• Korean Journal of Materials Research
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• v.28 no.11
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• pp.663-670
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• 2018

H13 tool steels are widely used as metallic mold materials due to their high hardness and thermal stability. Recently, many studies are undertaken to satisfy the demands for manufacturing the complex shape of the mold using a 3D printing technique. It is reported that the mechanical properties of 3D printed materials are lower than those of commercial forged alloys owing to micropores. In this study, we investigate the effect of microstructures and defects on mechanical properties in the 3D printed H13 tool steels. H13 tool steel is fabricated using a selective laser melting(SLM) process with a scan speed of 200 mm/s and a layer thickness of $25{\mu}m$. Microstructures are observed and porosities are measured by optical and scanning electron microscopy in the X-, Y-, and Z-directions with various the build heights. Tiny keyhole type pores are observed with a porosity of 0.4 %, which shows the lowest porosity in the center region. The measured Vickers hardness is around 550 HV and the yield and tensile strength are 1400 and 1700 MPa, respectively. The tensile properties are predicted using two empirical equations through the measured values of the Vickers hardness. The prediction of tensile strength has high accuracy with the experimental data of the 3D printed H13 tool steel. The effects of porosities and unmelted powders on mechanical properties are also elucidated by the metallic fractography analysis to understand tensile and fracture behavior.

• Journal of the Korea Convergence Society
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• v.10 no.3
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• pp.1-6
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• 2019

In modern society, lifestyle and individuality are important, and personalized lifestyle and patterns are emerging. The number of people with articulation diseases is increasing due to wrong living habits. In addition, as the number of households increases, there is a case where emergency care is not received at the appropriate time. We need information that can be managed by ourselves through accurate analysis according to the individual's condition for health and disease management, and care appropriate to the emergency situation. It is effectively used for classification and prediction of data using CNN in deep learning. CNN differs in accuracy and processing time according to the data features. Therefore, it is necessary to improve processing speed and accuracy for real-time healthcare. In this paper, we propose motion monitoring using Mask R-CNN for articulation disease management. The proposed method uses Mask R-CNN which is superior in accuracy and processing time than CNN. After the user's motion is learned in the neural network, if the user's motion is different from the learned data, the control method can be fed back to the user, the emergency situation can be informed to the guardian, and appropriate methods can be taken according to the situation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.1-6
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• 2019

The objective of this study was to investigate performance characteristics of thermal management system(TMS) in a fuel cell electric vehicle with 100kW Fuel Cell(FC) system. In order to build up analytic modelling for TMS, each component was installed and tested under various operating conditions, such as water pump, radiator, 3-Way valve, COD heater, and FC stack etc. and as the results of them, correlations reflecting component's characteristics with flow rate, air velocity were developed. Developed analytic modelling was carried out under various operating conditions on the road. To verify modelling's accuracy, after prediction for optimum coolant flow rate was fulfilled under certain operating conditions, such as FC system, water pump speed, opening of 3-way valve, and pipe resistance, analytic and experimental values were compared and good agreement was shown. In order to predict cold-start operating performance for analytic modelling, coolant temperature variation was analyzed with $-20^{\circ}C$ ambient temperature and duration was predicted to rise in optimum temperature for FC. Because there is appropriate temperature difference between inlet and outlet of FC stack to operate FC system properly, related analysis was performed with respect to power consumption for TMS and heat rejection rate and performance map was depicted along with FC operating conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.2
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• pp.7-13
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• 2019

This study aims to develop a FE Model to simulate dissimilar friction stir welding and to address its potential for fundamental analysis and practical applications. The FE model is based on Coupled Eulerian-Lagrangian approach. Multiphysics systems are calculated using explicit time integration algorithm, and heat generations by friction and inelastic heat conversion as well as heat transfer through the bottom surface are included. Using the developed model, friction stir welding between an Al6061T6 plate and an AZ61 plate were simulated. Three simulations are carried out varying the welding parameters. The model is capable of predicting the temperature and plastic strain fields and the distribution of void. The simulation results showed that temperature was generally greater in Mg plates and that, as a rotation speed increase, not the maximum temperature of Mg plate increased, but did the temperature of Al plate. In addition, the model could predict flash defects, however, the prediction of void near the welding tool was not satisfactory. Since the model includes the complex physics closely occurring during FSW, the model possibly analyze a lot of phenomena hard to discovered by experiments. However, practical applications may be limited due to huge simulation time.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.930-938
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• 2018

