• 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.21 no.2
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• pp.68-73
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• 2022

In this study, we produced the surface defect detection model to automatically detect defect bone plates using a deep learning algorithm. Bone plates with a width and a length of 50 mm are most used for fracture treatment. Normal bone plates and defective bone plates were printed on the 3d printer. Normal bone plates and defective bone plates were photographed with 1,080 pixels using the webcam. The total quantity of collected images was 500. 300 images were used to learn the defect detection model. 200 images were used to test the defect detection model. The mAP(Mean Average Precision) method was used to evaluate the performance of the surface defect detection model. As the result of confirming the performance of the surface defect detection model, the detection accuracy was 96.3 %.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.153-158
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• 2023

This research was conducted to reduce the defect rate caused by nozzle clogging of printing heads used in binder jet 3D printers. The binder jet 3D printing technology may adhere to the printing head nozzle by dispersing powder due to mechanical operation such as transferring the printing head and supplying powder, and may cause nozzle clogging by natural curing at the nozzle end depending on the type of binder used. To solve this problem, this study created a cleaning module exclusively for printing heads to check whether the durability of printing heads is improved through analysis of printing results before and after using the cleaning module. To this end, this research used a thermal bubble jet printing head, and the used powder was studied using gypsum powder.

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.13-15
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• 2014

Nowadays, with advanced 3D printing techniques, the custom-made implant can be manufactured for the patient. Especially in skull reconstruction, it is difficult to design the implant due to complicated geometry. In large defect, an autograft is inappropriate to cover the defect due to donor morbidity. We present the process of manufacturing the 3D custom-made implant for skull reconstruction. There was one patient with skull defect repaired using custom-made 3D titanium implant in the plastic and reconstructive surgery department. The patient had defect of the left parieto-temporal area after craniectomy due to traumatic subdural hematoma. Custom-made 3D titanium implants were manufactured by Medyssey Co., Ltd. using 3D CT data, Mimics software and an EBM (Electron Beam Melting) machine. The engineer and surgeon reviewed several different designs and simulated a mock surgery on 3D skull model. During the operation, the custom-made implant was fit to the defect properly without dead space. The operative site healed without any specific complications. In skull reconstruction, autograft has been the treatment of choice. However, it is not always available and depends on the size of defect and donor morbidity. As 3D printing technique has been advanced, it is useful to manufacture custom-made implant for skull reconstruction.

• Journal of Dental Rehabilitation and Applied Science
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• v.35 no.3
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• pp.191-198
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• 2019

This case report presents a closed hollow bulb obturator made by 3D printing for a maxillectomy patient. Final impression was taken according to the instructions and impression trays provided by the Magic $denture^{TM}$ system. Vertical dimension, facial appearance, and retention had been checked with the try-in denture. The try-in denture was corrected and adjusted to fulfill the demand of the patients, then these were reflected to the final design of the denture. The defect area was designed as a closed hollow bulb shape to reduce the weight and to provide uniform thickness of the denture. The patient satisfied with the esthetics and function of the denture.

• Journal of Powder Materials
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• v.31 no.2
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• pp.137-145
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• 2024

In order to predict the process window of laser powder bed fusion (LPBF) for printing metallic components, the calculation of volumetric energy density (VED) has been widely calculated for controlling process parameters. However, because it is assumed that the process parameters contribute equally to heat input, the VED still has limitation for predicting the process window of LPBF-processed materials. In this study, an explainable machine learning (xML) approach was adopted to predict and understand the contribution of each process parameter to defect evolution in Ti alloys in the LPBF process. Various ML models were trained, and the Shapley additive explanation method was adopted to quantify the importance of each process parameter. This study can offer effective guidelines for fine-tuning process parameters to fabricate high-quality products using LPBF.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2017.05a
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• pp.872-876
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• 2017

Laser Based 3D Printing is an recently advance manufacturing technology for making complex shape comopnent such as automobile and aerospace. So in this article, stainless steel 316L was manufactured by Selective Laser Melting (SLM) and Laser Melting Deposition (LMD) method. SLM is an additive manufacturing process that allow for the manufacture of small and complex component by laser melting and solidification of powder in bed using a high intensity laser beam. The results showed that the laser scanning speed and laser power affects the defect, microstructure and the hardness of the components.

• 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.

• The Journal of the Korea Contents Association
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• v.18 no.7
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• pp.628-634
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• 2018

In an examination of the temporomandibular joint disc, MRI(Magnetic Resonance Imaging) is a useful method, and it is necessary to conduct an examination with one's mouth open for a long time to observe the accurate position change of the disc. Thus, this study would produce a TMJ device, using the 3-D printing technology, which would maintain the state of opening the mouth and would evaluate its usefulness as compared to the existing fixed device. As compared to the image using the existing TMJ device, the image taken with the self-produced TMJ device with a 3-D printer showed a somewhat lower SNR, but there was no defect for a clinical use. It is judged that benefits to costs would increase, since it can be customized for the individual patient and can contribute to the production of similar tools by utilizing the 3-D printing technology.

• Journal of Korea Multimedia Society
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• v.23 no.8
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• pp.903-914
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• 2020

In this paper, we present a novel algorithm to generate a 3D model of patient-specific orbital implant, which is finally produced by the 3D printer. Given CT (computed tomography) scan data of the defective orbital wall or floor, we compose the depth image of the defect site by using the depth buffering, which is a computer graphics technology. From the depth image, we compute the 3D surface which fills the broken part by interpolating the points around the broken part. By thickening the 3D surface, we get the 3D volume mesh of the orbital implant. Our algorithm generates the patient-specific orbital implant whose shape is accurately coincident to the broken part of the orbit. It provides the significant time efficiency for manufacturing the implant with supporting high user convenience.