• Transactions of Materials Processing
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• v.20 no.7
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• pp.491-497
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• 2011

This work describes a method for determining material parameters included in recrystallization and grain growth models of metallic materials. The focus is on the recrystallization and grain growth models of Ni-Fe based superalloy, Alloy 718. High temperature compression test data at different strain, strain rate and temperature conditions were chosen to determine the material parameters of the model. The critical strain and dynamically recrystallized grain size and fraction at various process conditions were generated from the microstructural analysis and strain-stress relationships of the compression tests. Also, isothermal heat treatments were utilized to fit the material constants included in the grain growth model. Verification of the determined material parameters is carried out by comparing the average grain size data obtained from other compression tests of the Alloy 718 specimens with the initial grain size of $59.5{\mu}m$.

• Journal of Welding and Joining
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• v.24 no.4
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• pp.39-49
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• 2006

The austenite grain growth model in low alloyed steel HAZ without precipitates was proposed by analyzing isothermal grain growth behavior. Steels used in this study were designed to investigate the effect of alloying elements. Meanwhile, a systematic procedure was proposed to prevent inappropriate neglect of initial grain size (D0) and misreading both time exponent and activation energy for isothermal grain growth. It was found that the time exponent was almost constant, irrespectively of temperature and alloying elements, and activation energy increased with the addition of alloying elements. From quantification of the effect of alloying elements on the activation energy, an isothermal grain growth model was presented. Finally, combining with the additivity rule, the austenite grain size in the CGHAZ was predicted.

• Journal of ILASS-Korea
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• v.14 no.2
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• pp.77-83
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• 2009

In this study, the effect of important parameters such as the pixel size and number of a CCD, the object distance, the wavelength of laser, and the particle diameter on the depth of focus in digital in-line particle holography were investigated. The depth of focus in several different cases was calculated using simulation holograms and detailed description of the depth of focus in digital particle holography was presented. The depth of focus is directly proportional to the object distance and the particle size. With the increase of the wavelength of laser, the depth of focus is decreased. The depth of focus is also inversely proportional to the pixel size and number of a CCD. Using the data of depth of focus from simulation holograms and a data-fitting software, we obtained the prediction equations of depth of focus for typical CCD cameras. Finally, the prediction equations of depth of focus in digital particle holography were verified by investigating real holograms of the calibration target in different cases and satisfied agreement between measured values and predicted values was confirmed.

• Journal of Animal Environmental Science
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• v.20 no.2
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• pp.41-48
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• 2014

This study was conducted to investigate the dispersion prediction of odor from swine farms in Korea. Gaussian Plume model used in considering of farm size, wind velocity, atmospheric stability and threshold odor unit to prediction of odor dispersion based on the survey on current state of odor emission and control from 48 site of swine farms. Farm size, wind velocity and atmospheric stability were affected the distance of odor dispersion, showed longer distance in cases of large farm, low wind velocity and stable atmospheric condition. We will suggestion the adjusted distance of odor dispersion according to farm size was estimated to 180 m in small farm and 320 m in large farm when apply the 3 OU, 5 m/s wind velocity and stable atmospheric condition.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.24 no.4
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• pp.413-419
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• 2011

The nano-size hoenycomb structures have the higher ratio of the surface to the volume than macro-size honeycomb structures, and they can maximize the functionality of the electrical and chemical catalyst. The mechanical behaviors of the nano-sized structures are different from ones of the macro-size structure, and it is caused by the surface effect. This surface effect can be investigated by the atomistic simulation; however, the prediction of mechanical behaviors of the nano-sized honeycombs are practically impossible due to excessive computational resources and computation time. In this paper, by combining the bridging method considering the surface stress elasticity model with homogenization method, the mechanical behaviors of the nano-sized honeycombs are predicted efficiently.

• Journal of Ocean Engineering and Technology
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• v.35 no.5
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• pp.336-346
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• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.

• 제어로봇시스템학회:학술대회논문집
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• 1990.10a
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• pp.362-366
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• 1990

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitative analyses of sampled process variables have been successfully attempted to achieve a uniform nugget size. In this paper, the electrode movement signal which is a good indicative of the nugget size was examined by introducing a mathematical model with four parameters. A neural network method was applied for the estimation of the nugget size by four parameters. The prediction by the neural network is in good agreement with the actual nugget size. The results are quite promising in that the qualitative estimation of the invisible nugget size can be achieved without destructive testing of the welds.

• Proceedings of the KWS Conference
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• 1992.10a
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• pp.91-95
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• 1992

The resistance spot welding process has been extensively used for joining of sheet metals, which are subject to variation of many process variables. Many qualitive analyses of sampled process variables have been attempted to predict nugget size. In this paper, dynamic resistance and electrode movement signal which is a good indicative of the nugget size was examined by introducing an artificial neural network estimator. An artificial neural feedforward network with back-propagation of error was applied for the estimation of the nugget size. The prediction by the neural network is in good agreement with the actual nugget size for resistance spot welding of galvanized steel. The results are quite promising in that the quantitative estimation of the invisible nugget size can be achieved without conventional destructive testing of welds.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.46-50
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• 2019

In this paper, We used the regression model of machine learning for improve the print quantity problem when which print scaffold with 400 ㎛ pore using FDM 3d printer. We have difficult to experiment with changing all factors in the field. So we reduced print quantity by selected two factors that most impact the pore size. We printed and measured scaffold 5 times under same conditions. We created regression model using scaffold pore size and print conditions. We predicted pore size of untested print condition using the regression model. After print scaffold with 400 ㎛ pore, we printed scaffold 5 times under same conditions. We compare the predicted scaffold pore size and the measured scaffold pore size. We confirmed that error is less than 1 % and we verified the results quantitatively.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.159-161
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• 2017

In this paper, we propose an adaptive search range allocation algorithm for high-performance HEVC encoder and a hardware architecture suitable for the proposed algorithm. In order to improve the prediction performance, the existing motion vector is configured with the motion vectors of the neighboring blocks as prediction vector candidates, and a search range of a predetermined size is allocated using one motion vector having a minimum difference from the current motion vector. The proposed algorithm reduces the computation time by reducing the size of the search range by assigning the size of the search range to the rectangle and octagon type according to the structure of the motion vectors for the surrounding four blocks. Moreover, by using all four motion vectors, it is possible to predict more precisely. By realizing it in a form suitable for hardware, hardware area and computation time are effectively reduced.