• Journal of the Korean Ceramic Society
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• v.31 no.10
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• pp.1141-1146
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• 1994

Cyclic fatigue experiment was carried out to predict the life time of alumina ceramics. Four kinds of model were suggested to obtain the adequate representative static stress corresponding to the cyclic stress applied to the alumina specimens. Arithmetic mean stress model gives 21.81 of the crack growth exponent, integrated stress model gives 22.15, maximum stress model gives 24.57, and equivalent static stress model gives 24.43. It is considered that the equivalent static stress model is the most reasonable and gives the best adequate crack growth exponents value.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2949-2957
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• 2020

Fatigue crack growth model has been developed for dissimilar metal weld joints of a piping component under cyclic loading, where in the crack is located at the center of the weld in the circumferential direction. The fracture parameter, Stress Intensity Factor (SIF) has been computed by using principle of superposition as K_H + K_M. K_H is evaluated by assuming that, the complete specimen is made of the material containing the notch location. In second stage, the stress field ahead of the crack tip, accounting for the strength mismatch, the applied load and geometry has been characterized to evaluate SIF (K_M). For each incremental crack depth, stress field ahead of the crack tip has been quantified by using J-integral (elastic), mismatch ratio, plastic interaction factor and stress parallel to the crack surface. The associated constants for evaluation of K_M have been computed by using the quantified stress field with respect to the distance from the crack tip. Net SIF (K_H + K_M) computed, has been used for the crack growth analysis and remaining life prediction by Paris crack growth model. To validate the model, SIF and remaining life has been predicted for a pipe made up of (i) SA312 Type 304LN austenitic stainless steel and SA508 Gr. 3 Cl. 1. Low alloy carbon steel (ii) welded SA312 Type 304LN austenitic stainless-steel pipe. From the studies, it is observed that the model could predict the remaining life of DMWJ piping components with a maximum difference of 15% compared to experimental observations.

• Journal of the Korean Society of Safety
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• v.21 no.1 s.73
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• pp.28-34
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• 2006

A procedure of estimating failure probability is demonstrated for a pressurized pipe of CrMo steel used at $538^{\circ}C$. Probabilistic fracture mechanics were employed considering variations of pressure loading, material properties and geometry. Probability density functions of major material variables were determined by statistical analyses of implemented data obtained by previous experiments. Distributions of the major variables were reflected in Monte Carlo simulation and failure probability as a function of operating time was determined. The creep crack growth life assessed by conventional deterministic approach was shown to be conservative compared with those obtained by probabilistic one. Sensitivity analysis for each input variable was also conducted to understand the most influencing variables to the residual life analysis. Internal pressure, creep crack growth coefficient and creep coefficient were more sensitive to failure probability than other variables.

• Journal of Ocean Engineering and Technology
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• v.9 no.1
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• pp.142-151
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• 1995

A simulation program concerned with multi-surface fatigue cracks which initiated at the semi-circular surface notches has been developed to predict their growth and coalescence behaviors at the elevated temperature. Three kinds of coalescence models such as SPC(surface point connection), ASME and BSI(British Standards Institution) conditions were applied, and the results of the simulation were compared with those of the experiment. This simulation is able to enhance the reliance and integrity of structures especially under the elevated temperature which have lots of difficulties in experiments and applications. This shows that the simulation result has utility for fatigue life prediction. Even though all the specimens were the same shape, the error rate was increased in accordance with the applied stress to the specimen. Among the material constants C and m in the narrow band, the results applied upper values of the band to the simulation has shown quite small error compared with the experiment results.

• Journal of Korea Society of Industrial Information Systems
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• v.7 no.2
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• pp.33-39
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• 2002

Accelerated life testing of a car is used to get information quickly on its life distribution. Test cars are no under severe conditions and fail sooner than under usual conditions. A model is fitted to the accelerated failure times and then extrapolated to estimate the life distribution under usual conditions. This paper presents an accelerated test md the reliability growth theory, and applies it to some subsystems of cars during their prototype and pilot testing. The data presented illustrates explicitly the prediction of the reliability growth in the product development cycle. The application of these techniques is a part of the product assurance function that plays an important role in product reliability improvement.

• Transactions of the Korean Society of Automotive Engineers
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• v.4 no.4
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• pp.130-139
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• 1996

The silicon carbide particles reinforced aluminium 6061($SiCp/A\ell$) composites are generally known have wild range of applications from automobiles to airospaces. But, by the results of existing study for $SiCp/A\ell$ composites, there are reports that the fatigue life of $SiCp/A\ell$ composites has improved than $A\ell$matrixes and has not improved then $A\ell$ matrixes. Consequently, in order to perform the reliable life prediction for $SiCp/A\ell$, the properties of probability distribution of fatigue crack initiation life & fracture life, crack growth length in constant number of cycles, crack growth rate in constant stress intensity factor range and m & C value in Paris's fatigue crack growth law and the estimation of statistical parameters have been evaluated by the statistics method.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.681-702
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• 2015

Fracture analysis and remaining life prediction has been carried out for aluminium alloy (Al 2014A) plate panels with concentric stiffener by varying sizes and positions under fatigue loading. Tension coupon tests and compact tension tests on 2014A have been carried out to evaluate mechanical properties and crack growth constants. Domain integral technique has been used to compute the Stress intensity factor (SIF) for various cases. Generalized empirical expressions for SIF have been derived for various positions of stiffener and size. From the study, it can be concluded that the remaining life for stiffened panel for particular size and position can be estimated by knowing the remaining life of corresponding unstiffened panel.

• Proceedings of the KWS Conference
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• 1998.10a
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• pp.323-325
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• 1998

• Journal of the Korean Society for Precision Engineering
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• v.16 no.7
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• pp.168-177
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• 1999

Fatigue damage is the phenomena which is accumulated gradually with loading cycle in material. It is represented by fatigue crack growth rate da/dN and fatigue life ratio $N/N_{f}$. Fracture mechanical parameters estimating large crack growth behavior can calculate quantitative amount of fatigue crack growth resistance in engineering material. But fatigue damage has influence on various load, material and environment. Therefore, In this study, we propose that artificial intelligent fatigue damage model can predicts fatigue crack growth rate da/dN and fatigue life ratio $N/N_{f}$ simultaneously using fracture mechanical and nondestructive parameters.

• Smart Media Journal
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• v.11 no.11
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• pp.63-74
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• 2022

Nowadays, lithium-ion battery has become more popular around the world. Knowing when batteries reach their end of life (EOL) is crucial. Accurately predicting the remaining useful life (RUL) of lithium-ion batteries is needed for battery health management systems and to avoid unexpected accidents. It gives information about the battery status and when we should replace the battery. With the rapid growth of machine learning and deep learning, data-driven approaches are proposed to address this problem. Extracting aging information from battery charge/discharge records, including voltage, current, and temperature, can determine the battery state and predict battery RUL. In this work, we first outlined the charging and discharging processes of lithium-ion batteries. We then summarize the proposed techniques and achievements in all published data-driven RUL prediction studies. From that, we give a discussion about the accomplishments and remaining works with the corresponding challenges in order to provide a direction for further research in this area.