• Ocean Systems Engineering
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• v.14 no.1
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• pp.53-72
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• 2024

The interaction of the blade of surface-piercing propellers (SPPs) with the water/air surface is a physical phenomenon that is difficult to model mathematically, so that such propellers are usually designed using empirical approaches. In this paper, a newly developed mechanism for measuring the torque of SPPs in an open water circuit is presented. The mechanism includes a single-component load cell and a deformable torque sensor to detect the forces exerted on the propeller. Deformations in the sensor elements lead to changes in the strain gauge resistance, which are converted into voltage using a Wheatstone bridge. The amplified signal is then recorded by a 16-channel data recording system. The mechanism is calibrated using a 6-DoF calibration system and a Box-Behnken design, achieving 99% accuracy through multivariate regression and ANOVA. Finally, the results of performance tests on a 4-blade propeller were presented in the form of changes in the torque coefficient as a function of feed rate. The results show that the new mechanism is 8% more accurate than conventional empirical methods.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.678-683
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• 2008

Moment-based reliability analysis is the method to calculate reliability using Pearson System with first-four raw moments obtained from simulation model. But it is too expensive to calculate first four moments from complicate simulation model. To overcome this drawback the MD(multiplicative decomposition) method which approximates simulation model to kriging metamodel and calculates first four raw moments explicitly with multiplicative decomposition techniques. In general, kriging metamodel is an interpolation model that is decomposed of global model and local model. The global model, in general, can be used as the constant global model, the 1st order global model, or the 2nd order global model. In this paper, the influences of global models on the accuracy and robustness of raw moments are examined and compared. Finally, we suggest the best global model which can provide exact and robust raw moments using MD method.

• Geomechanics and Engineering
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• v.33 no.1
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• pp.1-9
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• 2023

The unconfined compression strength (UCS) of soils is commonly used either before or during the construction of geo-structures. In the pre-design stage, UCS as a mechanical property is obtained through a laboratory test that requires cumbersome procedures and high costs from in-situ sampling and sample preparation. As an alternative way, the empirical model established from limited testing cases is used to economically estimate the UCS. However, many parameters affecting the 1D soil compression response hinder employing the traditional statistical analysis. In this study, gene expression programming (GEP) is adopted to develop a prediction model of UCS with common affecting soil properties. A total of 79 undisturbed soil samples are collected, of which 54 samples are utilized for the generation of a predictive model and 25 samples are used to validate the proposed model. Experimental studies are conducted to measure the unconfined compression strength and basic soil index properties. A performance assessment of the prediction model is carried out using statistical checks including the correlation coefficient (R), the root mean square error (RMSE), the mean absolute error (MAE), the relatively squared error (RSE), and external criteria checks. The prediction model has achieved excellent accuracy with values of R, RMSE, MAE, and RSE of 0.98, 10.01, 7.94, and 0.03, respectively for the training data and 0.92, 19.82, 14.56, and 0.15, respectively for the testing data. From the sensitivity analysis and parametric study, the liquid limit and fine content are found to be the most sensitive parameters whereas the sand content is the least critical parameter.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.25 no.12
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• pp.1960-1965
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• 2001

In the practical design, it is important to extract the design space information of a complex system in order to optimize the design because the design contains huge amount of design conflicts in general. In this research FEA (finite element analysis) has been successfully implemented and integrated with a statistical approach such as DOE (design of experiments) based RSM (response surface model) to optimize the thermal deformation of an automotive disk brake. The DOE is used for exploring the engineer's design space and for building the RSM in order to facilitate the effective solution of multi-objective optimization problems. The RSM is utilized as an efficient means to rapidly model the trade-off among many conflicting goals existed in the FEA applications. To reduce the computational burden associated with the FEA, the second-order regression models are generated to derive the objective functions and constraints. In this approach, the multiple objective functions and constraints represented by RSM are solved using the sequential quadratic programming to archive the optimal design of disk brake.

• Structural Engineering and Mechanics
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• v.73 no.5
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• pp.501-513
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• 2020

In the present work, the optimization of machining parameters to achieve the desired technological parameters such as surface roughness, tool radial vibration and material removal rate have been carried out using response surface methodology (RSM). The hard turning of EN19 alloy steel with coated carbide (GC3015) cutting tools was studied. The main problem faced in manufacturer of hard and high precision components is the selection of optimum combination of cutting parameters for achieving required quality of surface finish with maximum production rate. This problem can be solved by development of mathematical model and execution of experiments by RSM. A face centred central composite design (FCCD), which comes under the RSM approach, with cutting parameters (cutting speed, feed rate and depth of cut) was used for statistical analysis. A second-order regression model were developed to correlate the cutting parameters with surface roughness, tool vibration and material removal rate. Consequently, numerical and graphical optimization were performed to obtain the most appropriate cutting parameters to produce the lowest surface roughness with minimal tool vibration and maximum material removal rate using desirability function approach. Finally, confirmation experiments were performed to verify the pertinence of the developed mathematical models.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.04a
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• pp.73-78
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• 2003

