• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.10a
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• pp.170-174
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• 2004

Despite the widespread use in the various manufacturing industries, the full automation of the robotic CO$_2$ welding has not yet been achieved partly because the mathematical model for the process parameters of a given welding task is not fully understood and quantified. Several mathematical models to control welding quality, productivity, microstructure and weld properties in arc welding processes have been studied. However, it is not an easy task to apply them to the various practical situations because the relationship between the process parameters and the bead geometry is non-linear and also they are usually dependent on the specific experimental results. Practically, it is difficult, but important to know how to establish a mathematical model that can predict the result of the actual welding process and how to select the optimum welding condition under a certain constraint. In this paper, an attempt has been made to develop an Radial basis function network model to predict the weld top-bead width as a function of key process parameters in the robotic CO$_2$ welding. and to compare the developed model and a simple neural network model using two different training algorithms in order to verify performance. of the developed model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.04a
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• pp.73-74
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• 2022

Concrete structures occupy the largest proportion of modern infrastructure, and concrete structures often have cracking problems. Existing concrete crack diagnosis methods have limitations in crack evaluation because they rely on expert visual inspection. Therefore, in this study, we design a deep learning model that detects, visualizes, and outputs cracks on the surface of RC structures based on image data by using a CNN (Convolution Neural Networks) model that can process two- and three-dimensional data such as video and image data. do. An experimental study was conducted on an algorithm to automatically detect concrete cracks and visualize them using a CNN model. For the three deep learning models used for algorithm learning in this study, the concrete crack prediction accuracy satisfies 90%, and in particular, the 'InceptionV3'-based CNN model showed the highest accuracy. In the case of the crack detection visualization model, it showed high crack detection prediction accuracy of more than 95% on average for data with crack width of 0.2 mm or more.

• Nuclear Engineering and Technology
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• v.49 no.7
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• pp.1423-1430
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• 2017

Detonation cell width is an important parameter in hydrogen explosion assessments. The experimental data on gas detonation are statistically analyzed to establish a universal method to numerically predict detonation cell widths. It is commonly understood that detonation cell width, ${\lambda}$, is highly correlated with the characteristic reaction zone width, ${\delta}$. Classical parametric regression methods were widely applied in earlier research to build an explicit semiempirical correlation for the ratio of ${\lambda}/{\delta}$. The obtained correlations formulate the dependency of the ratio ${\lambda}/{\delta}$ on a dimensionless effective chemical activation energy and a dimensionless temperature of the gas mixture. In this paper, support vector regression (SVR), which is based on nonparametric machine learning, is applied to achieve functions with better fitness to experimental data and more accurate predictions. Furthermore, a third parameter, dimensionless pressure, is considered as an additional independent variable. It is found that three-parameter SVR can significantly improve the performance of the fitting function. Meanwhile, SVR also provides better adaptability and the model functions can be easily renewed when experimental database is updated or new regression parameters are considered.

• Journal of the Korean Society of Industry Convergence
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• v.20 no.2
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• pp.195-204
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• 2017

Predicting the failure life of automated manufacturing systems can reduce overall downtime, maintenance costs, and total plant operation costs. Therefore, there is a growing interest in fatigue failure mechanisms as the safety or service life assessment of manufacturing systems becomes an important issue. In particular, fretting fatigue is caused by repeated tangential stresses that are generated by friction during small amplitude oscillatory movements or sliding between two surfaces pressed together in intimate contact. Previous studies in fretting fatigue have observed size effects related to contact width such that a critical contact width exists where there is drastic change in the fretting fatigue life. However, most of them are the two-dimensional finite element analyses based on the plane strain assumption. The purpose of this study is to investigate the contact size effects on the three-dimensional finite element model of a finite width of a flat specimen and a cylindrical pad exposed to fretting fatigue. The contact size effects were analyzed by means of the stress and strain averages at the element integration points of three-dimensional finite element model. This study shows that the fretting fatigue life of manufacturing systems can be predicted by three-dimensional finite element analysis based on SWT critical plane model.

• Journal of Korean Port Research
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• v.12 no.1
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• pp.105-111
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• 1998

A horizontal 2-D model which includes the wetting-drying treatment technique in the intertidal zone is applied for the prediction of tidal changes. The flow model is applied to Mokpo coastal zone and verified by measurement data. Comparing computation results with observed values for the M2 tidal constituent, agreeable correspondence is detected. The validity of the model is also proven by applying it to such areas which have narrow width and rapid velocity, irregular topography and complex geometry. Thus, this model can be used as the compatible prediction model for the tidal change and pollutant transport due to the development of Mokpo coastal zone.

• Geomechanics and Engineering
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• v.20 no.5
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• pp.385-397
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• 2020

This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio D_f/B, foundation net applied pressure q_net, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.

• Transactions of Materials Processing
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• v.22 no.7
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• pp.371-376
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• 2013

The shape defects such as edge waves and center buckles may be formed in the rolled strip because rolling can easily produce non-homogenous elongation across the strip width. The main purpose of tension leveling is to remove such defects by eliminating the differences in elongation. In this paper, a new approach for the optimization of the process conditions in tension leveling is presented. The approach consists of an analytic model for the prediction of the strip curvature and the force at each roll. The accuracy of the proposed model is examined through comparison with the predictions from a finite element model.

• Journal of The Korean Society of Agricultural Engineers
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• v.54 no.4
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• pp.73-81
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• 2012

In this study, the settlement data of 32 center cored rockfill dams (total 39 monitored data) were collected and analyzed to develop the method to predict the crest settlement of a CCRD after impounding by using the internal settlement data occurred during construction. An artificial neural network (ANN) modeling was used in developing the method, which was considered to be a more reliable approach since in the ANN model dam height, core width, and core type were all considered as input variables in deriving the crest settlement, whereas in conventional methods, such as Clements's method, only dam height is used as a variable. The ANN analysis results showed a good agreement with the measured data, compared to those by the conventional methods using regression analysis. In addition, a simple procedure to use the ANN model for engineers in practice was provided by proposing the equations used for given input values.

• Journal of Environmental Science International
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• v.28 no.8
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• pp.701-707
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• 2019

Highland farming is agriculture that takes place 400 m above sea level and typically involves both low temperatures and long sunshine hours. Most highland Chinese cabbages are harvested in the Gangwon province. The Ubiquitous Sensor Network (USN) has been deployed to observe Chinese cabbages growth because of the lack of installed weather stations in the highlands. Five representative Chinese cabbage cultivation spots were selected for USN and meteorological data collection between 2015 and 2017. The purpose of this study is to develop a weight prediction model for Chinese cabbages using the meteorological and growth data that were collected one week prior. Both a regression and random forest model were considered for this study, with the regression assumptions being satisfied. The Root Mean Square Error (RMSE) was used to evaluate the predictive performance of the models. The variables influencing the weight of cabbage were the number of cabbage leaves, wind speed, precipitation and soil electrical conductivity in the regression model. In the random forest model, cabbage width, the number of cabbage leaves, soil temperature, precipitation, temperature, soil moisture at a depth of 30 cm, cabbage leaf width, soil electrical conductivity, humidity, and cabbage leaf length were screened. The RMSE of the random forest model was 265.478, a value that was relatively lower than that of the regression model (404.493); this is because the random forest model could explain nonlinearity.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2004.08a
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• pp.153-162
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• 2004

The force profile from strip to work roll is very important factor in deformation of roll. But It is not easy to predict the profile because strip crown affect its tendency. From finite element method result, some assumptions can be obtained and the roll force profile model is derived. Also the tension profile and lateral strain are derived. The prediction accuracy of the proposed model is examined through comparison with finite element calculation result.