• 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 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 Korea Technical Association of The Pulp and Paper Industry
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• v.41 no.1
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• pp.52-60
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• 2009

A printing defect known as fluting (or waviness) of the web printed by heatset web offset printing process is one of the chronically serious problems deteriorating print quality. In this paper, fluting occurrence on uncoated papers was explored in terms of many printing conditions including drying temperature, fountain solution amount, ink supply, and press configurations. For this purpose, fluting on prints from real press runs was appraised in a quantitative manner. As results, ink supply was a distinctive factor for fluting such that the lower ink amount, the milder fluting. However increase in fountain solution seemed to make fluting severer while the effect of drying temperature was inconsistent for each paper. This result might indicate variable drying requirements for each paper. Thereby it was suggested that the optimum drying conditions related to the printabilities of each paper need to be established to minimize fluting potential. A press with short dryer and drastic cooling unit produced higher fluting. Suggestions for future work were given along with interpretation for the results.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.9
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• pp.894-898
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• 2009

In a post printing section of roll to roll printing systems, scratch problem is the major defects. The functional qualities such as conductivity, mobility could deteriorate because of the scratch defect. In general, the scratch of the printed pattern on the flexible substrate was induced from a contact between rolls and printed pattern in the post printing section. In this paper, for non-contacting transfer of a moving web, a mathematical tension model has been developed considering strain due to air floatation and the proposed mode has been validated by numerical simulation. Additionally, the correlation between floatation height and speed compensation to control the tension and register are investigated. On the basis of the proposed model, a guide line of speed control in R2R printing system is presented to guarantee the non-contact between rolls and R2R printed pattern on the flexible substrate.

• 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.