• Membrane Journal
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• v.11 no.1
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• pp.29-37
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• 2001

본 논문은 인공 진피와 조직공학용 scaffold로 이용하기 위해 다공성 membrane로서 gelatin-based sponge의 효율성을 연구하였다. 불용성의 다공성 membrane은 1-ethyl-(3-3dimethylaminopropyl)carbodiimide(EDC)로 가교하여 제조하였다. Fourier-transformed infrared (FT-IR) spectroscopy, scanning electron microscopy(SEM) 그리고 Instron analysis로 다공성 membrane의 특성을 조사하였다. 다공성 membrane은 용적당 큰 표면적을 제공하는 micro porous한 구조를 가지고 있다. Gelatin/hyaluronic acid (HA) membrane의 공경크기는 40~200$\mu\textrm{m}$이다. HA의 첨가는 다공성 membrane의 기계적 강도와 세포부착능력에 영향을 미쳤다. Gelatin/HA 다공성 membrane의 압축강도는 collagen과 비슷하며, 세포배양과 인공진피 transplantation에 있어서의 충분한 기계적 강도를 가지고 있다. Fibroblasts를 함유한 진피기질을 제조하기 위해 직경 8mm의 다공성 membran에 4$\times$10(sup)5cells/membrane의 세포밀도로 fibroblast를 배양하였다. GH91 porous membrane에서의 fibroblast 부착성은 GH55 porous membrane에서보다 우수하였다. 삼차원 구조의 gelatin/HA membrane matrix에서의 fibroblast의 배양은 생체내 조건과 유사한 생리적 환경을 제공하였다.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1415-1418
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• 2008

Scaffold fabrication for regenerating functional human tissues has an important role in tissue engineering, and there has been much progress in research on scaffold fabrication. However, current methods are limited by the mechanical properties of existing biodegradable materials and the irregular structures that they produce. Recently, Solid freeform fabrication (SFF) technology was remarked by fabricating 3D free-form micro-structures. Among SFF technologies, we tried to fabricate scaffolds using micro-stereolithography which contain the highest resolution of all SFF technologies and precision deposition system which can use various biomaterials. And we developed the CAD/CAM system to automate the process of scaffold fabrication and fabricate the patient customized scaffolds. These results showed the unlimited possibilities of our SFF technologies in tissue engineering.

• Journal of the Korean Society of Visualization
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• v.11 no.1
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• pp.16-21
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• 2013

In this study, diffusion coefficients of 20 kDa FITC-dextran in 2% agarose gel with different cell concentrations were measured using fiberoptic-based fluorescence recovery after photobleaching technique. As increasing cell concentration suspended in agarose gel, the diffusion coefficients were decreased. The diffusion coefficient of agarose gel which contains $10{\times}10^6$ cells/ml was decreased to 11% that of in agarose gel without cells. The distribution of fluorescence dye in 3D scaffold was also simulated. The simulation result shows that the diffusion coefficient is more significant factor than the scaffold structure.

• Journal of the Semiconductor & Display Technology
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• v.19 no.3
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• pp.77-81
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• 2020

In this paper, we create scaffold defect classification models using machine learning based data. We extract the characteristic from collected scaffold external images using USB camera. SVM, KNN, MLP algorithm of machine learning was using extracted features. Classification models of three type learned using train dataset. We created scaffold defect classification models using test dataset. We quantified the performance of defect classification models. We have confirmed that the SVM accuracy is 95%. So the best performance model is using SVM.

• Journal of the Semiconductor & Display Technology
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• v.18 no.3
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• pp.25-30
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• 2019

In this paper, we have analyzed manufacturing problems of the scaffold with pores using FDM 3D printer and PLGA. We suggested the solutions using 3SC practical TRIZ. We selected the final solution used machine learning. We reduced number of experiments using most influential factor after analysis print factors. We printed the scaffold and measured pore size. We created the regression model using python tensorflow. The print condition data of measured pore size was used as training data. We predicted the pore size of printed condition using regression model. We printed the scaffold using the predicted the print condition data. We quantitatively compare the predicted scaffold pore size data and the measured scaffold pore size data. We got satisfactory result.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.26-30
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• 2020

In this paper, we studied the problem if the experiment number occurring in order to identify defect in scaffold. We need to change each of the 5 print factor to predict defect when printing disk type scaffold using FDM 3d printer. So then the number of scaffold print will be more than 100,000 times. This experiment number is difficult to perform in the field. In order to solve this problem, we have produced a prediction model based on machine learning multiple linear regression using print conditions and defect scaffold data for print conditions. The prediction model produced was verified through experiments. The verification confirmed that the error was less than 0.5 %. We have confirmed that satisfied within the target margin of error 5 %.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.46-50
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• 2019

In this paper, We used the regression model of machine learning for improve the print quantity problem when which print scaffold with 400 ㎛ pore using FDM 3d printer. We have difficult to experiment with changing all factors in the field. So we reduced print quantity by selected two factors that most impact the pore size. We printed and measured scaffold 5 times under same conditions. We created regression model using scaffold pore size and print conditions. We predicted pore size of untested print condition using the regression model. After print scaffold with 400 ㎛ pore, we printed scaffold 5 times under same conditions. We compare the predicted scaffold pore size and the measured scaffold pore size. We confirmed that error is less than 1 % and we verified the results quantitatively.

• Journal of the Semiconductor & Display Technology
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• v.18 no.2
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• pp.92-97
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• 2019

In this paper, we investigated the problems and solutions in the production of scaffolds using commercially available FDM 3D printers. We used ADRIGE TRIZ algorithm to systematically analyze the problems and suggest solutions. We printed scaffolds using suggested solutions. We measured the pore size and printing time of the scaffolds. We have confirmed that the printing precision is greater than 99% and the printing time is decreased by half. The suggested solutions proved its validity through experiments and showed satisfactory results.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.125-130
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• 2021

Scaffold is used to produce bio sensor. Scaffold is required high dimensional accuracy. 3D printer is used to manufacture scaffold. 3D printer can't detect defect during printing. Defect detection is very important in scaffold printing. Real-time defect detection is very necessary on industry. In this paper, we proposed the method for real-time scaffold defect detection. Real-time defect detection model is produced using CNN(Convolution Neural Network) algorithm. Performance of the proposed model has been verified through evaluation. Real-time defect detection system are manufactured on hardware. Experiments were conducted to detect scaffold defects in real-time. As result of verification, the defect detection system detected scaffold defect well in real-time.

• Journal of the Semiconductor & Display Technology
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• v.19 no.4
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• pp.59-64
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• 2020

In this paper, we found the solution using data based machine learning regression method to check the pore shape, to solve the problem of the experiment quantity occurring when producing scaffold with the 3d printer. Through experiments, we learned secured each print condition and pore shape. We have produced the scaffold from scaffold pore shape defect prediction model using multiple linear regression method. We predicted scaffold pore shapes of unsecured print condition using the manufactured scaffold pore shape defect prediction model. We randomly selected 20 print conditions from various predicted print conditions. We print scaffold five times under same print condition. We measured the pore shape of scaffold. We compared printed average pore shape with predicted pore shape. We have confirmed the prediction model precision is 99 %.