An approximate sequential optimization and reliability assessment method for reliability-based design optimization

Yi, Ping; Zhu, Zuo; Gong, Jinxin

doi:10.1007/s00158-016-1478-2

Cited by 44 publications

(16 citation statements)

References 22 publications

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“…Step 5: Output the minimum reliability R i min calculated by (5) based on the minimum reliability index β i min calculated by (3) if (14) and (15) are satisfied. Otherwise, increase k by 1 and go to Step 2.…”

Section: Algorithm 1 the Proposed Ass-hra For Structures With Random mentioning

confidence: 99%

“…The RBDO problems of engineering structures are usually described as either probabilistic or non-probabilistic models [9]- [11]. Specifically, uncertain parameters are usually described as random variables or random fields based on the theory of probability and statistics when the sample data of uncertain factors are abundant [12]- [14], and then the failure probability of an engineering structure can be precisely calculated based on reliability analysis [15], [16]. The probabilistic reliability analysis has two characteristics: 1) It depends on the probability density function (PDF) which relies on a large amount of statistical data; 2) It is very sensitive to the variations of the parameters' probabilistic distributions.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Reliability-Based Design Optimization of Complex Structures With Random and Interval Uncertainties Based on ASS-HRA

Cheng

Lü

et al. 2019

IEEE Access

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In this paper, an efficient hybrid reliability analysis (HRA) method and a hybrid reliability-based design optimization (HRBDO) approach are proposed for realistic complex engineering structures with random and interval uncertainties. First, the HRBDO model for complex engineering structures is constructed with its objective and performance functions described as the implicit functions of design variables and random and interval parameters. Then, an efficient HRA method based on adaptive step size (ASS-HRA) is put forward to calculate the minimum reliability of the structure's performance function under the influences of both random and interval uncertainties, the computational efficiency and accuracy of which are verified by a benchmark test. Subsequently, an efficient HRBDO approach integrating the proposed ASS-HRA method with the polynomial response surface model (PRSM) is developed for solving the HRBDO problems of complex engineering structures, the effectiveness of which is demonstrated by a numerical example. Finally, the HRBDO of a high-speed press slider demonstrates the efficiency, effectiveness, and versatility of the proposed HRBDO approach based on the ASS-HRA in the design of realistic complex engineering structures. INDEX TERMS Hybrid reliability analysis (HRA), adaptive step size (ASS), hybrid reliability-based design optimization (HRBDO), random and interval uncertainties, complex structure.

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Section: Algorithm 1 the Proposed Ass-hra For Structures With Random mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Reliability-Based Design Optimization of Complex Structures With Random and Interval Uncertainties Based on ASS-HRA

Cheng

Lü

et al. 2019

IEEE Access

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“…Each element of the matrices can be collected near the mean value of each input, output, and effective design parameters. The kinematic output error can be deduced from equation (4) as…”

Section: @Fmentioning

confidence: 99%

“…1 In conventional optimal design process, 2,3 the uncertainty of design variables in both objective functions and constraints is seldom considered, which leads to unacceptable reliability of the design outcomes. With the development of theories of reliability design [4][5][6][7] and robust design, [8][9][10] the reliability and robustness of products have been consistently improved. While as for a rational design process of complex products, one of the most important issues is the multiobjective optimization problem, in which the uncertainty of design objectives will propagate with the coupled relationship in among them, causing both interactions and conflicts of the design objectives.…”

Section: Introductionmentioning

confidence: 99%

Reliability-based robust design for kinematic accuracy of a mechanism with axiomatic theory

Qui

Zhang

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, a reliability-based robust design approach was proposed on the basis of axiomatic theory, aiming at actualizing a reliability-based robust design framework for mechanism motion. First, reliability constraint functions of the motion output error of mechanisms were established by using cumulative distribution method after an accuracy analysis of mechanism motion. Second, the number of effective design parameters was equated to the number of functional requirements by classifying design parameters using sensitivity analysis or constructing additional objective functions based on axiomatic theory, and then the independence axiom was satisfied by establishing objective functions according to regularity and the semangularity. Third, a reliability-based robust design model of mechanisms was established based on the axiomatic theory according to the above objective functions and constraint functions. Taking a planar four bar linkage as an example, a reliability-based robust design was achieved with the optimization and symbol toolbox in MATLAB. Meanwhile, correctness and effectiveness of the proposed method was verified under the condition that the first and second moments of basic random parameters were available. Finally, the method proposed in this paper was applied to the reliability-based robust design of an initiative lock mechanism in space docking latch system.

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“…Due to the high efficiency of SORA framework, a series of important variants are further developed to enhance the performance in terms of efficiency, [18][19][20][21][22] accuracy, 23,24 or convergence. [25][26][27] In the above RBDO methods, the efficiency is improved by employing FROM, but the accuracy may be not enough because of the limitation of FORM. 28,29 In the FORM, each non-normal variable is required to be transformed into the standard normal variable, and the performance function is linearized at MPP in the standard normal space.…”

Section: Introductionmentioning

confidence: 99%

A decoupling algorithm with first-order asymptotic integration for reliability-based design optimization

Huang

Yang

2018

Advances in Mechanical Engineering

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Conventional decoupling approaches usually employ first-order reliability method to deal with probabilistic constraints in a reliability-based design optimization problem. In first-order reliability method, constraint functions are transformed into a standard normal space. Extra non-linearity introduced by the non-normal-to-normal transformation may increase the error in reliability analysis and then result in the reliability-based design optimization analysis with insufficient accuracy. In this article, a decoupling approach is proposed to provide an alternative tool for the reliability-based design optimization problems. To improve accuracy, the reliability analysis is performed by first-order asymptotic integration method without any extra non-linearity transformation. To achieve high efficiency, an approximate technique of reliability analysis is given to avoid calculating time-consuming performance function. Two numerical examples and an application of practical laptop structural design are presented to validate the effectiveness of the proposed approach.

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An approximate sequential optimization and reliability assessment method for reliability-based design optimization

Cited by 44 publications

References 22 publications

Hybrid Reliability-Based Design Optimization of Complex Structures With Random and Interval Uncertainties Based on ASS-HRA

Hybrid Reliability-Based Design Optimization of Complex Structures With Random and Interval Uncertainties Based on ASS-HRA

Reliability-based robust design for kinematic accuracy of a mechanism with axiomatic theory

A decoupling algorithm with first-order asymptotic integration for reliability-based design optimization

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