Enhancing product robustness in reliability-based design optimization

Zhuang, Xiaotian; Pan, Rong; Du, Xiaoping

doi:10.1016/j.ress.2015.01.026

Cited by 29 publications

(13 citation statements)

References 40 publications

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“…For general cases of the Reliability Based Design (RBD), the SSI theory plays a major role in formulating the system reliability model [10][11][12][13]. The reason that SSI theory is widely used to model the system reliability is that it is easy to represent the limit states of the components directly in terms of stress and strength.…”

Section: Introductionmentioning

confidence: 99%

“…The reason that SSI theory is widely used to model the system reliability is that it is easy to represent the limit states of the components directly in terms of stress and strength. However, the conventional SSI theory results in a static reliability model and cannot be used for designing load sharing system, since it does not reflect the relationship between lifetime and failure probability [12][13][14]. The reliability function from the conventional SSI model is derived by evaluating the joint probability density function of stress (L) and strength (S) and is given by Equation (1).…”

Section: Introductionmentioning

confidence: 99%

“…Du et al [10] developed Sequential Optimization and Reliability Analysis (SORA) decoupled loop formulation for the RBDO framework, which can handle any type of reliability analysis and optimization technique. Zhuang and Pan [11][12][13] used SORA decoupled loop approach to design individual component for a given reliability level. The shifting vector strategy worked well for designing individual components, but this method cannot handle system level reliability constraints.…”

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Design Optimization of Load Sharing Systems Using SSI-Markov Models

Subramaniyan

Pan

2019

Designs

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This paper presents a novel single loop approach to design the components of the load sharing systems by optimally allocating the failure probabilities to each component, thereby satisfying the overall system reliability requirement. The Reliability–Based Design Optimization (RBDO) of load sharing systems is computationally intensive due to the dynamic nature of component failure probabilities, since the failure of one component will vary the failure probabilities of other working components. Many RBDO methods have been successfully utilized to design individual components, however using these methods for handling system level reliability constraints is still a challenging task. This is because of a drop in accuracy and computational efficiency, especially when considering a load sharing system, where there is dependency in failure probabilities of components. The key idea is to integrate Stress–Strength Interference (SSI) theory with discrete (or) continuous time-discrete state Markov model for the reliability assessment of system, with the states being the condition of components (working/failed). This method takes advantage of the state transition probability matrix to represent the dynamic nature of the system performance. A numerical example of a simple load sharing system with two I-Beams is presented to illustrate and evaluate the performance of the proposed methodology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliability-Based Design Optimization of Load Sharing Systems Using SSI-Markov Models

Subramaniyan

Pan

2019

Designs

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“…That is evident since these methods are deterministic and they show a tendency of convergence to local minima points 2 Mathematical Problems in Engineering due to possible nonlinear function behavior in probabilistic problems. In order to increase robustness, authors [9] proposed to construct a response surface that is the product of individual performance functions and be sued as surrogate to get optimal design solutions. Despite the large amount of literature in the theme, a good introduction to the RBDO subject can be found in [10].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Truss Mass Minimization considering Uncertainties

Gomes

Corso

2017

Mathematical Problems in Engineering

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In real-world structural problems, a number of factors may cause geometric imperfections, load variability, or even uncertainties in material properties. Therefore, a deterministic optimization procedure may fail to account such uncertainties present in the actual system leading to optimum designs that are not reliable; the designed system may show excessive safety or sometimes not sufficient reliability to carry applied load due to uncertainties. In this paper, we introduce a hybrid reliability-based design optimization (RBDO) algorithm based on the genetic operations of Genetic Algorithm, the position and velocity update of the Particle Swarm Algorithm (for global exploration), and the sequential quadratic programming, for local search. The First-Order Reliability Method is used to account uncertainty in design and parameter variables and to evaluate the associated reliability. The hybrid method is analyzed based on RBDO benchmark examples that range from simple to complex truss parametric sizing optimizations with stress, displacements, and frequency deterministic and probabilistic constraints. The proposed final problem, which cannot be handled by single loop RBDO algorithms, highlights the importance of the proposed approach in cases where the discrete design variables are also random variables.

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“…Therefore, the result of metamodel evaluation is referred to as r to distinguish it from the result of the black-box evaluation y. The idea behind using a metamodel of the process is that the evaluation of the response on a metamodel is much faster than on the model itself (Dellino et al, 2015;Koziel et al, 2011;Zhuang et al, 2015).…”

Section: Metamodel-based Robust Optimizationmentioning

confidence: 99%

Robust optimization and tailoring scatter of forming processes

Nejadseyfi¹

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ix malisatie methode word het op maat maken van spreiding genoemd. Een gradint-gebaseerde aanpak word gebruikt voor de analytische evaluatie van karakteristieken van de responsverdeling om het inverse probleem op te lossen. Omdat de evaluatie van een robuust optimum weinig rekenkracht vraagt dankzij de analytische beschrijving, is de inverse analyse ook efficint uit te voeren. Het op maat maken van spreiding bij het vormen van de B-stijl is met succes uitgevoerd op basis van de voorgestelde aanpak. Deze benadering resulteert in Pareto-grenzen die de optimale aanpassing van de tolerantie voor elke bron van ruis tonen, zodat aan de vereiste uitgangstolerantie wordt voldaan. Deze methode is succesvol toegepast voor het bepalen van de meest economische samenstelling van toleranties op alle bronnen van ruis, waarbij aan de vereiste nauwkeurigheid van het proces voldaan wordt. 3 Uncertainty evaluation based on analytical method 3.1 Calculation of the response mean and standard deviation 3.2 Analytical gradients of mean and standard deviation. .. 3.3 Iterative improvement of the metamodel. .. .. .. .. . 3.4 Evaluation of higher order statistical moments of the response. .

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Enhancing product robustness in reliability-based design optimization

Cited by 29 publications

References 40 publications

Reliability-Based Design Optimization of Load Sharing Systems Using SSI-Markov Models

Reliability-Based Design Optimization of Load Sharing Systems Using SSI-Markov Models

A Hybrid Method for Truss Mass Minimization considering Uncertainties

Robust optimization and tailoring scatter of forming processes

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