• Korean Journal of Metals and Materials
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• v.47 no.10
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• pp.605-612
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• 2009

The suitability of twin roll strip casting for Ag-27.5%Cu-20.5%Zn-2.5%Mn-0.5%Ni brazing alloy (known as HS-49D) was examined in the present work and the mechanical properties and microstructure of the strip were also investigated. The effect of annealing heat treatment on the properties was also studied. The new manufacturing process has applications in the production of the brazing alloy. XRD and microstructural analyses of the Ag-27.5%Cu-20.5%Zn-2.5%Mn-0.5%Ni strip revealed a eutectic microstructure of an Ag-rich matrix (FCC) and a Cu-rich phase (FCC) regardless of heat treatment. The results of mechanical tests showed tensile strength of 434 MPa and 80% elongation for the twin roll casted strip. Tensile results showed decreasing strengths and increasing elongation with annealing heat treatment. Microstructural evolution and fractography were also investigated and related to the mechanical properties.

• Analytical Science and Technology
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• v.34 no.3
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• pp.128-133
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• 2021

During titration, several chemical reactions result in changes not only in the potential of chemicals, but also in the colors of the indicator. In a potentiometric titration, a titration curve is obtained by measuring the abrupt change in the potential at the endpoint. Generally, acid-base titration is performed by observing the color change caused by an indicator to determine the endpoint. The method of determining the endpoint by measuring the potential difference has been well established and commercialized; however, the devices that can obtain the endpoint by observing the color change are limited. Consequently, in this study, a simple and precise spectral endpoint detector was manufactured using a drop-counter comprising an infrared emitter and a phototransistor, a white light LED as the light source and photodetector, and an analog-to-digital converter (Arduino). Spectrator, a new named, showed excellent results in terms of the reproducibility of acid-base titration using thymol blue as an indicator. Herein, we present the results of the Spectrator-manufacturing process as well as the experimental results.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.749-756
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• 2021

Thanks to PLC and IoT Sensor, huge amounts of data has been accumulated onto the companies' databases. Machine Learning Algorithms for the predictive model with good performance have been widely utilized in the manufacturing process. We present how to improve the performance of machine learning predictive models. To improve the performance of the predictive model, typical techniques such as increasing the sample size, optimizing the hyper parameters for the algorithm, and selecting a proper machine learning algorithm for the predictive model would be shown. We suggest some new ways to make the model performance much better. With the proposed methods, we can build a better predictive model for predicting and controlling product qualities and save incredibly large amount of quality failure cost.

• Journal of Powder Materials
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• v.28 no.6
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• pp.470-477
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• 2021

Energy storage systems should address issues such as power fluctuations and rapid charge-discharge; to meet this requirement, CoFe₂O₄ (CFO) spinel nanoparticles with a suitable electrical conductivity and various redox states are synthesized and used as electrode materials for supercapacitors. In particular, CFO electrodes combined with carbon nanofibers (CNFs) can provide long-term cycling stability by fabricating binder-free three-dimensional electrodes. In this study, CFO-decorated CNFs are prepared by electrospinning and a low-cost hydrothermal method. The effects of heat treatment, such as the activation of CNFs (ACNFs) and calcination of CFO-decorated CNFs (C-CFO/ACNFs), are investigated. The C-CFO/ACNF electrode exhibits a high specific capacitance of 142.9 F/g at a scan rate of 5 mV/s and superior rate capability of 77.6% capacitance retention at a high scan rate of 500 mV/s. This electrode also achieves the lowest charge transfer resistance of 0.0063 Ω and excellent cycling stability (93.5% retention after 5,000 cycles) because of the improved ion conductivity by pathway formation and structural stability. The results of our work are expected to open a new route for manufacturing hybrid capacitor electrodes containing the C-CFO/ACNF electrode that can be easily prepared with a low-cost and simple process with enhanced electrochemical performance.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.337-342
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• 2022

Although printing technology has recently been widely used in IT fields including displays and fuel cells, residual and thermal stress are generated by a manufacturing process of stacking the layers of the print head and result in the substrate deformation and nozzle plate crack, which may cause ink leaks or not be ejected onto a desired region. Therefore, in this paper, we propose a new design of thermal inkjet print head with a robust and reliable structure. Diverse types of inkjet print head such as a rib, pillar, support wall and individual feed hole are designed to reduce the deformation of the substrate and nozzle plate, and their feasibility is numerically investigated through FEA analysis. The numerical results show that the maximum stress and deformation of proposed print head dramatically drops to at least 40~50%, and it is confirmed that there is no nozzle plate cracks and ink leakage through the fabrication of pillar and support wall typed print head. Therefore, it is expected that the proposed head shape can be applied not only to ink ejection in the normal direction, but also to large-area printing technology.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.11
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• pp.455-464
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• 2022