Underwater noise radiated from submarines is directly related to the probability of being detected by the sonar of an enemy vessel. Therefore, minimizing the noise of a submarine is essential for improving survival outcomes. For modern submarines, as the speed and size of a submarine increase and noise reduction technology is developed, interest in flow noise around the hull has been increasing. In this study, a noise analysis technique was developed to predict flow noise generated around a submarine shape considering the free surface effect. When a submarine is operated near a free surface, turbulence-induced noise due to the turbulence of the flow and bubble noise from breaking waves arise. First, to analyze the flow around a submarine, VOF-based incompressible two-phase flow analysis was performed to derive flow field data and the shape of the free surface around the submarine. Turbulence-induced noise was analyzed by applying permeable FW-H, which is an acoustic analogy technique. Bubble noise was derived through a noise model for breaking waves based on the turbulent kinetic energy distribution results obtained from the CFD results. The analysis method developed was verified by comparison with experimental results for a submarine model measured in a Large Cavitation Tunnel (LCT).

• The Journal of Bigdata
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• v.3 no.2
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• pp.19-33
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• 2018

Owing to the increase of FTA, food trade, and versatile preferences of consumers, food import has increased at tremendous rate every year. While the inspection check of imported food accounts for about 20% of the total food import, the budget and manpower necessary for the government's import inspection control is reaching its limit. The sudden import food accidents can cause enormous social and economic losses. Therefore, predictive system to forecast the compliance of food import with its preemptive measures will greatly improve the efficiency and effectiveness of import safety control management. There has already been a huge data accumulated from the past. The processed foods account for 75% of the total food import in the import food sector. The analysis of big data and the application of analytical techniques are also used to extract meaningful information from a large amount of data. Unfortunately, not many studies have been done regarding analyzing the import food and its implication with understanding the big data of food import. In this context, this study applied a variety of classification algorithms in the field of machine learning and suggested a data preprocessing method through the generation of new derivative variables to improve the accuracy of the model. In addition, the present study compared the performance of the predictive classification algorithms with the general base classifier. The Gaussian Naïve Bayes prediction model among various base classifiers showed the best performance to detect and predict the nonconformity of imported food. In the future, it is expected that the application of the abnormality detection model using the Gaussian Naïve Bayes. The predictive model will reduce the burdens of the inspection of import food and increase the non-conformity rate, which will have a great effect on the efficiency of the food import safety control and the speed of import customs clearance.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.84-89
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• 2019

This is a pilot study of machine learning based structural health monitoring system using flight data of composite aircraft. In this study, the most suitable machine learning algorithm for structural health monitoring was selected and dimensionality reduction method for application on the actual flight data was conducted. For these tasks, impact test on the cantilever beam with added mass, which is the simulation of damage in the aircraft wing structure was conducted and classification model for damage states (damage location and level) was trained. Through vibration test of cantilever beam with fiber bragg grating (FBG) sensor, data of normal and 12 damaged states were acquired, and the most suitable algorithm was selected through comparison between algorithms like tree, discriminant, support vector machine (SVM), kNN, ensemble. Besides, through neighborhood component analysis (NCA) feature selection, dimensionality reduction which is necessary to deal with high dimensional flight data was conducted. As a result, quadratic SVMs performed best with 98.7% for without NCA and 95.9% for with NCA. It is also shown that the application of NCA improved prediction speed, training time, and model memory.

