Fuzzy clustering based hierarchical metamodeling for design space reduction and optimization

Wang, G. Gary; Simpson, Timothy W.

doi:10.1080/03052150310001639911

Cited by 111 publications

(37 citation statements)

References 42 publications

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“…Although the final optimum value reported in Ref. [11] is slightly lower, the number of function evaluations and, therefore, computation time to reach this optimum value is extremely high.…”

Section: Example Problemmentioning

confidence: 95%

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Optimization of composite pressure vessels with metal liner by adaptive response surface method

Vafaeesefat

2011

J Mech Sci Technol

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This paper presents a new strategy to optimize composite pressure vessels with metallic liners using an adaptive response surface method. Finite analysis is used to evaluate the fitness function and constraints. To reduce the computation time, the proposed algorithm creates the response surface (RS) model to find the optimum design. To improve the accuracy of the RS model, the design domain is iteratively reduced and new sample points are added to the RS model. In this way, the accuracy of the RS model is improved close to the optimum point. The genetic algorithm is used to find the reduced design domain and to generate new sample points. The optimization algorithm is first applied to a highly nonlinear function to evaluate its efficiency. It is then used to find the optimal design for a composite pressure vessel with a metallic liner.

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Section: Example Problemmentioning

confidence: 95%

“…(11). When the RS model is constructed, the objective function and variable boundary used for the genetic algorithm is defined by: …”

Section: Design Optimization Type 3 Cng Cylindermentioning

confidence: 99%

Optimization of composite pressure vessels with metal liner by adaptive response surface method

Vafaeesefat

2011

J Mech Sci Technol

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“…Schonlau, Welch, and Jones (1998) describe a sequential algorithm to balance local and global searches using metamodels during constrained optimization. Gary Wang et al (2004) proposed fuzzy-clustering-based hierarchical metamodeling for design space reduction and optimization; this method proposed methodology can intuitively capture promising design regions and can efficiently identify the global or near-global design optimum in the presence of highly nonlinear constraints. Wang and Li developed intelligent sampling scheme based on ''nature based" methods such as particle swarm method (Hu, Li, & Zhong, 2008) and boundary-based methods (Wang, Li, Li, & Zhong, 2007).…”

Section: Introductionmentioning

confidence: 99%

A metamodel optimization methodology based on multi-level fuzzy clustering space reduction strategy and its applications

Wang

et al. 2008

Computers & Industrial Engineering

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“…In each step, a new Latin hypercube design is generated in the reduced design space and a second-order model is fitted. Wang and Simpson 6 proposed an intuitive method to systematically reduce the design space by using Fuzzy c-means clustering. In the reduced design space, metamodels are sequentially refined by augmenting new design points to a Latin hypercube design.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sequential Procedure for Efficient Optimization of the Sheet Metal Spinning Process

Henkenjohann

Göbel

Kleiner

et al. 2005

Quality & Reliability Eng

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Due to technological advances in science, the complexity of processes under investigation increased continuously in recent years. For example, in mechanical engineering, there are often highly nonlinear input-output relationships combined with a large number of constraints. Sheet metal spinning is one example of such a production process. Many flexible and powerful methods to global optimization have been developed. In this paper, a sequential approach originally developed for computer experiments will be adopted and applied to optimize the spinning process based on physical experiments. This approach sequentially refines the model by adding new design points based on the expected improvement criterion. This criterion balances the need to observe at the predicted optimum with the need to investigate the design space in areas of high uncertainty. However to guarantee an efficient optimization of the spinning process, this approach has to be embedded in a more substantial procedure. One reason for this is the liability of workpiece failure for most of the operable design space. Since the shape of the failure region is unknown, many missing observations have to be expected when exploring the design space. The other reason is the need to incorporate available process knowledge of sheet metal spinning to improve the efficiency in optimization. The main problem in implementing this information is a changing process behavior for different geometries and materials used. Hence, if a component with a new geometry has to be optimized, it is difficult to include available process knowledge. In this paper, an adaptive sequential optimization procedure (ASOP) is presented to cope with these problems in order to guarantee an efficient optimization of such complex processes. The approach is exemplified by optimizing the spinning process for a fixed geometry.

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Fuzzy clustering based hierarchical metamodeling for design space reduction and optimization

Cited by 111 publications

References 42 publications

Optimization of composite pressure vessels with metal liner by adaptive response surface method

Optimization of composite pressure vessels with metal liner by adaptive response surface method

A metamodel optimization methodology based on multi-level fuzzy clustering space reduction strategy and its applications

An Adaptive Sequential Procedure for Efficient Optimization of the Sheet Metal Spinning Process

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