• Journal of the Korean Society of Manufacturing Process Engineers
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• v.18 no.9
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• pp.72-80
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• 2019

In this task, the tool dynamometer design and manufacture, and the Ansys S/W structural analysis program for tool attachment that satisfies the cutting force measurement requirements of the tool dynamometer system are used to determine the cutting force generated by metal cutting using 3-axis static structural analysis and the LabVIEW system. The cutting power in a cutting process using a milling tool for processing metals provides useful information for understanding the processing, optimization, tool status monitoring, and tool design. Thus, various methods of measuring cutting power have been proposed. The device consists of a strain-gauge-based sensor fitted to a new design force sensing element, which is then placed in a force reduction. The force-sensing element is designed as a symmetrical cross beam with four arms of a rectangular parallel line. Furthermore, data duplication is eliminated by the appropriate setting the strain gauge attachment position and the construction of a suitable Wheatstone full-bridge circuit. This device is intended for use with rotating spindles such as milling tools. Verification and machining tests were performed to determine the static and dynamic characteristics of the tool dynamometer. The verification tests were performed by analyzing the difference between strain data measured by weight and that derived by theoretical calculations. Processing test was performed by attaching a tool dynamometer to the MCT to analyze data generated by the measuring equipment during machining. To maintain high productivity and precision, the system monitors and suppresses process disturbances such as chatter vibration, imbalances, overload, collision, forced vibration due to tool failure, and excessive tool wear; additionally, a tool dynamometer with a high signal-to-noise ratio is provided.

• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.259-271
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• 2019

It is important to obtain reasonable predictions of the extent of the damage during maritime accidents such as ship collisions and groundings. Many fracture models based on different mechanical backgrounds have been proposed and can be used to estimate the extent of damage involving ductile fracture. The goal of this study was to compare the damage extents provided by some selected fracture models. Instead of performing a new series of material constant calibration tests, the fracture test results for the ship building steel EH36 obtained by Park et al. (2019) were used which included specimens with different geometries such as central hole, pure shear, and notched tensile specimens. The test results were compared with seven ductile fracture surfaces: Johnson-Cook, Cockcroft-Latham-Oh, Bai-Wierzbicki, Modified Mohr-Coulomb, Lou-Huh, Maximum shear stress, and Hosford-Coulomb. The linear damage accumulation law was applied to consider the effect of the loading path on each fracture surface. The Swift-Voce combined constitutive model was used to accurately define the flow stress in a large strain region. The reliability of these simulations was verified by the good agreement between the axial tension force elongation relations captured from the tests and simulations without fracture assignment. The material constants corresponding to each fracture surface were calibrated using an optimization technique with the minimized object function of the residual sum of errors between the simulated and predicted stress triaxiality and load angle parameter values to fracture initiation. The reliabilities of the calibrated material constants of B-W, MMC, L-H, and HC were the best, whereas there was a high residual sum of errors in the case of the MMS, C-L-O, and J-C models. The most accurate fracture predictions for the fracture specimens were made by the B-W, MMC, L-H, and HC models.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.35 no.3
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• pp.281-291
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• 2022

Global warming is accelerating due to the use of fossil fuels that have been used continuously for centuries. Now, humankind recognizes its seriousness, and is conducting research on searching for eco-friendly and sustainable energy. In the field of solar energy, which is a kind of eco-friendly and sustainable, many studies are being conducted to enhance the output performance of the module. In this study, the output improvement for the shingled module structure was studied. In order to improve the output performance of the module, the thickness of the encapsulant was increased, and the lamination process conditions have been improved accordingly. After that, the crosslinking rate was analyzed, and the suitability of the lamination process conditions was judged using this. In addition, a peeling test was conducted to analyze the correlation between the adhesion of the encapsulant and the output performance of the module. Finally, the optimization for the encapsulant material and the lamination process conditions for high-power shingled modules was established, and accordingly, the market share of high-power shingled modules in the solar module market can be expected to rise.

• Journal of the Society of Naval Architects of Korea
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• v.59 no.2
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• pp.96-108
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• 2022

In this paper, we propose a systematic hull form variation technique which automatically satisfies the displacement constraint and guarantees a high level of fairness. This method is possible through multiple parameter correction curves. The present method is to improve the hull form variation method based on parametric modification function and consists of two sub-categories: SAC variation and section lines modification. For SAC variation, the utilization of two B-Spline curves satisfying GC¹ condition led to the satisfaction of displacement constraint and high level of fairness at the same time. Section lines modification methods involves in using two fuctions: the first is the waterplane modification function combining two cubic splines. the other function is the sectional area modification function consisting of 2nd order polynomial over the DLWL(Design Load Waterline) and 3rd order polynomial below the DLWL, This function enables not only the fundamental U-V section shape variation but also systematically modified section lines. The present method is expected to be more useful in the hull form optimization process using CFD compared to the existing method.