Ultrasonic measurement is one of important technologies in the lift-time maintenance of nuclear poler plant. Ultrasonic inspection system is consisted of the operator, equipment and procedure. The reliability of ultrasonic inspection system is affected by its ability. The performance demonstration round robin was conducted to quantify the capability of ultrasonic inspection for in-service. The small number of teams who employed procedures that met or exceeded ASME Sec. XI Code requirements detected the piping of nuclear power plant with various cracks to evaluate the capability of detection and sizing. In this paper, the statistical reliability assessment of ultrasonic nondestructive inspection data using Monte Carlo simulation is presented. The results of the probability of detection (POD) analysis using Monte Carlo simulation are compared to these of logistic probability model. In these results, Monte Carlo simulation was found to be very useful to the reliability assessment f3r the small hit/miss data sets.

• Architectural research
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• v.8 no.1
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• pp.37-45
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• 2006

A fungal spore transportation model that accounts for the concentration of airborne indoor spores and the amount of spores deposited on interior surfaces has been developed by extending the current aerosol model. This model is intended to be used for a building with a mechanical ventilation system, and considers HVAC filter efficiency and ventilation rate. The model also includes a surface-cleaning efficiency and frequency that removes a portion of spores deposited on surfaces. The developed model predicts indoor fungal spore concentration and provides an indoor/outdoor ratio that may increase or decrease mold growth risks in real, in-use building cases. To get a more useful outcome from the model simulation, an uncertainty analysis has been conducted in a real building case. By including uncertainties associated with the parameters in the spore transportation model, the simulation results provide probable ranges of indoor concentration and indoor/outdoor ratio. This paper describes the uncertainty quantification of each parameter that is specific to fungal spores, and uncertainty propagation using an appropriate statistical technique. The outcome of the uncertainty analysis showed an agreement with the results from the field measurement with air sampling in a real building.

• Proceedings of the Acoustical Society of Korea Conference
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• autumn
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• pp.211-214
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• 2004

이륙과 음속 통과시 랜덤진동형태의 음향/진동환경에 노출되는 위성체의 음향/진동시험은 시제품을 완성한 후에 슨1행되므로 않은 시행착오를 겪거나, 과다한 안전계수를 사용하여 불필요한 무게증가 등의 문제점을 가지고 있다. 이러한 문제점을 극복하기 위하여 통계적 에너지 해석법 (Statistical Energy Analysis)을 이용한 선행 해석이 필요하다. 본 연구에서는 KOMPSAT-1 (Korea Multi-Purpose Satellite-1) 위성체의 SDM (Structural Dynamic Model)에 대하여 SEA 해석을 수행하였다. 감쇠 손실 인자 (Damping Loss Ffactor)는 단판을 분리하여, 연성 손실 인자(Coupling Loss Factor)는 SDM모델 하부의 두 샌드위치 패널을 분리하여 실험적으로 산정하였다.

• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.9
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• pp.2257-2264
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• 1994

A method to evaluate the position performance for a stochastically defined planar robot manipulator is presented. Performance is defined as the operational reliability based upon the positional errors of the manipulator tip. An analytical method is developed and applied to a two-link robot manipulator through forward kinematics. This study includes uncertainties in the link length, pin center location and radial clearance. By virtue of the effective link length model, only the nominal manipulator model and statistical information on the uncertainties are required. The results from the analytical method is compared to those from the Monte Carlo simulation.

• Journal of Practical Engineering Education
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• v.15 no.2
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• pp.419-427
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• 2023

In this study, optimal adhesion conditions to alleviate defects caused by heat shrinkage with FDM type 3D printers with machine learning are researched. Machine learning is one of the "statistical methods of extracting the law from data" and can be classified as supervised learning, unsupervised learning and reinforcement learning. Among them, a function model for adhesion between the bed and the output is presented using supervised learning specialized for optimization, which can be expected to reduce output defects with FDM type 3D printers by deriving conditions for optimum adhesion between the bed and the output. Machine learning codes prepared using Python generate a function model that predicts the effect of operating variables on adhesion using data obtained through adhesion testing. The adhesion prediction data and verification data have been shown to be very consistent, and the potential of this method is explained by conclusions.