• 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.20 no.2
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• pp.109-114
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• 2021

When we inspect scaffold defect using sight, inspecting performance is decrease and inspecting time is increase. We need for automatically scaffold defect detection method to increase detection accuracy and reduce detection times. In this paper. We produced scaffold defect classification models using densenet, alexnet, vggnet algorithms based on CNN. We photographed scaffold using multi dimension camera. We learned scaffold defect classification model using photographed scaffold images. We evaluated the scaffold defect classification accuracy of each models. As result of evaluation, the defect classification performance using densenet algorithm was at 99.1%. The defect classification performance using VGGnet algorithm was at 98.3%. The defect classification performance using Alexnet algorithm was at 96.8%. We were able to quantitatively compare defect classification performance of three type algorithms based on CNN.

• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.99-103
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• 2021

Warpage defect detecting of scaffold is very important in biosensor production. Because warpaged scaffold cause problem in cell culture. Currently, there is no detection equipment to warpaged scaffold. In this paper, we produced detection model for shape warpage detection using deep learning based CNN. We confirmed the shape of the scaffold that is widely used in cell culture. We produced scaffold specimens, which are widely used in biosensor fabrications. Then, the scaffold specimens were photographed to collect image data necessary for model manufacturing. We produced the detecting model of scaffold warpage defect using Densenet among CNN models. We evaluated the accuracy of the defect detection model with mAP, which evaluates the detection accuracy of deep learning. As a result of model evaluating, it was confirmed that the defect detection accuracy of the scaffold was more than 95%.

• 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.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.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 %.

• Journal of Veterinary Clinics
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• v.33 no.6
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• pp.332-339
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• 2016

Polycaprolactone (PCL) scaffold have been developed as an alternative to natural donor tissue to repair a large osteochondral defect. The objective of this study is to evaluate efficacy and biocompatibility of bilayer PCL scaffold implanted for osteochondral repair in rabbit. Twenty-two male New Zealand White rabbits were used in this animal experiment. Rabbits were divided into three groups. Experimental surgery was carried out under general anesthesia. Osteochondral defects (5 mm diameter and 5 mm deep) were made in the center of the patellar groove using a 5 mm diameter biopsy punch. In group I (3D plotting) and group II (salt-leaching), the scaffold was implanted using the press-fitted technique into the defect. In control group, after osteochondral defect was created, the defect was left without implant. After four and eight weeks, rabbits were sacrificed and the defects were evaluated by macro -and microscopical methods. There were not found animal death and severe inflammatory evidence during the experimental periods. There were no significant differences between the experimental groups in gross evaluation. However the group I scored significantly higher than group II at 8 weeks in histological evaluation (P < 0.05). The 3-D plotting PCL scaffold was more suitable method for reconstruction of osteochondral defect than a salt-leaching PCL scaffold.

• Archives of Plastic Surgery
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• v.32 no.4
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• pp.521-528
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• 2005

The purpose of this research is to find out the degree of cartilage regeneration by inserting the atelo-collagen scaffold obtained from dermis of a calf on cartilage defect site. Dissection underneath the perichondrium by the periosteal elevator on both side of ears of six New Zealand white rabbits were made to expose the cartilage, leaving pairs of circular holes 3, 6, 9 mm width with punches. One hole was left for a control, and on the other hole atelo-collagen scaffold of the same size was transplanted. In postoperative 1, 2, 4 weeks, the tissues were dyed. The length of long axis of neocartilage was measured through an optical microscope with a 0.1 mm graduation at original magnification, ${\times}40$. In the first and second week, both group showed no sign of cartilage regeneration. In the fourth week, regeneration on marginal portions was observed on all groups and the average values of length of long axis of neocartilage according to defect size were as follows: In the cases with 3mm defect, it was $0.85{\pm}0.30mm$ in the control group, and $1.85{\pm}0.38mm$ in the graft group; in the cases with 6 mm defect, $1.33{\pm}0.58mm$ in the control group, and $2.25{\pm}0.46mm$ in the graft group; and in the cases with 9 mm defect, $2.33{\pm}0.77mm$ in the control group, and $4.47{\pm}1.39mm$ in the graft group. This means that the collagen scaffold has an influence on the regeneration of neocartilage. But the relative ratio of the length of neocartilage to cartilage defect size was not significant in the statistics.

• Journal of the Semiconductor & Display Technology
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• v.22 no.1
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• pp.54-58
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• 2023

In this study, we compared the detection accuracy of the parameters of the scaffold failure detection model. A detection algorithm based on convolutional neural network was used to construct a failure detection model for scaffold. The parameter properties of the model were changed and the results were quantitatively verified. The detection accuracy of the model for each parameter was compared and the parameter with the highest accuracy was identified. We found that the activation function has a significant impact on the detection accuracy, which is 98% for softmax.

• Journal of the Semiconductor & Display Technology
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• v.22 no.2
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• pp.40-44
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• 2023

The defect detection based on deep learning requires minimal loss and high accuracy to pinpoint product defects. In this paper, we confirm the loss rate of deep learning training based on disc-shaped artificial scaffold images. It is intended to compare the performance of Cross-Entropy functions used in object detection algorithms. The model was constructed using normal, defective artificial scaffold images and category cross entropy and sparse category cross entropy. The data was repeatedly learned five times using each loss function. The average loss rate, average accuracy, final loss rate, and final accuracy according to the loss function were confirmed.