• The Journal of Korean Academy of Prosthodontics
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• v.62 no.2
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• pp.131-139
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• 2024

In a edentulous patient, various methods can be employed for prosthetic treatment using implants, such as implant-supported fixed prostheses, overdentures, hybrid prostheses, and implant assisted removable partial denture. In this case, in a patient with moderate to severe chronic periodontitis requiring full arch extractions, implants were strategically placed using computer-guided surgery. In the maxilla, due to inadequate bone quality and quantity leading to insufficient initial stability, delayed loading was implemented, and interim prosthesis was used during the osseointegration period. In the mandible, stable initial stability was achieved, allowing for immediate loading to reduce patient discomfort. Primary stability is considered the most crucial factor for obtaining immediate loading, so a thorough clinical and radiological evaluation of the remaining alveolar bone quantity and quality must be conducted before surgery.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.663-675
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• 2023

Purpose: Skewness is an indicator used to measure the asymmetry of data distribution. In the past, product quality was judged only by mean and variance, but in modern management and manufacturing environments, various factors and volatility must be considered. Therefore, skewness helps accurately understand the shape of data distribution and identify outliers or problems, and skewness can be utilized from this new perspective. Therefore, we would like to propose a statistical quality control method using skewness. Methods: In order to generate data with the same mean and variance but different skewness, data was generated using normal distribution and gamma distribution. Using Minitab 18, we created 20 sets of 1,000 random data of normal distribution and gamma distribution. Using this data, it was proven that the process state can be sensitively identified by using skewness. Results: As a result of the analysis of this study, if the skewness is within ± 0.2, there is no difference in judgment from management based on the probability of errors that can be made in the management state as discussed in quality control. However, if the skewness exceeds ±0.2, the control chart considering only the standard deviation determines that it is in control, but it can be seen that the data is out of control. Conclusion: By using skewness in process management, the ability to evaluate data quality is improved and the ability to detect abnormal signals is excellent. By using this, process improvement and process non-sub-stitutability issues can be quickly identified and improved.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.34 no.2
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• pp.125-133
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• 2024

Objectives: The aim of this study is to develop a comprehensive Occupational Safety and Health (OSH) guide for maintenance tasks in semiconductor processing, specifically focusing on etching, deposition, and ion implantation processes. Methods: The development of the OSH guide involved a literature review, consultations with industry experts, and field investigations. It concentrates on Maintenance Work (MW) operations in these specialized areas. Results: The result is a detailed OSH guide tailored to MW in etching, deposition, and ion implantation facilities within semiconductor processing. This guide is structured to assist maintenance workers through pre-, during and post-MW phases, ensuring easy comprehension and adherence to safety protocols. It highlights the necessity of safety and health measures throughout the MW process to protect personnel. The guide is enriched with real-life scenarios and visual aids, including cartoons and photographs, to aid in the understanding and implementation of safety and health principles. Conclusions: This OSH guide is designed to enhance the protection of workers engaged in maintenance activities in the electronics sector, particularly in semiconductor manufacturing. It aims to improve compliance with safety and health standards in these high-risk environments.

• Journal of the Korean Institute of Gas
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• v.28 no.2
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• pp.76-83
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• 2024

In the semiconductor manufacturing process, the Diffusion process generates various reactive by-products. These by-products are deposited inside the pipes of post-processing and exhaust treatment systems, posing a potential risk of substantial dust explosions. In this study, three methods material verification, selection of analysis samples, and risk analysis were employed to address the substances produced during the Diffusion process. Among the materials handled in the Diffusion process, ZrO₂, TEOD, and E-DEOS were identified as raw material capable of generating by-product dust. Test for Minimum Ignition Energy and dust explosion were conducted on the by-products collected from each processing facility. The results indicated that, in the case of MIE, none of the by-products ignited. However, the dust explosion test revealed that ZrO₂ exhibited a maximum pressure of 7.6 bar and K_st value of 73.3 bar·m/s, its explosive hazard. Consequently, to mitigate such risks in semiconductor processes, it is excessive buildup.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.4
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• pp.57-64
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• 2024

The introduction of Smart Factories has transformed manufacturing towards more objective and efficient line management. However, most companies are not effectively utilizing the vast amount of sensor data collected every second. This study aims to use this data to predict product quality and manage production processes efficiently. Due to security issues, specific sensor data could not be verified, so semiconductor process-related training data from the "SAMSUNG SDS Brightics AI" site was used. Data preprocessing, including removing missing values, outliers, scaling, and feature elimination, was crucial for optimal sensor data. Oversampling was used to balance the imbalanced training dataset. The SVM (rbf) model achieved high performance (Accuracy: 97.07%, GM: 96.61%), surpassing the MLP model implemented by "SAMSUNG SDS Brightics AI". This research can be applied to various topics, such as predicting component lifecycles and process conditions.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.185-199
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• 2024

