• Journal of the Korean Society of Manufacturing Process Engineers
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• v.17 no.4
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• pp.64-69
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• 2018

This paper investigates the compressive deformation behavior of direct metal tooling (DMT), processing titanium alloy (Ti-6Al-4V) parts under high strain loading conditions. Split Hopkinson Pressure Bar (SHPB) experiments were performed to determine the flow stress and the coefficients of the Johnson-Cook model. This model is described as a function of strain, strain rate, and temperature. SHPB experiments were performed to characterize the deformation behavior of specimens made with 3D printers, using Ti-6Al-4V material under high temperature and dynamic loading.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.45-45
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.

• Journal of Welding and Joining
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• v.20 no.6
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• pp.769-775
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• 2002

This study is a middle report on the development of intelligent spot welding monitoring technology applicable to the production line. An intelligent algorithm has been developed to predict the quality of welding in real time. We examined whether it is effective or not through the In-Line and the Off-Line tests. The purpose of the present study is to provide a reliable solution which can prevent welding defects in production site. In this study, the process variables, which were monitored in the primary circuit of the welding, are used to estimate the weld quality by Hidden Markov Model(HMM). The primary dynamic resistance patterns are recognized and the quality is estimated in probability method during the welding. We expect that the algorithm proposed in the present study is feasible to the applied in the production sites for the purpose of in-process real time quality monitoring of spot welding.

• Journal of the Semiconductor & Display Technology
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• v.6 no.3
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• pp.1-6
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• 2007

The integrated thermal-chemical-mechanical (TCM) material removal model presented in the companion paper is dynamically simulated in this work. The model is applied to a Cu CMP process for the simulation and the results of the three individual ingredients composing the model are presented separately first. These results are then incorporated to calculate the total material removal rate (MRR) of the Cu CMP. It is shown that the non-linear trend of MRR with respect to the applied mechanical power (i.e., non-Prestonian behavior), which is not well explained with the models established in principle on conventional contact mechanics, may be due to the chemical reaction(s) varying non-linearly with the temperature in the wafer.

• Journal of information and communication convergence engineering
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• v.11 no.1
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• pp.24-29
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• 2013

In this paper, we propose a fuzzy inference model for a navigation algorithm for a mobile robot that intelligently searches goal location in unknown dynamic environments. Our model uses sensor fusion based on situational commands using an ultrasonic sensor. Instead of using the "physical sensor fusion" method, which generates the trajectory of a robot based upon the environment model and sensory data, a "command fusion" method is used to govern the robot motions. The navigation strategy is based on a combination of fuzzy rules tuned for both goal-approach and obstacle-avoidance based on a hierarchical behavior-based control architecture. To identify the environments, a command fusion technique is introduced where the sensory data of the ultrasonic sensors and a vision sensor are fused into the identification process. The result of experiment has shown that highlights interesting aspects of the goal seeking, obstacle avoiding, decision making process that arise from navigation interaction.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.24-27
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• 1995

This paper presents, step-by-step, the capabilities that a general-purpose simulation environment, such as Simulab/Matlab,provides for an intuitive and sfficient modelling of grinding processes. Starting from a revision of the different approaches which can go found in the technical literature the paper begins with the well-known block-diagram forst presented by Snoyes and how the different parameters for the simulation are introduced in the model(machine,grinding wheel and process parameters). Special attention is paid to the fact that nonlinear phenomena can easily be include.

• Korean Chemical Engineering Research
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• v.61 no.1
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• pp.89-96
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• 2023

The Wiener-type nonlinear model where a static nonlinear block follows a dynamic linear block is widely used to describe the dynamics of chemical processes. A long process excitation step is typically needed to identify this Wiener-type nonlinear model with two blocks. In order to cope with this disadvantage, an identification method for the Wiener-type nonlinear model that uses only a single-step response is proposed here. The proposed method estimates the response of the dynamic linear sub-block from the initial part of the step response, and then the static nonlinear sub-block is identified. Because the only single-step response is used to identify the Wiener-type nonlinear model, there is great benefit in time and cost for obtaining process response. The performance of the proposed identification method with the single-step response is verified through a representative Wiener-type nonlinear process, a pH titration process, and a liquid level system.

• Journal of Satellite, Information and Communications
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• v.6 no.1
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• pp.12-18
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• 2011

Signal fading due to rain is one of the most significant factors degrading link quality in satellite communication systems. Adaptive transmission is considered to be the most efficient means to countermeasure the rain attenuation. In order to develop and design a good adaptive transmission system, we need a dynamic rain attenuation model which can synthesize time series of rain attenuation. In this paper, we present a modeling technique for dynamic rain attenuation using a Markov process. We derive statistical fading properties of the rain attenuation data measured in second time interval and define four states in the Markov process. We synthesize the rain attenuation data using the 4-state Markov process, and compare statistical properties of the simulated data to those of the measured data.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.12
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• pp.84-94
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• 1996

In this work, a fuzzy petri net model for modeling a general form of fuzzy production system which consists of chaining fuzzy production rules and so requires multistage reasoning process is presented. For the obtained fuzzy petri net model, the net will be transformed into some matrices, and also be systematically led to an algebraic form of a state equation. Since it is fond that the approximate reasoning process in fuzzy systems corresponds to the dynamic behavior of the fuzzy petri net, it is further shown that the multistage reasoning process can be carried out by executing the state equation.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.156-159
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• 1996

An adaptive predictive control for steam-reforming plant which consist of a steam-gas reformer and a waste heat steam-boiler was studied by using MIMO bilinear model. The simulation experiments of the process identification were performed by using linear and bilinear models. From the simulation results it was found that the bilinear model represented the dynamic behavior of a steam-reforming plant very well. ARMA model was used in the process identification and the adaptive predictive control. To verify the performance and effectiveness of the adaptive predictive controller proposed in this study the simulation results of steam-reforming plant control based on bilinear model were compared to those of linear model. The simulation results showed that the adaptive predictive controller based on bilinear model provides better performance than those of linear model.