.16 no.3
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• pp.51-58
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• 2019

The objective of this study was to investigate a simulation technology for the AM field based on ANSYS Inc.. The introduction of metal 3D printing AM process, and the examining of the present status of AM process simulation software, and the AM process simulation processor were done in the previous study (part 1). This present study (part 2) examined the use of the AM process simulation processor, presented in Part 1, through direct execution of Topology Optimization, Ansys Workbench, Additive Print and Additive Science. Topology Optimization can optimize additive geometry to reduce mass while maintaining strength for AM products. This can reduce the amount of material required for additive and significantly reduce additive build time. Ansys Workbench and Additive Print simulate the build process in the AM process and optimize various process variables (printing parameters and supporter composition), which will enable the AM to predict the problems that may occur during the build process, and can also be used to predict and correct deformations in geometry. Additive Science can simulate the material to find the material characteristic before the AM process simulation or build-up. This can be done by combining specimen preparation, measurement, and simulation for material measurements to find the exact material characteristics. This study will enable the understanding of the general process of AM simulation more easily. Furthermore, it will be of great help to a reader who wants to experience and appreciate AM simulation for the first time.