• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.336-345
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• 2004

Investigated via a series of finite-element(FE) process simulation is the effect of diverse process variables on some selected non-dimensional parameters characterizing the thermo-mechanical behavior of the roll and strip in hot strip rolling. Then, on the basis of these parameters, on-line models are derived for the precise prediction of the temperature changes occurring in the bite zones as well as in the inter-stand zones in a finishing mill. The prediction accuracy of the proposed models is examined through comparison with predictions from a FE process model.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.349-358
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• 2004

The strip temperature history of finishing mill process is one of the most important factors to stabilize the facilities and to achieve the better product quality including a better prediction of roll force etc. The ultimate goal of this study is to improve scientific understanding of the finishing mill process in the view of heat transfer science. Finishing mill cooling facilities of KwangYang $\#3$ hot rolling are introduced and heat transfer analyses from FET to FDT are particularly focused in this study Three major tasks are successfully achieved as follows: 1) The temperature Prediction Models are developed. 2) The average absolute error is found to be less then 10 Celsius degree (about $8.5^{\circ}C$). 3) Prediction rate (less then $\bar{+}20$) are $10.2\%$ improved $(80.1\;\rightarrow\;90.3\%)$.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.11 no.2
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• pp.34-39
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• 2012

In this study, the friction welding experiment was performed by using the design of experiment. And the signal data acquired by acoustic emission sensor were analyzed to predict the tensile strength of friction welding part at friction welding process for AZ31 magnesium alloy. A dimensionless coefficient($\phi_{AE}$), which consisted in the square of AE rms and variance, was defined as the characteristic of friction welding and the prediction equation was obtained by using linear regression. As the result of analysis, it was seen that the correlation between predicted and measured values became very close and on-line prediction of the ensile strength was possible in friction welding part.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.6
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• pp.160-169
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• 2001

To predict busting failure in tubular hydroforming, the criteria for ductile fracture proposed by Oyane is combined with the finite element method. From the histories of stress and strain in each element obtained from finite element analysis, the fracture initiation site is predicted by mean of the criterion. The prediction by the ductile fracture criterion is applied to three hydroforming processes such as a tee extrusion, an automobile rear axle housing and lower am. For these products, the ductile fracture integral I is not only affected by the process parameters, but also by preforming processes. All the simulation results show the combination of the finite element analysis and the ductile fracture criteria is useful in the prediction of farming limit in hydroforming processes.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.5
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• pp.123-130
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• 2008

In order to clarify detailed mechanism of the flexible disk grinding system, workpiece length was introduced and its performance was evaluated. Flat zone ratio increased as the workpiece length increased. Increasing wheel speed and depth of cut also enhanced process performance by producing larger flat zone ratio. Neural network system was successfully applied to predict minimum depth of engagement and flat zone ratio. An additional input parameter as workpiece length to the neural network system enhanced the prediction performance by reducing error rate. By rearranging the Input combinations to the network, the workpiece length was precisely predicted with the prediction error rate lower than 2.8% depending on the network structure.

• Smart Structures and Systems
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• v.29 no.6
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• pp.767-775
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• 2022

Metal additive manufacturing (AM), also known as metal three-dimensional (3D) printing, produces 3D metal products by repeatedly adding and solidifying metal materials layer by layer. During the metal AM process, products experience repeated local melting and cooling using a laser or electron beam, resulting in product defects, such as warpage, cracks, and internal pores. Such defects adversely affect the final product. This paper proposes the in situ monitoring-based warpage prediction of metal AM products with experimental feature extraction. The temperature profile of the metal AM substrate during the process was experimentally collected. Time-domain features were extracted from the temperature profile, and their relationships to the warpage mechanism were investigated. The standard deviation showed a significant linear correlation with warpage. The findings from this study are expected to contribute to optimizing process parameters for metal AM warpage reduction.

• Computers and Concrete
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• v.32 no.5
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• pp.513-525
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• 2023

Recently, research on predicting the behavior of reinforced concrete (RC) columns using machine learning methods has been actively conducted. However, most studies have focused on predicting the ultimate strength of RC columns using a regression algorithm. Therefore, this study develops a successive machine learning process for predicting multiple nonlinear behaviors of rectangular RC columns. This process consists of three stages: single machine learning, bagging ensemble, and stacking ensemble. In the case of strength prediction, sufficient prediction accuracy is confirmed even in the first stage. In the case of displacement, although sufficient accuracy is not achieved in the first and second stages, the stacking ensemble model in the third stage performs better than the machine learning models in the first and second stages. In addition, the performance of the final prediction models is verified by comparing the backbone curves and hysteresis loops obtained from predicted outputs with actual experimental data.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.61-66
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• 2007

The performance of a building material degrades as time goes by and the failure of the material is often defined as the point at which the performance of the material reaches a pre-specified degraded level. Based on a stochastic deterioration model, a performance based service life prediction method for building materials and components is developed. As a stochastic degradation model, a gamma process is considered and lifetime distribution and service life of a material are predicted using the degradation model. A numerical example is provided to illustrate the use of the proposed service life prediction method.

• Journal of Korea Multimedia Society
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• v.13 no.12
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.4
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• pp.38-43
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• 2010

A continuing challenge in the aviation industry is how to safely keep aircraft in service longer with limited maintenance budgets. Therefore, all the advanced countries in aircraft technologies put great efforts in prediction of failure rate in parts and system, but in the domestic aircraft industry is lack of theoretical and experimental research. Prediction of failure rate provides a rational basis for design decisions such as the choice of part quality levels and derating factors to be applied. For these reasons, analytic prediction of failure rate is essential process in developing aircraft structure. In this paper, a procedure for prediction of failure rate for aircraft structural parts is presented. Cargo door kinematic parts are taken to illustrate the process, in which the failure rate for Hook part is computed by using Monte Carlo Simulation along with Response Surface Model, and system failure rate is obtained afterwards.