• Journal of radiological science and technology
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• v.44 no.4
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• pp.359-365
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• 2021

A heavy particle accelerator is a device that accelerates particles using high energy and is used in various fields such as medical and industrial fields as well as research. However, secondary neutrons and particle fragments are generated by the high-energy particle beam, and among them, the neutrons do not have an electric charge and directly interact with the nucleus to cause radiation of the material. Quantitative evaluation of the radioactive material produced in this way is necessary, but there are many difficulties in actual measurement during or after operation. Therefore, this study compared and evaluated the generated radioactive material in the concrete shield for protons and carbon ions of specific energy by using the simulation code FLUKA. For the evaluation of each energy of proton beam and carbon ion, the reliability of the source term was secured within 2% of the relative error with the data of the NASA Space Radiation Laboratory(NSRL), which is an internationally standardized data. In the evaluation, carbon ions exhibited higher neutron flux than protons. Afterwards, in the evaluation of radioactive materials under actual operating conditions for disposal, a large amount of short-lived beta-decay nuclides occurred immediately after the operation was terminated, and in the case of protons with a high beam speed, more radioactive products were generated than carbon ions. At this time, radionuclides of ⁴⁴Sc, ³H and ²²Na were observed at a high rate. In addition, as the cooling time elapsed, the ratio of long-lived nuclides increased. For nonparticulate radionuclides, ³H, ²²Na, and for particulate radionuclides, ⁴⁴Ti, ⁵⁵Fe, ⁶⁰Co, ¹⁵²Eu, and ¹⁵⁴Eu nuclides showed a high ratio. In this study, it is judged that it is possible to use the particle accelerator as basic data for facility maintenance, repair and dismantling through the prediction of radioactive materials in concrete according to the cooling time after operation and termination of operation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.5
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• pp.15-21
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• 2021

Electrically powered airplanes can reduce CO₂ emissions from fossil fuel use and reduce airplane costs in the long run through efficient energy use. For this reason, advanced aviation countries such as the United States and the European Union are leading the development of innovative technologies to implement the full-electric airplane in the future. Currently, the research and development to convert existing two-seater engine airplanes to electric-powered airplanes are underway domestically. The airplane converted to electric propulsion is the KLA-100, which aims to carry out a 30-minute flight test with a battery pack installed using the engine mounting space and copilot space. The lithium-ion battery installed on the airplane converted to electric propulsion was designed with a specific power of 150Wh/kg, weight of 200kg, and a C-rate 3~4. This study confirmed the possibility of a 30-minute flight with a designed battery pack before conducting a flight test of a modified electrically propelled airplane. The battery performance was verified by dividing the 30-minute flight profile into start/run stage, take-off stage, climbing stage, cruise stage, descending stage, and landing/run stage. The final target of the 30-minute flight was evaluated by calculating the battery capacity required for each stage. Furthermore, the flight performance of the electrically propelled airplane was determined by calculating the flight availability time and navigation distance according to the flight speed.

• Korean Chemical Engineering Research
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• v.62 no.1
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• pp.27-35
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• 2024

Fuel gases such as methane and propane are used in explosion hazardous area of domestic plants and can form non-uniform mixtures with the influence of process conditions due to leakage. The fire-explosion risk assessment using literature data measured under uniform mixtures, damage prediction can be obtained the different results from actual explosion accidents by gas leaks. An explosion characteristics such as explosion pressure and flame velocity of non-uniform gas mixtures with concentration change similar to that of facility leak were examined. The experiments were conducted in a closed 0.82 m long stainless steel duct with observation recorded by color high speed camera and piezo pressure sensor. Also we proposed the quantification method of non-uniform mixtures from a regression analysis model on the change of concentration difference with time in explosion duct. For the non-uniform condition of this study, the area of flame surface enlarged with increasing the concentration non-uniform in the flame propagation of methane and was similar to the wrinkled flame structure existing in a turbulent flame. The time to peak pressure of methane decreased as the non-uniform increased and the explosion pressure increased with increasing the non-uniform. The ranges of K_G (Deflagration index) of methane with the concentration non-uniform were 1.30 to 1.58 [MPa·m/s] and the increase rate of K_G was 17.7% in methane with changing from uniform to non-uniform.