• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.345-348
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• 2011

구조요소의 설계에서 유한요소해석은 매우 효과적인 방법이다. 이 방법은 시험 수행에 드는 시간과 비용을 줄여준다. 그러나 공정 과정과 환경에 의하여 생기는 입력 물성치들의 변화 때문에 우리는 유한요소해석의 결과를 전적으로 믿어서는 안 된다. 따라서 유한요소해석의 신뢰성을 증명하는 것은 매우 중요하다. 본 연구에서는 현장에 축적된 피로 수명 시험 데이터를 바탕으로 유한요소해석을 이용하여 피로수명 파라미터를 역 추정 하는 연구를 수행하였다. 베이지안 접근법을 이용하여 불확실성 피로 수명 파라미터의 사후분포를 구하였고, 마코프체인몬테카를로(Markov Chain Monte Carlo) 기법을 이용하여 역 추정된 파라미터의 샘플 데이터를 생성하였다. 얻어진 샘플 데이터를 기반으로 새로운 형상의 스프링에 대한 피로 수명을 예측한다. 신뢰성 기반 형상 최적화(RBDO)는 서스펜션 코일 스프링의 요구수명을 만족시키기 위하여 수행된다. 또한 크리깅 근사 모델은 유한요소해석의 연산 량 감소를 위해 이용한다.

• Journal of Ship and Ocean Technology
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• v.9 no.1
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• pp.64-71
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• 2005

In probabilistic design, the challenge is to estimate the uncertainty propagation, since outputs of subsystems at lower levels could constitute inputs of other systems or at higher levels of the multilevel systems. Three uncertainty propagation estimation techniques are compared in this paper in terms of numerical efficiency and accuracy: root sum square (linearization), distribution-based moment approximation, and Taguchi-based integration. When applied to reliability-based design optimization (RBDO) under uncertainty, it is investigated which type of applications each method is best suitable for. Two nonlinear analytical examples and one vehicle crashworthiness for side-impact simulation example are employed to investigate the unique features of the presented techniques for uncertainty propagation. This study aims at helping potential users to identify appropriate techniques for their applications in the multilevel design.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1082-1089
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• 2011

Most engineered systems are designed with high levels of system redundancies to satisfy required reliability requirements under adverse events, resulting in high systems' LCCs (Life-Cycle Costs). Recent years have seen a surge of interest and tremendous advance in PHM (Prognostics and Health Management) methods that detect, diagnose, and predict the effects of adverse events. The PHM methods enable proactive maintenance decisions, giving rise to adaptive reliability. In this paper, we present a RAP (Resilience Allocation Problem) whose goal is to allocate reliability and PHM efficiency to components in an engineering context. The optimally allocated reliability and PHM efficiency levels serve as the design specifications for the system RBDO (Reliability-Based Design Optimization) and the system PHM design, which can be used to derive the detailed design of components and PHM units. The RAP is demonstrated using a simplified aircraft control actuator design problem resulting in a highly resilient actuator with optimally allocated reliability, PHM efficiency and redundancy for the given parameter settings.

• International Journal of Precision Engineering and Manufacturing
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• v.5 no.4
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• pp.61-68
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• 2004

Necessity to address engineering system uncertainties in design processes has long been acknowledged. To obtain quality of product, a safety factor is traditionally used by many design engineers due to its easy of use and comprehension. However, the safety factor approach often yields either conservative or unreliable designs, since it ignores the type of probability distribution and the mechanism of uncertainty propagation from the input to the output. For a consistent reliability-based design, two fundamental issues must be investigated thoroughly. First, the design-decision process that clearly identifies a mechanism of uncertainty propagation under system uncertainties needs to be developed, which must be an efficient and accurate process. To identify the mechanism more effectively, an adaptive probability analysis is proposed by adaptively setting probability levels through a posteriori error estimation. The second is to develop the design process that not only yields a high quality design but also a cost-effective optimum design from manufacturing point of view. As a result, a response surface methodology is specially developed for RBDO, thus enhancing numerical challenges of efficiency and complicatedness. Side crashworthiness application is used to demonstrate the integrated design process for product and manufacturing process design.

