• 한국산업융합학회 논문집
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• 제23권5호
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• pp.799-807
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• 2020

This paper deals with identification of probabilistic design using reliability based robust optimization in structure design of automatic salt collector. The thickness sizing variables of main structure member in the automatic salt collector were considered the random design variables including the uncertainty of corrosion that would be an inevitable hazardousness in the saltern work environment. The probabilistic constraint functions were selected from the strength performances of the automatic salt collector. The reliability based robust optimum design problem was formulated such that the random design variables were determined by minimizing the weight of the automatic salt collector subject to the probabilistic strength performance constraints evaluating from reliability analysis. Mean value reliability method and adaptive importance sampling method were applied to the reliability evaluation in the reliability based robust optimization. The three sigma level quality was considered robustness in side constraints. The probabilistic optimum design results according to the reliability analysis methods were compared to deterministic optimum design results. The reliability based robust optimization using the mean value reliability method showed the most rational results for the probabilistic optimum structure design of the automatic salt collector.

• 한국해양공학회지
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• 제26권6호
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• pp.80-85
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• 2012

The methods of robust design optimization (RDO) and reliability-based robust design optimization (RBRDO) were implemented in the present study. RBRDO is an integrated method that accounts for the design robustness of an objective function and for the reliability of constraints. The objective function in RBRDO is expressed in terms of the mean and standard deviation of an original objective function. Thus, a multi-objective formulation is employed. The regressive approximate models are generated via the moving least squares method (MLSM) and constraint-feasible moving least squares method (CF-MLSM), which make it possible to realize the feasibility regardless of the multimodality/nonlinearity of the constraint function during the approximate optimization processes. The regression model based RBRDO is newly devised and its numerical characteristics are explored using the design of an actively controlled ten bar truss structure.

• Journal of Mechanical Science and Technology
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• 제20권10호
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• pp.1662-1669
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• 2006

Since randomness and uncertainties of design parameters are inherent, the robust design has gained an ever increasing importance in mechanical engineering. The robustness is assessed by the measure of performance variability around mean value, which is called as standard deviation. Hence, constraints in robust optimization problem can be approached as probability constraints in reliability based optimization. Then, the FOSM (first order second moment) method or the AFOSM (advanced first order second moment) method can be used to calculate the mean values and the standard deviations of functions describing constraints and object. Among two methods, AFOSM method has some advantage over FOSM method in evaluation of probability. Nevertheless, it is difficult to obtain the mean value and the standard deviation of objective function using AFOSM method, because it requires that the mean value of function is always positive. This paper presented a special technique to overcome this weakness of AFOSM method. The mean value and the standard deviation of objective function by the proposed method are reliable as shown in examples compared with results by FOSM method.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.870-875
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• 2004

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, the robust design strategy is developed based on the DACE and the global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the system. The robustness is determined by the DACE model to reduce the real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 대한기계학회논문집A
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• 제29권9호
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• pp.1243-1252
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• 2005

A current trend of design methodologies is to make engineers objectify or automate the decision-making process. Numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, the Taguchi method, reliability-based optimization and robust optimization are being used. To obtain the target performance with the maximum robustness is the main functional requirement of a mechanical system. In this research, a design procedure for global robust optimization is developed based on the kriging and global optimization approaches. The DACE modeling, known as the one of Kriging interpolation, is introduced to obtain the surrogate approximation model of the function. Robustness is determined by the DACE model to reduce real function calculations. The simulated annealing algorithm of global optimization methods is adopted to determine the global robust design of a surrogated model. As the postprocess, the first order second-moment approximation method is applied to refine the robust optimum. The mathematical problems and the MEMS design problem are investigated to show the validity of the proposed method.

• 한국항공우주학회지
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• 제34권8호
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• pp.24-32
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• 2006

결정론적인 최적 설계 방법을 이용하는 경우 불확실성의 영향으로 인하여 제약조건의 위반이나 목표 성능의 저하를 초래할 수 있다. 이러한 까닭에 불확실성하에서 제약 조건에 대한 신뢰성을 보장하고 목적함수의 강건성을 확보하는 설계가 필요하다. 그러므로 본 연구에서는 강건성과 신뢰성을 평가하기 위하여 Monte Carlo Simulation(MCS)을 이용하였으며, 계산 효율의 증가를 위하여 MCS에 적합한 근사모델을 선정하는 과정을 거쳐 신경망 모델을 채택하게 되었다. 이를 공력-구조가 연동된 항공기 날개 설계 문제에 적용하여 봄으로써 그 가능성을 타진하였다. 불확실성을 고려한 최적 설계를 수행한 결과 요구되는 신뢰도 수준을 만족시키면서 baseline보다 강건한 최적해를 탐색하는 것이 가능하였다.

• 한국안전학회지
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• 제27권6호
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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.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2003년도 추계학술대회
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• pp.1763-1768
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• 2003

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. Based on the independence axiom of axiomatic design theory that illustrates the relationship between desired specifications and design parameters, the designs can be classified into three types: uncoupled, decoupled and coupled. To best approach the target performance with the maximum robustness is one of the main functional requirements of a mechanical system. Most engineering designs are pertaining to either coupled or decoupled ones, but these designs cannot currently accomplish a real robustness thus a trade-off between performance and robustness has to be made. In this research, the game theory will be applied to optimize the trade-off.

• 대한기계학회논문집A
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• 제24권1호
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• pp.215-224
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• 2000

In order to reduce the variation effects of uncertainties in the engineering environments, new robust optimization method, which considers the uncertainties in design process, is proposed. Both design variables and system parameters are considered as random variables about their nominal values. To ensure the robustness of performance function, a new objective is set to minimize the variance of that function. Constraint variations are handled by introducing probability constraints. Probability constraints are solved by the advanced first order second moment (AFOSM) method based on the reliability theory. The proposed robust optimization method has an advantage that the second derivatives of the constraints are not required. The suggested method is examined by solving three examples and the results are compared with those for deterministic case and those available in literature.

• 한국기계가공학회지
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• 제7권2호
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• pp.8-15
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• 2008

Current trend of design technologies shows engineers to objectify or automate the given decision-making process. The numerical optimization is an example of such technologies. However, in numerical optimization, the uncertainties are uncontrollable to efficiently objectify or automate the process. To better manage these uncertainties, Taguchi method, reliability-based optimization and robust optimization are being used. Based on the independence axiom of axiomatic design theory that illustrates the relationship between desired specifications and design parameters, the designs can be classified into three types: uncoupled, decoupled and coupled. To best approach the target performance with the maximum robustness is one of the main functional requirements of a mechanical system. Most engineering designs are pertaining to either coupled or decoupled ones, but these designs cannot currently accomplish a real robustness thus a trade-off between performance and robustness has to be made. In this research, the game theory will be applied to optimize the trade-off.