Conservative methods for structural optimization

Bennett, James A.; Lust, R. V.

doi:10.2514/3.25243

Cited by 15 publications

(5 citation statements)

References 5 publications

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“…In the past, conservative designs were generated by adding a factor of safety to constraints/variables [17]. In recent years, more involved research has focused on development of approaches to quantify and identify robust optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Six-Sigma Robust Design Optimization Using a Many-Objective Decomposition-Based Evolutionary Algorithm

Asafuddoula

Singh

Ray

2015

IEEE Trans. Evol. Computat.

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Robust design optimization aims to find solutions that are competent and reliable under given uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions etc.), this study focuses on problems where uncertainties emanate from design variables. In commercial designs, being reliable is often of more practical value than being globally best (but unreliable). Robust optimization poses three key challenges (a) appropriate formulation of the problem, (b) accurate estimation of the 'robustness' measure and (c) efficient means to identify the set of tradeoff robust solutions with an affordable computational cost. In this paper, four different problem formulations for robust optimization are presented and analyzed. The proposed formulations offer a set of tradeoff solutions with robustness from two perspectivesfeasibility robustness, i.e., robustness against failure and performance robustness, i.e., robustness assuring good performance. The approach also provides means to identify critical constraints or performance functions that affect the overall robustness. The problem is posed as a many-objective optimization problem and a decomposition based evolutionary approach is used for solving it. The performance of the proposed approach and the consequences of using different formulations are illustrated using two numerical examples and four engineering problems.

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Section: Introductionmentioning

confidence: 99%

Six-Sigma Robust Design Optimization Using a Many-Objective Decomposition-Based Evolutionary Algorithm

Asafuddoula

Singh

Ray

2015

IEEE Trans. Evol. Computat.

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“…In order to obtain robust MEMS structures considering uncertainties, the design technique of robust optimization should be applied from the early stage of structural design. A number of researchers have applied robust optimal design concepts to mechanical structures [6][7][8][9][10][11][12][13][14]. In the multiobjective optimization [7,8] with the mean and standard deviation of original objective function, constraints are supplemented by adding penalty terms to original constraints for the feasibility robustness.…”

Section: Introductionmentioning

confidence: 99%

“…In the multiobjective optimization [7,8] with the mean and standard deviation of original objective function, constraints are supplemented by adding penalty terms to original constraints for the feasibility robustness. Other approaches are to add a padding term to each constraint [9] or to consider worst-case manufacturing tolerances [10]. Methods based on Taguchi's robust design concept have been also proposed [11].…”

Section: Introductionmentioning

confidence: 99%

Robust optimal design of a vibratory microgyroscope considering fabrication errors

Han¹,

Kwak²

2001

J. Micromech. Microeng.

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A robust optimal design of shape and size is formulated for vibratory microgyroscopes that can reduce the effect of variations from uncertainties in microelectromechanical systems fabrication. The important objective in the design of vibratory microgyroscopes is to reduce the difference between the resonance frequencies of the vertical (detecting) and lateral (driving) modes in order to attain high mechanical detecting sensitivity. The deterministic optimization for this goal results in good performance but is sensitive to fabrication errors. The basic idea of the present formulation is to obtain robustness of the objective function by minimizing the gradient of the objective function with respect to uncertain variables through a proper selection of shapes and sizes. The beam width, length and thickness of vibratory microgyroscopes are adopted as design variables and are simultaneously regarded as uncertain variables in the optimization problems. A robustness check using a newly defined yield through the Monte Carlo simulation has shown that the robust optimal design obtained has generated twice the number of acceptable designs than the deterministic optimum. The important point is that the formulation of minimizing the maximum sensitivity of the objective function requires no statistical information on the uncertainties and yet achieves robustness.

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“…Conforme Bennet (1990) alguns autores propõem a avaliação da robustez das soluçõeś otimas apenas ao final do processo de otimização, istoé, avaliam a sensibilidade dos objetivos e restrições ao perturbar uma soluçãoótima. Porém o uso desta estratégia requer o conhecimento das funções envolvidas no processo.…”

Section: Robustounclassified

Otimização robusta multiobjetivo para o projeto de sistemas em Engenharia

Moreira¹

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Conservative methods for structural optimization

Cited by 15 publications

References 5 publications

Six-Sigma Robust Design Optimization Using a Many-Objective Decomposition-Based Evolutionary Algorithm

Six-Sigma Robust Design Optimization Using a Many-Objective Decomposition-Based Evolutionary Algorithm

Robust optimal design of a vibratory microgyroscope considering fabrication errors

Otimização robusta multiobjetivo para o projeto de sistemas em Engenharia

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