• Proceedings of the Korean Society For Composite Materials Conference
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• 2004.10a
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• pp.73-77
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• 2004

Failure predictions of composite single-lap bonded joints were performed considering both of composite adherend failure and bondline failure. An elastic-perfectly plastic model of adhesive and a delamination failure criterion are used. The failure prediction results such as failure mode and strength have very good agreements with the test results of joint specimens with various bonding methods and parameters. The influence of variations in the effective strength (that is, adhesion performance) and plastic behavior of adhesive on the failure characteristics of composite bonded joints are investigated numerically. The numerical results show that optimal joint strength is archived when adhesive and delamination failure occur in the same time.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1186-1191
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• 2003

The 40% of wall criterion, which is generally used for the plugging of steam generator tubes, may be applied only to a single crack. In the previous study, a total of 9 failure models were introduced to estimate the local failure of the ligament between cracks and the optimum coalescence model of multiple collinear cracks was determined among these models. It is, however, known that parallel axial cracks are more frequently detected during an in-service inspection than collinear axial cracks. The objective of this study is to determine the plastic collapse model which can be applied to the steam generator tube containing two parallel axial through-wall cracks. Nine previously proposed local failure models were selected as the candidates. Subsequently interaction effects between two adjacent cracks were evaluated to screen them. Plastic collapse tests for the plate with two parallel through-wall cracks and finite element analyses were performed for the determination of the optimum plastic collapse model. By comparing the test results with the prediction results obtained from the candidate models, a plastic zone contact model was selected as an optimum model.

• Nuclear Engineering and Technology
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• v.36 no.4
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• pp.316-326
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• 2004

The $40\%$ of wall criterion, which is generally used for the plugging of steam generator tubes, is applied only to a single crack. In a previous study, a total number of 9 failure models were proposed to estimate the local failure of the ligament between cracks, and the optimum coalescence model of multiple collinear cracks was determined among these models. It is, however known that parallel axial cracks are more frequently detected than collinear axial cracks during an in-service inspection. The objective of this study is to determine the plastic collapse model that can be applied to steam generator tubes containing two parallel axial through-wall cracks. Three previously proposed local failure models were selected as the candidates. Subsequently, the interaction effects between two adjacent cracks were evaluated to screen them. Plastic collapse tests for the plate with two parallel through-wall cracks and finite element analyses were performed to determine the optimum plastic collapse model. By comparing the test results with the prediction results obtained from the candidate models, a COD base model was selected as an optimum model.

• Journal of Applied Reliability
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• v.16 no.2
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• pp.98-103
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• 2016

Purpose: The purpose of this paper is to get a meaningful information for improving manufacturing quality of the products before they are produced in client's manufacturing process. Methods: A variety of data mining techniques have been being used for wide range of industries from process data in manufacturing factories for quality improvement. One application of those is to get meaningful information from process data in manufacturing factories for quality improvement. In this paper, the failure rate at client's manufacturing process is predicted by using the parameters of the characteristics of the product based on PCA (Principle Component Analysis) and regression analysis. Results: Through a case study, we proposed the predicting methodology and regression model. The proposed model is verified through comparing the failure rates of actual data and the estimated value. Conclusion: This study can provide the guidance for predicting the failure rate on the manufacturing process. And the manufacturers can prevent the defects by confirming the factor which affects the failure rate.

• 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 Visualization
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• v.19 no.1
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• pp.62-67
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• 2021

Selective catalytic reduction(SCR) is an exhaust gas reduction device to remove nitro oxides (NOx). SCR operation of ship can be controlled through valves for minimizing economic loss from SCR. Valve in SCR-high pressure (HP) system is directly connected to engine exhaust and operates in high temperature and high pressure. Long-term thermal deformation induced by engine heat weakens the sealing of the valve, which can lead to unexpected failures during ship sailing. In order to prevent the unexpected failures due to long-term valve thermal deformation, a failure prediction system using autoregressive integrated moving average (ARIMA) was proposed. Based on the heating experiment, virtual data mimicking temperature range around the SCR-HP valve were produced. By detecting abnormal temperature rise and fall based on the short-term ARIMA prediction, an algorithm determines whether present temperature data is required for failure prediction. The signal processed by the data collection algorithm was interpolated for the failure prediction. By comparing mean average error (MAE) and root mean square error (RMSE), ARIMA model and suitable prediction instant were determined.

• Journal of the Korean Society of Systems Engineering
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• v.16 no.1
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• pp.9-17
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• 2020

Currently, Storage Reliability is analyzed when predicting the reliability of guided missile. However, Mission Reliability and Logistics Reliability should be analyzed according to the definition of reliability in MIL-STD-785B. Therefore, it is necessary to accurately predict the reliability of guided missile based on the definition of reliability. In this paper, we proposed improved the reliability procedure and model for guided missile based on which the definition of reliability considering the mission profile. The proposed model can calculate the final failure rate by applying the ratio of the dormant and storage according to the mission profile. The proposed model has been confirmed to be more accurate than the existing model compared to the actual failure rate value. The results of this study can be useful for applying the reliability prediction to any guided missile.

• Journal of KIISE
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• v.42 no.12
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• pp.1551-1560
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• 2015

Software Reliability Growth Models (SRGMs) are used to quantitatively evaluate software reliability and to determine the software release date or additional testing efforts using software failure data. Because a single SRGM is not universally applicable to all kinds of software, the selection of an optimal SRGM suitable to a specific case has been an important issue. The existing methods for SRGM selection assess the goodness-of-fit of the SRGM in terms of the collected failure data but do not consider the accuracy of future failure predictions. In this paper, we propose a method for selecting SRGMs using the trend of failure data and failure prediction ability. To justify our approach, we identify problems associated with the existing SRGM selection methods through experiments and show that our method for selecting SRGMs is superior to the existing methods with respect to the accuracy of future failure prediction.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.3
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• pp.283-290
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• 2014

In the flat panel display industry, to meet production target quantities and the deadline of production, the scheduler and dispatching systems are major production management systems which control the order of facility production and the distribution of WIP (Work In Process). Especially the delivery time is a key factor of the dispatching system for the time when a lot can be supplied to the facility. In this paper, we use survival analysis methods to identify main factors of the delivery time and to build the delivery time forecasting model. To select important explanatory variables, the cox proportional hazard model is used to. To make a prediction model, the accelerated failure time (AFT) model was used. Performance comparisons were conducted with two other models, which are the technical statistics model based on transfer history and the linear regression model using same explanatory variables with AFT model. As a result, the mean square error (MSE) criteria, the AFT model decreased by 33.8% compared to the statistics prediction model, decreased by 5.3% compared to the linear regression model. This survival analysis approach is applicable to implementing the delivery time estimator in display manufacturing. And it can contribute to improve the productivity and reliability of production management system.

• Composites Research
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• v.18 no.1
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• pp.1-9
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• 2005

A methodology is presented for the failure prediction of composite single-lap bonded joints considering both of composite adherend failure and bondline failure. An elastic-perfectly plastic model of adhesive and a delamination failure criterion are used in the methodology. The failure predictions have been performed using finite element method and the proposed methodology. The failure prediction results such as failure mode and strength have very good agreements with the test results of joint specimens with various bonding methods and parameters. The influence of variations in the effective strength (that is, adhesion performance) and plastic behavior of adhesive on the failure characteristics of composite bonded Joints are investigated numerically. The numerical results show that optimal joint strength is archived when adhesive and delamination failure occur in the same time.