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초록·키워드 목차

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method. #Reliability Analysis(신뢰성 분석) #Input Variable Uncertainty(입력변수 불확실성) #Metamodel Uncertainty(근사모델 불확실성) #Bayesian Approach(베이지안 접근법) #Markov Chain Monte Carlo(마코프체인몬테카를로), Reliability Based Design Optimization(신뢰성 기반 최적설계)

Abstract
1. 서론
2. 베이지안 접근법
3. 입력변수 및 근사모델 불확실성
4. 설계 문제에의 적용
5. 토의
6. 결론
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