An importance learning method for non-probabilistic reliability analysis and optimization

Meng, Zeng; Zhang, Dequan; Li, Gang; Yu, Bo

doi:10.1007/s00158-018-2128-7

Cited by 103 publications

(19 citation statements)

References 65 publications

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“…In this article, to improve the efficiency of HRBDO-RI in the cases with time-consuming constraints, each true constraint is replaced by the Kriging metamodel, which is widely used in the field of structural optimization. 60,[67][68][69] The brief review about the basic theory of Kriging is given in Appendix. From Equation 7, it can be seen that the separate surface of I U F (X) = 0 and I U F (X) = 1 is g min (X) = 0, which is the projection bound in X space.…”

Section: Projection-outline-based Active Learning Krigingmentioning

confidence: 99%

A new hybrid reliability‐based design optimization method under random and interval uncertainties

Zhang

Gao

Xiao

2020

Numerical Meth Engineering

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Summary This article proposes a new method for hybrid reliability‐based design optimization under random and interval uncertainties (HRBDO‐RI). In this method, Monte Carlo simulation (MCS) is employed to estimate the upper bound of failure probability, and stochastic sensitivity analysis (SSA) is extended to calculate the sensitivity information of failure probability in HRBDO‐RI. Due to a large number of samples involved in MCS and SSA, Kriging metamodels are constructed to substitute true constraints. To avoid unnecessary computational cost on Kriging metamodel construction, a new screening criterion based on the coefficient of variation of failure probability is developed to judge active constraints in HRBDO‐RI. Then a projection‐outline‐based active learning Kriging is achieved by sequentially select update points around the projection outlines on the limit‐state surfaces of active constraints. Furthermore, the prediction uncertainty of Kriging metamodel is quantified and considered in the termination of Kriging update. Several examples, including a piezoelectric energy harvester design, are presented to test the accuracy and efficiency of the proposed method for HRBDO‐RI.

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Section: Projection-outline-based Active Learning Krigingmentioning

confidence: 99%

A new hybrid reliability‐based design optimization method under random and interval uncertainties

Zhang

Gao

Xiao

2020

Numerical Meth Engineering

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“…As can be seen from the FLC forming limit diagram of the fender, the surface of the fender is green, which indicates that the material thickness change rate of each part of the finished fender is within the safe range after the deep drawing process [16]. According to the structural characteristics of the fender and its assembly mode, take investigation points at the corners of step surfaces and the installation places of headlights where quality problems such as sliding and cracking are easy to occur.…”

Section: Fender Performance Testmentioning

confidence: 99%

Study on Simulation of Stamping Process and Optimization of Process Parameters of Fender

Wang

Wan

2019

Advances in Materials Science and Engineering

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In order to obtain the optimum technological parameters of fender forming, the finite element analysis software DYNAFORM is used to simulate the drawing process of fender in this paper. The orthogonal test is designed and completed. On the basis of the orthogonal test, Taguchi method is used to design the orthogonal inner table, and the SNR of each factor at each level is calculated. The results of Taguchi method are analyzed with variance analysis, and the adjustable influencing variables and influence factors on quality stability that affect the forming quality of the fender are obtained. Experimental optimization of the adjustable variables is carried out again to obtain the optimal combination of process parameters that affect the shaping quality of the fender. The optimal combination of process parameters is as follows: blank holder force is 120 kN, model clearance is 1.0 t (t represents sheet thickness), friction coefficient is 0.11, and radius of die is 6 mm. Finally, the experimental results show that the fender manufactured with this combination of process parameters has better performance.

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“…Meng et al presented chaos control method to performance measure approach for the inverse reliability analysis, and the robustness and efficiency were increased obviously . Recently, improved HL‐RF, stability transformation method, directional stability transformation method, adaptive finite step length, hybrid relaxed HL‐RF, conjugate HL‐RF, limited and enriched Fletcher‐Reeves method, chaos control, adaptive and relaxed conjugate FORM, and hybrid conjugate FORM were developed to increase the robustness and efficiency of FORM during the process of searching the most probable failure point and the reliability index.…”

Section: Introductionmentioning

confidence: 99%

Structural reliability assessment by salp swarm algorithm–based FORM

Zhong

Wang

Dang

et al. 2020

Quality & Reliability Eng

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The first-order reliability method (FORM) is well recognized as an efficient approach for reliability analysis. Rooted in considering the reliability problem as a constrained optimization of a function, the traditional FORM makes use of gradient-based optimization techniques to solve it. However, the gradient-based optimization techniques may result in local convergence or even divergence for the highly nonlinear or high-dimensional performance function. In this paper, a hybrid method combining the Salp Swarm Algorithm (SSA) and FORM is presented. In the proposed method, a Lagrangian objective function is constructed by the exterior penalty function method to facilitate meta-heuristic optimization strategies. Then, SSA with strong global optimization ability for highly nonlinear and high-dimensional problems is utilized to solve the Lagrangian objective function. In this regard, the proposed SSA-FORM is able to overcome the limitations of FORM including local convergence and divergence. Finally, the accuracy and efficiency of the proposed SSA-FORM are compared with two gradient-based FORMs and several heuristic-based FORMs through eight numerical examples. The results show that the proposed SSA-FORM can be generally applied for reliability analysis involving low-dimensional, high-dimensional, and implicit performance functions. KEYWORDS first-order reliability method, high-dimensional problem, meta-heuristic algorithm, reliability analysis, Salp Swarm Algorithm 1224

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An importance learning method for non-probabilistic reliability analysis and optimization

Cited by 103 publications

References 65 publications

A new hybrid reliability‐based design optimization method under random and interval uncertainties

A new hybrid reliability‐based design optimization method under random and interval uncertainties

Study on Simulation of Stamping Process and Optimization of Process Parameters of Fender

Structural reliability assessment by salp swarm algorithm–based FORM

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