• Journal of the Korean Society of Safety
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• v.27 no.6
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• pp.115-121
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• 2012

This paper, being the second in a two-part series, presents the robust performance of the proposed design method which can enhance a reliability-based design optimization(RBDO) under the uncertainties of probabilistic models. The robust performances of the solutions obtained by the proposed method, described in the Part 1, are investigated through the parametric studies. A 10-bar truss example is considered, and the uncertain parameters include the number of data observed, and the variations of applied loadings and allowable stresses. The numerical results show that the proposed method can produce a consistent result despite of the large variations in the parameters. Especially, even with the relatively small data set, the analysis results show that the exact probabilistic model can be successfully predicted with optimized design sections. This consistency of estimating appropriate probability model is also observed in the case of the variations of other parameters, which verifies the robustness of the proposed method.

• Journal of the Korean Society of Safety
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• v.27 no.5
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• pp.148-157
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• 2012

Reliability-based design optimization (RBDO) problem is usually formulated as an optimization problem to minimize an objective function subjected to probabilistic constraint functions which may include deterministic design variables as well as random variables. The challenging task is that, because the probability models of the random variables are often assumed based on limited data, there exists a possibility of selecting inappropriate distribution models and/or model parameters for the random variables, which can often lead to disastrous consequences. In order to select the most appropriate distribution model from the limited observation data as well as model parameters, this study takes into account a set of possible candidate models for the random variables. The suitability of each model is then investigated by employing performance and risk functions. In this regard, this study enables structural design optimization and fitness assessment of the distribution models of the random variables at the same time. As the first paper of a two-part series, this paper describes a new design method considering probability model uncertainties. The robust performance of the proposed method is presented in Part 2. To demonstrate the effectiveness of the proposed method, an example of ten-bar truss structure is considered. The numerical results show that the proposed method can provide the optimal design variables while guaranteeing the most desirable distribution models for the random variables even in case the limited data are only available.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.2
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• pp.100-106
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• 2014

Lower control arm (LCA) is a part of chassis in automotive. Performances of LCA such as stiffness, durability and permanent displacement must be considered in design optimization. However it is hard to consider different performances at once in optimization because these are measured by different commercial tools like Radioss, Abaqus, etc. In this paper, firstly, we construct the integrated design automation system for LCA based on Matlab including Hypermesh, Radioss and Abaqus. Secondly, Akaike information criterion (AIC) is used for assessment of reliability of LCA. It can find the best estimated distribution of performance from limited and discrete stochastic information and then obtains the reliability from the distribution. Finally, we consider tolerances of design variables and variation of elastic modulus and achieve the target reliability by carrying out reliability-based design optimization (RBDO) with the integrated system.

• Journal of the Korea Convergence Society
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• v.13 no.4
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• pp.287-292
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• 2022

In this paper, the stochastic distribution of the springback amount is investigated for the stamping process of a U-channel shaped-product with ultra-high strength steel. Using the reliability-based design optimization technique (RBDO), stochastic distribution of process parameters is considered in the analysis including material properties and process variation. Quantification of the springback scatters is carried out with the statistical analysis method according to the material strength. It is found that the scattering amount of springback decreases while the amount of springback increases as the tensile strength of the blank material increases, which is investigated by analyzing the strain and stress distribution of the punch and die shoulder. It is noted that the proposed scheme is capable of predicting and responding to the unavoidable scattering of springback in the sheet metal forming process.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.2
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• pp.125-130
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• 2011

Since conventional optimization that is classified as a deterministic method does not consider the uncertainty involved in a modeling or manufacturing process, an optimum design is often determined to be on the boundaries of the feasible region of constraints. Reliability-based design optimization is a method for obtaining a solution by minimizing the objective function while satisfying the reliability constraints. This method includes an optimization process and a reliability analysis that facilitates the quantization of the uncertainties related to design variables. Moment-based reliability analysis is a method for calculating the reliability of a system on the basis of statistical moments. In general, on the basis of these statistical moments, the Pearson system estimates seven types of distributions and determines the reliability of the system. However, it is technically difficult to practically consider the Pearson Type IV distribution. In this study, we propose an enhanced Pearson Type IV distribution based on a kriging model and validate the accuracy of the enhanced Pearson Type IV distribution by comparing it with a Monte Carlo simulation. Finally, reliability-based design optimization is performed for a system with type IV distribution by using the proposed method.