• Structural Engineering and Mechanics
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• v.81 no.3
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• pp.293-303
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• 2022

As infrastructure ages and traffic load increases, serious public concerns have arisen for the well-being of bridges. The current health monitoring practice focuses on large-scale bridges rather than short span bridges. However, it is critical that more attention should be given to these behind-the-scene bridges. The relevant information about the construction methods and as-built properties are most likely missing. Additionally, since the condition of a bridge has unavoidably changed during service, due to weathering and deterioration, the material properties and boundary conditions would also have changed since its construction. Therefore, it is not appropriate to continue using the design values of the bridge parameters when undertaking any analysis to evaluate bridge performance. It is imperative to update the model, using finite element (FE) analysis to reflect the current structural condition. In this study, a FE model is established to simulate a concrete culvert bridge in New South Wales, Australia. That model, however, contains a number of parameter uncertainties that would compromise the accuracy of analytical results. The model is therefore updated with a neural network (NN) optimisation algorithm incorporating Monte Carlo (MC) simulation to minimise the uncertainties in parameters. The modal frequency and strain responses produced by the updated FE model are compared with the frequency and strain values on-site measured by sensors. The outcome indicates that the NN model updating incorporating MC simulation is a feasible and robust optimisation method for updating numerical models so as to minimise the difference between numerical models and their real-world counterparts.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.9
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• pp.1382-1391
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• 2022

White-box cryptography can respond to white-box attacks that can access and modify memory by safely hiding keys in the lookup table. However, because the size of lookup tables is large and the speed of encryption is slow, it is difficult to apply them to devices that require real-time while having limited resources, such as IoT(Internet of Things) devices. In this work, we propose a scheme for collecting short-length plaintexts and processing them at once, utilizing the characteristics that white-box ciphers process encryption on a lookup table size basis. As a result of comparing the proposed method, assuming that the table sizes of the Chow and XiaoLai schemes were 720KB(Kilobytes) and 18,000KB, respectively, memory usage reduced by about 29.9% and 1.24% on average in the Chow and XiaoLai schemes. The latency was decreased by about 3.36% and about 2.6% on average in the Chow and XiaoLai schemes, respectively, at a Traffic Load Rate of 15 Mbps(Mega bit per second) or higher.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4134-4145
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• 2022

The collimator is one of the high-heat-flux components used to avoid a series of vacuum and thermal problems. In this paper, the heat load distribution throughout the collimator is first calculated through experimental data, and a transient thermodynamic simulation analysis of the original model is carried out. The error of the pipe outlet temperature between the simulated and experimental values is 1.632%, indicating that the simulation result is reliable. Second, the model is optimized to improve the heat transfer performance of the collimator, including the contact mode between the pipe and the flange, the pipe material and the addition of a twisted tape in the pipe. It is concluded that the convective heat transfer coefficient of the optimized model is increased by 15.381% and the maximum wall temperature is reduced by 16.415%; thus, the heat transfer capacity of the optimized model is effectively improved. Third, to adapt the long-pulse steady-state operation of the experimental advanced superconducting Tokamak (EAST) in the future, steady-state simulations of the original and optimized collimators are carried out. The results show that the maximum temperature of the optimized model is reduced by 37.864% compared with that of the original model. The optimized model was changed as little as possible to obtain a better heat exchange structure on the premise of ensuring the consumption of the same mass flow rate of water so that the collimator can adapt to operational environments with higher heat fluxes and long pulses in the future. These research methods also provide a reference for the future design of components under high-energy and long-pulse operational conditions.

• Nuclear Engineering and Technology
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• v.55 no.7
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• pp.2621-2627
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• 2023

In Experimental Advanced Superconducting Tokamak (EAST) experiments, to achieve long pulse and high-power ICRH system operation, a new kind of ICRH antenna has been designed. One of the most critical factors in limiting the operation of long pulse and high power is the intense heat load in the front face of the ICRH antenna, especially the Faraday Screen (FS). Therefore, the cooling channels of FS need to be designed. According to thermal-hydraulic analysis, the FS tubes are divided into several groups to achieve more excellent water cooling capability. The number of series and parallel tubes in one group is chosen as six. This antenna went into service in the spring of 2021, and it is delightful that the temperature distribution of the FS tube is below 400 ℃ in 14.5 s and 1.8 MW ICRH system operation. However, the active water-cooling design was not carried out on the upper and lower plates of FS, which led to severe ablations on that region under long pulse and high power operation, and the temperature is up to 800. Therefore, the upper and lower side plates of the FS were designed with water cooling based on thermal-hydraulic analysis. During the 2022 winter experiments, the temperature of ICRH antenna FS was lower than 400 in the pulse of 200s and the power of 1 MW operation.

• Journal of the Korea Society of Computer and Information
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• v.28 no.4
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• pp.113-120
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• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.