• Journal of Power System Engineering
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• v.15 no.6
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• pp.73-80
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• 2011

The experimental investigation focuses on an influence of artificial aging time in longitudinal butt welded Al 6013-T4 aluminum alloy on the fatigue crack growth resistance. The preferred welding processes for this alloy are frequently tungsten inert gas welding (TIG) process due to its comparatively easier applicability and better weldability than other gas metal arc welding. Fatigue crack growth tests were carried out on compact tension specimens (CT) in longitudinal butt TIG welded after T82 heat treatment was varied in three artificial aging times of 6 hours, 18 hours and 24 hours. Of the three artificial aging times, 24 hours of artificial aging time are offering better resistance against the growing fatigue cracks. The superior fatigue crack growth resistance preferred spatial variation of materials within each specimen in the Paris equation based on reliability theory and fatigue crack growth rate by crack length are found to be the reasons for superior fatigue resistance of 24 hours of artificial aging time was compared to other joints. The highest of crack propagation resistance occurs in artificial aging times of 24 hours due to the increase in grain size (fine grained microstructures).

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.89-96
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• 2006

Nondestructive evaluation using surface waves needs an analytical solution for the reference value to compare with experimental data. Finite element analysis is very powerful tool to simulate the wave propagation, but has some defects. It is very expensive and high time-complexity for the required high resolution. For those reasons, it is hard to implement an optimization problem in the actual situation. The developed engine in this paper can substitute for the finite element analysis of surface waves propagation, and it accomplishes the fast analysis possible to be used in optimization. Including this artificial intelligence engine, most of soft computing algorithms can be applied on the special database. The database of surface waves propagation is easily constructed with the results of finite element analysis after reducing the dimensions of data. The principal wavelet-component analysis is an efficient method to simplify the transient wave signal into some representative peaks. At the end, artificial neural network based on the database make it possible to invent the artificial intelligence engine.

• Horticultural Science & Technology
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• v.16 no.2
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• pp.242-243
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• 1998

This research was conducted to find the most effective and economical artificial propagation method of Lycoris squamigera among chipping, half-chipping, twin-scaling, coring, scooping and notching, in order to investigate the number of bulblets obtained from one bulb and the number of bulbs treated by a man in a day for artificial propagation. We got 42.2 bulblets from one bulb by twin-scaling, 23.2 bulblets by half-chipping, 18.2 bulblets by chipping, 14.3 bulblets by notching, 1 bulblet by coring and also 1 bulblet by scooping. Although we got the most bulblets by twin-scaling, it took the most time to treat the bulbs for artificial propagation (200 bulbs per day) and the bulblets formed were very small. But 400 bulbs were treated in a day by chipping and the bulblets formed were comparatively large. In addition, machines could be used for chipping, so chipping was considered to be the most effective and economical artificial propagation method.

• Journal of Environmental Health Sciences
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• v.28 no.3
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• pp.19-25
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• 2002

In this paper, the water quality forecast was performed on the BOD of the Chungju Dam using the ARIMA model, which is a nonlinear statistics model, and the artificial neural network model. The monthly data of water quality were collected from 1991 to 2000. The most appropriate ARIMA model for Chungju dam was found to be the multiplicative seasonal ARIMA(1,0,1)(1,0,1)$_{12}$, model. While the artificial neural network model, which is used relatively often in recent days, forecasts new data by the strength of a learned matrix like human neurons. The BOD values were forecasted using the back-propagation algorithm of multi-layer perceptrons in this paper. Artificial neural network model was com- posed of two hidden layers and the node number of each hidden layer was designed fifteen. It was demonstrated that the ARIMA model was more appropriate in terms of changes around the overall average, but the artificial neural net-work model was more appropriate in terms of reflecting the minimum and the maximum values.s.

• Journal of the Korean GEO-environmental Society
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• v.10 no.5
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• pp.49-57
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• 2009

In this study, one of the complicated geotechnical problem, compression index was predicted by a artificial neural network method of Levenberg-Marquardt (LM) algorithm. Predicted values were compared and evaluated by the results of the Back Propagation (BP) method, which is used extensively in geotechnical engineering. Also two different results were compared with experimental values estimated by verified experimental methods in order to evaluate the accuracy of each method. The results from experimental method generally showed higher error than the results of both artificial neural network method. The predicted compression index by LM algorithm showed better comprehensive results than BP algorithm in terms of convergence, but accuracy was similar each other.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2009.05a
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• pp.357-360
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. In this research, we tried the back-propagation algorithm of artificial neural network to find an optimum solution in the cooling system design of injection mold. The cooling system optimization problem that was once solved by a response surface method with 4 design variables was solved by applying the back-propagation algorithm, resulting in a solution with a sufficient accuracy. Furthermore the number of training points was much reduced by applying the fractional factorial design without losing solution accuracy.

• Tunnel and Underground Space
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• v.5 no.3
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• pp.251-265
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• 1995

One of the most important matters in stress measurement by hydraulic fracturing technique is the determination of the breakdown pressure, reopening pressure, and shut-in pressure, since these values are the basic input data for the calculation of the in-situ stress. The control of the fracture propagation is also important when the hydraulic fracturing technique is applied to the development of groundwater system, geothermal energy, oil, and natural gas. In this study, a laboratory scale hydraulic fracturing device was built and a series of model tests were conducted with cube blocks of Machon gabbro. A new method called 'flatjack method' was adopted to determine shut-in pressure. The initial stress calculated from the shut-in pressure measured by flatjack method showed much higher accuracy than the stress determined by the conventional method. The dependency of the direction of fracture propagation on the state of the initial stresses was measured by introducin g artificial slots in the borehole made by water jet system. Numerical modeling by BEM was also performed to simulate the fracture propagation process. Both results form numerical and laboratory tests showed good agreement. From this study which provides the extensive results on the determination of shut-in pressure and the control of fracture propagation which are the critical issue in the recent hydraulic fracturing, it is conclued that in-situ stress measurement and the control of fracture propagation could be achived more accurately.

• Proceedings of the Korean Geotechical Society Conference
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• 2002.10a
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.18 no.4
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• pp.430-435
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• 2009

The cooling stage greatly affects the product quality in the injection molding process. The cooling system that minimizes temperature variance in the product surface will improve the quality and the productivity of products. The cooling circuit optimization problem that was once solved by a response surface method with 4 design variables. It took too much time for the optimization as an industrial design tool. It is desirable to reduce the optimization time. Therefore, we tried the back-propagation algorithm of artificial neural network(BPN) to find an optimum solution in the cooling circuit design in this research. We tried various ways to select training points for the BPN. The same optimum solution was obtained by applying the BPN with reduced number of training points by the fractional factorial design.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.367-368
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• 2006

Today, the Artificial Intervertebral Disc (AID) is being developed by increasing the oblique of the endplate gradually. In other words, Ultra-high Molecular Weight Polyethylene (UHMWPE) which is apply to the sliding core of the AID, does not change the shape but alters the oblique of endplate. However, the unreasonable increase of degree of freedom (DOF) can result in the aggravation of the bone fusion and the initial stability and it can also lead to the increase of the concentrated force in core. For these reasons, it is necessary to develop the advanced techniques, which choose the most adequate DOF. In this study, the new optimized modeling of the sliding core and the endplate, the fatigue characteristics, the crack propagation and the formation mechanism of wearing debris was studied and the minimizing technique will be derived from this research.