Purpose: The improvement of yield and quality in product manufacturing is crucial from the perspective of process management. Controlling key variables within the process is essential for enhancing the quality of the produced items. In this study, we aim to identify key variables influencing product defects and facilitate quality enhancement in CNC machining process using SHAP(SHapley Additive exPlanations) Methods: Firstly, we conduct model training using boosting algorithm-based models such as AdaBoost, GBM, XGBoost, LightGBM, and CatBoost. The CNC machining process data is divided into training data and test data at a ratio 9:1 for model training and test experiments. Subsequently, we select a model with excellent Accuracy and F1-score performance and apply SHAP to extract variables influencing defects in the CNC machining process. Results: By comparing the performances of different models, the selected CatBoost model demonstrated an Accuracy of 97% and an F1-score of 95%. Using Shapley Value, we extract key variables that positively of negatively impact the dependent variable(good/defective product). We identify variables with relatively low importance, suggesting variables that should be prioritized for management. Conclusion: The extraction of key variables using SHAP provides explanatory power distinct from traditional machine learning techniques. This study holds significance in identifying key variables that should be prioritized for management in CNC machining process. It is expected to contribute to enhancing the production quality of the CNC machining process.

• Journal of the Korean Crystal Growth and Crystal Technology
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• v.34 no.4
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• pp.139-142
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• 2024

Along with the use of wide bandgap energy materials such as SiC and GaN in power semiconductors and the development trend of devices, many research results have been reported, including the success of research on AlN single crystals with higher energy gaps and the development of 2-inch single crystal wafers. However, AlN single crystals grown using chemical vapor deposition have been developed into thin films less than a few micrometers thick, but there are almost no results with thicknesses greater than that. Therefore, in this study, we attempted to grow by applying HVPE (Hydride vapor phase epitaxy), one of the chemical vapor deposition methods. The grown AlN single crystal was manufactured using self-designed equipment, and we attempted to establish the conditions for manufacturing AlN single crystals on sapphire wafer. We would like to characterize the growth behavior through an optical microscope observation.

• BioChip Journal
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• v.16
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• pp.82-98
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• 2020

We report a quantitative and systematic method for determining 3D-printing and surface-treatment conditions that can help improve the optical quality of direct-printed microfluidic devices. Digital light processing (DLP)-stereolithography (SLA) printing was extensively studied in microfluidics owing to the rapid, one-step, cleanroom-free, maskless, and high-definition microfabrication of 3D-microfluidic devices. However, optical imaging or detection for bioassays in DLP-SLA-printed microfluidic devices are limited by the translucence of photopolymerized resins. Various approaches, including mechanical abrasions, chemical etching, polymer coatings, and printing on transparent glass/plastic slides, were proposed to address this limitation. However, the effects of these methods have not been analyzed quantitatively or systematically. For the first time, we propose quantitative and methodological determination of 3D-printing and surface-treatment conditions, based on optical-resolution analysis using USAF 1951 resolution test targets and a fluorescence microbead slide through 3D-printed coverslip chips. The key printing parameters (resin type, build orientation, layer thickness, and layer offset) and surface-treatment parameters (grit number for sanding, polishing time with alumina slurry, and type of refractive-index-matching coatings) were determined in a step-wise manner. As a result, we achieved marked improvements in resolution (from 80.6 to 645.1 lp/mm) and contrast (from 3.30 to 27.63% for 645.1 lp/mm resolution). Furthermore, images of the fluorescence microbeads were qualitatively analyzed to evaluate the proposed 3D-printing and surface-treatment approach for fluorescence imaging applications. Finally, the proposed method was validated by fabricating an acoustic micromixer chip and fluorescently visualizing cavitation microstreaming that emanated from an oscillating bubble captured inside the chip. We expect that our approach for enhancing optical quality will be widely used in the rapid manufacturing of 3D-microfluidic chips for optical assays.

• Journal of the Korea Fashion and Costume Design Association
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• v.26 no.3
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• pp.1-12
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• 2024

3D printing technology is a key technology of the Fourth Industrial Revolution and has been gaining attention in various fields, having been selected as one of the top 10 core manufacturing technologies by the U.S. government. In the apparel industry as well, there have been various attempts to develop products using 3D printers. However, compared to other industries and research fields, utilization remains insufficient. This is mainly due to the high price of large 3D printers and a limited varieties of filaments, making it difficult to implement traditional textiles and produce full-size garments. In this study, to develop 3D printed textiles, textile structures that can be 3D printed were categorized. Applying various types of filaments and layering methods allowed for the printing and evaluation of structures, ultimately leading to the selection of three types of 3D printed textile structures suitable for use as clothing materials. Subsequently, types of filaments were selected that match the chosen textile structures and suitabel designs were applied to develop 3D printed clothing designs. As a result of this study, an ideal form for 3D printing textiles was proposed and mehods were presented for clothing construction using practical (versatile) 3D printing technology. This study plays a significant role in contributing to the expansion of research areas related to 3D printing technology in the fashion field and suggesting effective research directions.