• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2006.05a
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• pp.373-376
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• 2006

The ring rolling design for a large-scale Ti-6Al-4V alloy ring was performed with a calculation method and FEM simulation. The ring rolling design includes geometry design and optimization of process variables. The calculation method was to determine geometry design such as initial billet and blank size, and final rolled ring shape. A commercial FEM code, SHAPE was used to simulate the effect of process variables in ring rolling on the distribution of the internal state variables such as strain, strain rate and temperature. In order to predict the forming defects during ring rolling, the process-map approach based on Ziegler's instability criterion was used with FEM simulation. Finally, an optimum process design to obtain sound Ti-6Al-4V rings without forming defects was suggested through combined approach of Ziegler's instability map and FEM simulation results.

• The Journal of Korean Association of Computer Education
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• v.10 no.3
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• pp.31-38
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• 2007

The purpose of the study was to examine the correlations between team cognition and team process and team performance in e-learning based team learning environment. 55 graduate students consisting of 11 teams participated in the study voluntarily during the spring semester. In the result, it was found that team cognition had no relationship with team process and performance although sub-variables of team cognition and team process as well as team performance had a significant relation with each other. Some further research issues were addressed in terms of team leadership and potential variables affecting each variables.

• Nuclear Engineering and Technology
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• v.51 no.5
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• pp.1451-1469
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• 2019

This paper proposes a residual stress mitigation of a nuclear safety-related austenitic stainless steel TP304 pipe bended by local induction heating process via performing elastic-plastic finite element analysis. Residual stress distributions of the pipe bend were calculated by performing finite element analysis. Validity of the finite element analysis procedure was verified via comparing with temperature histories measured by using thermocouples, ultrasonic thickness measurement results, and residual stress measurement results by a hole-drilling method. Parametric finite element stress analysis was performed to investigate effects of the process and geometric shape variables on the residual stresses on inner surfaces of the pipe by applying the verified procedure. As a result of the parametric analysis, it was found that it is difficult to considerably reduce the inner surface residual stresses by changing the existing process and geometric shape variables. So, in order to mitigate the residual stresses, effect of an additional process such as cooling after the bending on the residual stresses was investigated. Finally, it was identified that the additional heating after the bending can significantly reduce the residual stresses while other variables have insignificant effect.

• KIEAE Journal
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• v.15 no.4
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• pp.105-110
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• 2015

Purpose: This study aimed at finding the optimal input variables of the artificial neural network-based predictive model for the optimal controls of the indoor temperature environment. By applying the optimal input variables to the predictive model, the required time for restoring the current indoor temperature during the setback period to the normal setpoint temperature can be more precisely calculated for the cooling season. The precise prediction results will support the advanced operation of the cooling system to condition the indoor temperature comfortably in a more energy-efficient manner. Method: Two major steps employing the numerical computer simulation method were conducted for developing an ANN model and finding the optimal input variables. In the first process, the initial ANN model was intuitively determined to have input neurons that seemed to have a relationship with the output neuron. The second process was conducted for finding the statistical relationship between the initial input variables and output variable. Result: Based on the statistical analysis, the optimal input variables were determined.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.16 no.5
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• pp.205-210
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• 2007

The integration method of binary and real encoding in genetic algorithm is proposed to deal with design variables of various types in gear drive design. The method is applied to optimum design of multi-stage gear drive. Integer and Discrete type design variables represent the number of teeth and module, and continuous type design variables represent face width, helix angle and addendum modification factor etc. The proposed genetic algorithm is applied for the gear ratio optimization and the volume optimization(minimization) of multi-stage geared motor which is used in field. In result, the proposed design optimization method shows an effectiveness in optimum design process and the new design has a better results compared with the existing design.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.243-246
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• 2003

Data mining is a process of discovering useful patterns for decision making from an amount of data. It has recently received much attention in a wide range of business and engineering fields Classifying a group into subgroups is one of the most important subjects in data mining Tree-based methods, known as decision trees, provide an efficient way to finding classification models. The primary concern in tree learning is to minimize a node impurity, which is evaluated using a target variable in the data set. However, there are situations where multiple target variables should be taken into account, for example, such as manufacturing process monitoring, marketing science, and clinical and health analysis. The purpose of this article is to present several methods for measuring the node impurity, which are applicable to data sets with multiple target variables. For illustrations, numerical examples are given with discussion.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2007.05a
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• pp.357-360
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• 2007

The profile ring rolling process of Ti-6Al-4V alloy was designed by finite element(FE) simulation and experimental analysis. The design includes geometry design and optimization of process variables. The geometry design such as initial billet and blank sizes, and final rolled ring shape was carried out with the calculation method based on the uniform deformation concept between the wall thickness and ring height. FEM simulation was used to calculate the state variables such as strain, strain rate and temperature and to predict the formation of forming defects during ring rolling process. Finally, the mechanical properties of profiled Ti-6Al-4V alloy ring product were analyzed with the evolution of microstructures during the ring rolling process.

• Journal of the Korean Data and Information Science Society
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• v.19 no.2
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• pp.609-620
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• 2008

There may be several parallel equipments having the same function in a multi-stage manufacturing process, which affect the product quality differently and have significant differences in defect rate. The product quality may depend on what equipments it has been processed as well as what process variable values it has. Applying one model ignoring the presence of different equipments may distort the prediction of defect rate and the identification of important quality variables affecting the defect rate. We propose a procedure for data segmentation when constructing models for predicting the defect rate or for identifying major process variables influencing product quality. The proposed procedure is based on the principal component analysis and the analysis of variance, which demonstrates a better performance in predicting defect rate through a case study with a PDP manufacturing process.

• Journal of Korean Society for Quality Management
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• v.31 no.4
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• pp.219-225
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• 2003

Two­dimensional warranty data can be modelled using two different approaches： two­dimensional point process and one­dimensional point process with usage as a function of age. The first approach has three different models. First of all, bivariate model is appealing but is not appropriate for explaining warranty claims. Next, the rest of the two models (marked point process, and counting and matching on both directions independently) are more appropriate for explaining warranty claims. However, the second one (counting and matching on both directions independently) assumes that the two variables (variables representing the two­dimensions) are independent. Last of all, one­dimensional point process with usage as a function of age is also promising to explain the two­dimensional warranty claims. But the models or variations of them need more investigation to be applicable to real warranty claim data.