• Journal of the Korean Society for Precision Engineering
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• v.30 no.8
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• pp.870-877
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• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.25 no.5
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• pp.313-320
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• 2016

The need for high-strength, multi-axis, and multi-functional machine tools has recently increased because of part complexity and workpiece strength. However, most of the machine tool manufacturers rely on experience for a detailed design because of the shortcomings in the existing design technology. This study uses a topology optimization method to more effectively design a large multi-functional machine tool considering static stiffness. The ram, saddle, and column parts are important structures in a machine tool. Hence, they are selected for the finite element method analysis. Based on this analysis, the optimized internal rib structure for those parts is designed for desirable rigidity and weight. This structure could possibly provide the required design technology for machine tool manufacturers.

• Journal of the Korean Society of Industry Convergence
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• v.11 no.3
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• pp.121-125
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• 2008

Commutator which plays the major role in switching electric currents from AC to DC is composed of copper and molding compound. The longevity of the DC motors are mostly hampered by the improper machining of the parts. Smooth surface will be mandatory to create the proper air gap of the commutator. In this thesis the selection of the proper materials and tools, the design and analysis of machine structure and the final test procedures have been investigated to achieve the smooth cut surface of the commutators. The performance and the product of the newly manufactured machine has been compared with those of the existing one. And the test result shows the new sawing machine has better overall efficiency and durability.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.4
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• pp.44-49
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• 2014

Roll forming technology has a problem in that it depends only on experience without accurate data in the actual field. To solve this problem, it is necessary to procure accurate data during the roll forming process. To this end, we determined the operating force and the material thickness by implementing several changes to those variables during an experiment. This study compares the FEA results and experimental results. Experimental results were used for the basic data of the design. The FEA results show that the roll forming machine is operating accurately and safely. And, a comparison of the results shows that the design of the automatic roll forming machine is operating in the right way. This design of an automatic roll forming machine will be helpful for many areas of the industry.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.5
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• pp.486-492
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• 2013

Energy cost has been increasing rapidly to comply with environmental regulations worldwide and the manufacturing industry who consumes more than half of the total energy needs to improve their cost competitiveness considering environmental costs. Machine tools are essential elements in manufacturing industry and efforts have been made recently to increase their energy efficiency mainly by German and Japanese machine tool builders. In this paper, trends in energy saving technology are described on the hardware and software sides of peripheral equipment of machine tools. In addition, the power consumption of a machining center is measured and analyzed to develop a software-based standby strategy for energy saving with peripheral equipment of machine tools.

• Journal of the Korean institute of surface engineering
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• v.56 no.6
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• pp.353-370
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• 2023

Corrosion detection and analysis is a very important topic in reducing costs and preventing disasters. Recently, image processing techniques have been widely applied to corrosion identification and analysis. In this work, we briefly introduces traditional image processing techniques and machine learning algorithms applied to detect or analyze corrosion in various fields. Recently, machine learning, especially CNN-based algorithms, have been widely applied to corrosion detection. Additionally, research on applying machine learning to region segmentation is very actively underway. The corrosion is reddish and brown in color and has a very irregular shape, so a combination of techniques that consider color and texture, various mathematical techniques, and machine learning algorithms are used to detect and analyze corrosion. We present examples of the application of traditional image processing techniques and machine learning to corrosion detection and analysis.

• Journal of the Korean Society of Industry Convergence
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• v.14 no.2
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• pp.45-52
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• 2011

Since the most exclusive machines of the modern industries which require the nano precision rates are evolved from the machine tools, the design of the stable machine tool structure is very critical. Exclusive machines for the modern industries such as semiconductor, solar cell and LED have surface machining processes which are similar to the face cutting and grinding of conventional machine tools. This study was initiated to stabilize a milling machine structure and further to help design those exclusive machines which have similar machining mechanisms. The vibrations inherent to the machine tool structures hurt the precision machining as well as damage the longevity of the structures. There have been numerous researches in order to suppress the vibrations of machine tool structures using the extra modules such as actuators and dampers. In this paper, the dynamic properties are analyzed to obtain the natural frequencies and mode shapes of a machine tool structure which reflect the main reasons of the biggest vibrations under the given operating conditions. And the feasibility of improving the stability of the structure without using any additional apparatus has been investigated with minor design changes. The result of the study shows that simple changes based on proper system identification can considerably improve the stability of the machine tool structure.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.22 no.5
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• pp.824-830
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• 2013

In ultra-precision machining, the inside temperature should be controlled precisely. The important factors are environmental conditions (outside temperature, humidity) and temperature conditions (cutting heat, spindle heat). Thus, in this study, an environmental control cell for the ultra-precision machine that could control the inside temperature and minimize effects of the surrounding environment to achieve a thermal deformation of less than 400nm of a specimen was designed and verified through C.F.D. Further, a method that could control the temperature precisely by using a blower, heat exchanger and heater was evaluated. As a result, this study established a C.F.D technic for the environmental control cell, and the specimen temperature was controlled to be within $17.465{\pm}0.055^{\circ}C$.

• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.1796-1809
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• 2021

The subchannel of a research reactor used to generate high power density is designed to be narrow and rectangular and comprises plate-type fuels operating under downward flow conditions. Critical heat flux (CHF) is a crucial parameter for estimating the safety of a nuclear fuel; hence, this parameter should be accurately predicted. Here, machine learning is applied for the prediction of CHF in a narrow rectangular channel. Although machine learning can effectively analyze large amounts of complex data, its application to CHF, particularly for narrow rectangular channels, remains challenging because of the limited flow conditions available in existing experimental databases. To resolve this problem, we used four CHF correlations to generate pseudo-data for training an artificial neural network. We also propose a network architecture that includes pre-training and prediction stages to predict and analyze the CHF. The trained neural network predicted the CHF with an average error of 3.65% and a root-mean-square error of 17.17% for the test pseudo-data; the respective errors of 0.9% and 26.4% for the experimental data were not considered during training. Finally, machine learning was applied to quantitatively investigate the parametric effect on the CHF in narrow rectangular channels under downward flow conditions.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2297-2310
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• 2022

A condensation heat transfer model is essential to accurately predict the performance of the passive containment cooling system (PCCS) during an accident in an advanced light water reactor. However, most of existing models tend to predict condensation heat transfer very well for a specific range of thermal-hydraulic conditions. In this study, a new correlation for condensation heat transfer coefficient (HTC) is presented using machine learning technique. To secure sufficient training data, a large number of pseudo data were produced by using ten existing condensation models. Then, a neural network model was developed, consisting of a fully connected layer and a convolutional neural network (CNN) algorithm, DenseNet. Based on the hold-out cross-validation, the neural network was trained and validated against the pseudo data. Thereafter, it was evaluated using the experimental data, which were not used for training. The machine learning model predicted better results than the existing models. It was also confirmed through a parametric study that the machine learning model presents continuous and physical HTCs for various thermal-hydraulic conditions. By reflecting the effects of individual variables obtained from the parametric analysis, a new correlation was proposed. It yielded better results for almost all experimental conditions than the ten existing models.