There are two unique characteristics of the datasets from a manufacturing process. They are the severe class imbalance and lots of Out-of-Distribution samples. Some good strategies such as the oversampling over the minority class, and the down-sampling over the majority class, are well known to handle the class imbalance. In addition, SMOTE has been chosen to address the issue recently. But, Out-of-Distribution samples have been studied just with neural networks. It seems to be hardly shown that Out-of-Distribution detection is applied to the predictive model using conventional machine learning algorithms such as SVM, Random Forest and KNN. It is known that conventional machine learning algorithms are much better than neural networks in prediction performance, because neural networks are vulnerable to over-fitting and requires much bigger dataset than conventional machine learning algorithms does. So, we suggests a new approach to utilize Out-of-Distribution detection based on SVM algorithm. In addition to that, bagging technique will be adopted to improve the precision of the model.

• The Journal of the Convergence on Culture Technology
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• v.9 no.5
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• pp.529-534
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• 2023

Although line printing technology is capable of high-speed and large area printing, residual stresses generated during the manufacturing process can deform the feedhole, causing nozzle plate crack or ink leaks. Therefore, in this paper, we propose a new thermal inkjet print head that is robust, reliable and more suitable for line-printing. The amount of deformation of the conventional line printing head measured through the experiment was converted into an equivalent load, and the validity of the load estimation method was verified through FEA analysis. In addition, in order to minimize deformation without increasing the head size, the head structure was designed to increase internal rigidity by reinforcing the unit nozzle with a pillar or support wall or by adding a support beam or dry/wet etched bridge. The FEA analysis results show that the feedhole deformation was reduced by up to 90%, and it is confirmed that the suggested print head with dry etched feedhole bridge operates normally without nozzle plate cracks and ink leakage through fabrication.

• Korea Trade Review
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• v.48 no.2
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• pp.1-26
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• 2023

The purpose of this study is to demonstrate how internal capability adjustments made by subsidiaries in response to local market adaption and market changes, together with the use of internal and external networks, have an impact on the investment performance of such subsidiaries. From the empirical results, it was proven that the larger the extent of internal capability adjustment made by subsidiaries, and the more quickly and flexibly it is implemented, the more positive the investment performance is. The empirical findings also showed that in this process, the use of internal/external network embeddedness has a positive mediating impact on the investment performance. Additionally, the results of statistical analysis support the research hypothesis that external embeddedness has a greater mediating influence on multinational manufacturing companies entering Chinese market than internal embeddedness. It implies to the top managers of subsidiaries that the subsidiary should actively utilize external embeddedness to create a new locational competitive advantage in the local region, as well as develop a strategy to reduce foreignness costs such as cost of adapting to the local system.

• Journal of the Microelectronics and Packaging Society
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• v.30 no.4
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• pp.69-78
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• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.909-918
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• 2010

To analyze the national competitiveness of Korea leads to the conclusion that global high-tech enterprises have been playing leading and pulling roles in making Korea in line with advanced countries even though the country is lacking in various natural resources. The characteristics of these companies above are as follows; Firstly, these enterprises continue to accumulate core technologies and know-how with highly competent human resources and well-organized management. Secondly, they are well structured and equipped with information technology infrastructures which are, for example, ERP, SCM, CRM, and PLM. Among them PLM is considered to be the principal core information technology infra in manufacturing industry. The urgent task of manufacturing industry recently is to develop new products to accept various needs of consumers, and to launch the products in time to market, which requires the manufactures to be equipped with product development infra and system to upgrade product fulfillment and mass production system in a short period. The introduction of PLM System is a solution of core strategy as a manufacturer for collaboration, global development, reengineering of manufacturing system, the innovation and efficiency of manufacturing process, and product quality improvement. The purpose of this study is to analyze the success factors of introducing PLM System and its practicing effectiveness. And the results of empirical study are as follows; (1) Technical success factors positively impact system quality and user satisfaction, (2) Organizational success factors positively impact system quality, but does not impact user satisfaction, (3) Environmental success factors positively impact system quality and user satisfaction, (4) System quality positively impacts user satisfaction, (5) User satisfaction positively impacts the effectiveness of implementing PLM systems, but system quality does not impact it.