• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.11
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• pp.970-977
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• 2008

This paper deals with the dynamic characteristics of the shaft with impeller model which is the most important part in developing the resin mixing machine. Through reverse engineering, it is possible to make the shaft with impeller geometry model which is necessary vibration characteristic analysis by commercial impeller. The natural frequency analysis and structural analysis using finite element analysis software are performed on the imported commercial shaft with impeller model. The most important fundamental natural frequency of the shaft with impeller model is around 14.5 Hz, which well agrees with modal testing. The most effective design variables were extracted by ANOM(analysis of means) and pareto chart. This paper presents approximation 2nd order polynomial as design variables using RSM(response surface methodology). Generally, RSM take 2 or 3 design variables, but this method uses 5 design variables with table of mixed orthogonal array. Further more, the analyzed result of the commercial shaft with impeller is to be utilized for the structural design of resin chock mixing machine.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1057-1058
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• 2011

This paper deals with finding the optimal ratio of height and length of Single-Sided Linear Induction Motors (SLIM) using Finite Element Method (FEM) for magnetic field analysis coupled with optimal design methodology. For effective analysis, FEM is conducted in time harmonic field which provides steady state performance with the fundamental components of voltage and current. The ratio of height to length providing the required output power is obtained by Response Surface Methodology (RSM) and optimal values are presented by the variation in output power. When output power is small, the ratio is high and as the power increases, the ratio shows a converged value. Considering the general application of linear motors, using a small ratio can be limiting, however, the shape ratio for maximum thrust can be identified.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.1
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• pp.80-86
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• 2009

Main bearing cap is one of the essential structural elements in internal combustion engine. Main bearing cap guides and holds the crankshaft, withstanding the full combustion and inertia loads of the engine. A seamless design methodology using FEA has been proposed to produce a reliable design of main bearing cap. A Levy's thick cylinder model was applied to calculate the contact pressure between bearing shell and housing bore. A calculated contact pressure at housing bore is within the allowed limit comparing with that from bearing shell model. An adequate FEA model was suggested to obtain reliable solutions for the durability of main bearing cap. 3D global model consists of engine bulkhead, main bearing cap, and bolts. Sub-model consisting of cap and part of bolts is used to get detailed solution of main bearing cap. A very careful contact modeling practice is needed to resolve the convergence problems frequently encountering during combined geometric and material non-linear problems. A proposed methodology has been applied to the main bearing cap model successfully and obtained reliable stress results and fatigue safety factors.

• Nuclear Engineering and Technology
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• v.56 no.4
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• pp.1347-1356
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• 2024

Measuring the residual stress in the components in nuclear power plants is crucial to their safety evaluation. The instrumented indentation technique is a minimally invasive approach that can be conveniently used to determine the residual stress in structural materials in service. Because the indentation behavior of a structure with residual stresses is closely related to the elastic-plastic behavior of the indented material, an accurate understanding of the elastic-plastic behavior of the material is essential for evaluation of the residual stresses in the structures. However, due to the analytical problems associated with solving the elastic-plastic behavior, empirical equations with limited applicability have been used. In the present study, the impact of the non-equibiaxial residual stress state on indentation behavior was investigated using finite element analysis. In addition, a new nonequibiaxial residual-stress prediction methodology is proposed using a convolutional neural network, and the performance was validated. A more accurate residual-stress measurement will be possible by applying the proposed residual-stress prediction methodology in the future.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1055-1056
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• 2011

This paper deals with optimum design criteria for maximum torque density & minimum current density of a single phase line-start permanent-magnet motor (LSPMM) using RSM (Response Surface Methodology) & FEM (Finite Element Method). The focus of this paper is to find a design solution through the comparison of torque density and minimum current density resulting from rotor shape variations. And then, a central composite design (CCD) mixed resolution is introduced, and analysis of variance (ANOVA) is conducted to determine the significance of the fitted regression model.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.4
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• pp.118-124
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• 1998

The die wear is one of the main factors affecting die accuracy and tool lifetime. It is desired to reduce die wear by developing simulation method to predict wear based on process variables, and then optimizing the process. Therefore, this paper describes methodology of preform design for minimizing wear of finisher die in multi-stage cold forging processes. The finite element method is combined with the routine of wear prediction. The cold forging process is analyzed using developed simulation method. In order to obtain preform to minimize die wear, the Flexible Polyhedron Search(FPS) algorithm is used. The optimal preform shape is found from iterative deformation analysis and wear calculation.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.896-900
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• 1996

New approach to determine the optimal design of automotive tire profile was introduced. In this study, optimal design technology was combined with a finite element method. Through tire profile optimization, tire profile was obtained and its profile improved high speed durability and maneuverability Static and dynamic behavior analysis of new concepted tire was compared with conventional tire profile. Optimal design methodology will provide much informations to improve various tire performances.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 1997.10a
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• pp.164-167
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• 1997

The die wear is one of the main factors affecting die accuracy and tool lifetime. It is desired to reduce die wear by developing simulation method to predict wear based on process parameters, and then optimize the process. Therefore, this paper describes disign methodology of preform for minimizing wear of finisher die in multi-stage cold forging processes. The finite element method is combined with the routine of wear prediction and the cold forging processes. The finite element method is combined with the routine of wear prediction and the cold forging process is analyzed. In order to obtain preform to minimize die wear, the FPS algorithm is applied and the optimal preform shape is found from iterative deformation analysis and wear calculation.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1199-1209
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• 2021

The detection of unexpected loose parts in the primary coolant system in a nuclear power plant remains an extremely important issue. It is essential to develop a methodology for the localization and mass estimation of loose parts owing to the high prediction error of conventional methods. An effective approach is presented for the localization and mass estimation of a loose part using machine-learning and deep-learning algorithms. First, a methodology was developed to estimate both the impact location and the mass of a loose part at the same times in a real structure in which geometric changes exist. Second, an impact database was constructed through a series of impact finite-element analyses (FEAs). Then, impact parameter prediction modes were generated for localization and mass estimation of a simulated metallic loose part using machine-learning algorithms (artificial neural network, Gaussian process, and support vector machine) and a deep-learning algorithm (convolutional neural network). The usefulness of the methodology was validated through blind tests, and the noise effect of the training data was also investigated. The high performance obtained in this study shows that the proposed methodology using an FEA-based database and deep learning is useful for localization and mass estimation of loose parts on site.

• Computers and Concrete
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• v.2 no.5
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• pp.389-409
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• 2005

The Finite Element Analysis (FEA) is evidently a powerful tool for the analysis of structural concrete having nonlinearity and brittle failure properties. However, the result of FEA of structural concrete is sensitive to two modeling factors: the shear transfer coefficient (STC) for an open concrete crack and force convergence tolerance value (CONVTOL). Very limited work has been done to find the optimal FE Modeling (FEM) methodologies for structural concrete members strengthened with externally bonded FRP sheets. A total of 22 experimental deep beams with or without FRP flexure or/and shear strengthening systems are analyzed by nonlinear FEA using ANAYS program. For each experimental beams, an FE model with a total of 16 cases of modeling factor combinations are developed and analyzed to find the optimal FEM methodology. Two elements the SHELL63 and SOLID46 representing the material properties of FRP laminate are investigated and compared. The results of this research suggest that the optimal combination of modeling factor is STC of 0.25 and CONVTOL of 0.2. A SOLID 46 element representing the FRP strengthening system leads to better results than a SHELL 63 element does.