Optimization of sheet metal forming processes by adaptive response surface based on intelligent sampling method

Wang, Hu; Yao, Li Guang; Hua, Zhong Zhi

doi:10.1016/j.jmatprotec.2007.06.018

Cited by 102 publications

(39 citation statements)

References 37 publications

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“…The boundary and best neighbor search (BBNS) method of Hu et al (2008) was applied in parallel fashion using support vector regression surrogate. The approach appears to be applicable to any other surrogate without using an uncertainty structure.…”

Section: Methods Without Uncertainty Modelsmentioning

confidence: 99%

“…The compromise is governed by weighting the two objectives with weights that cycle from emphasis on the objective to emphasis on the distance. Hu et al (2008Hu et al ( , 2009) developed a sampling scheme called boundary and best neighbor searching (BBNS) that does not use an uncertainty model. Instead it balances exploration and exploitation by on the one hand looking for neighbors of the best few current samples and on the other hand looking to move towards the boundary of the design space.…”

Section: Distance-based and Other Criteriamentioning

confidence: 99%

“…The mode-pursuing sampling method (Wang et al 2004) creates local quadratic surrogates for promising regions, where the sampling approach tends to generate dense samples. Hu et al (2008) pursued this idea further, using particle swarm optimization to refine the sampling. Peri and Tinti (2012) used a global surrogate of the objective function combined with second order approximations at each DOE (based on the surrogate) point to find an approximate local optimum near the starting point.…”

Section: Global-local Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

209

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Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

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Section: Methods Without Uncertainty Modelsmentioning

confidence: 99%

Section: Distance-based and Other Criteriamentioning

confidence: 99%

Section: Global-local Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Parallel surrogate-assisted global optimization with expensive functions – a survey

Haftka

Villanueva

Chaudhuri

2016

Struct Multidisc Optim

209

View full text Add to dashboard Cite

show abstract

“…In a traditional process optimization problem, the manufacturing process objectives are optimized by varying the process control parameters (Sun et al, 2010;Wei and Yuying, 2008;Hu et al, 2008;Konak et al, 2006) whereas in product optimization, the geometry (e.g., shape, size) is altered to enhance the product performance. However, in a coupled processperformance optimization problem as considered here, both manufacturing-and performancelevel attributes are optimized.…”

Section: Multi-objective Design Optimizationmentioning

confidence: 99%

Concurrent Process-Product Design Optimization Using Coupled Nonlinear Finite-Element Simulations

Najafi¹,

Rais‐Rohani²

2012

Finite Element Analysis - Applications in Mechanical Engineering

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“…This method involves three steps (a) creating a set of design points; (b) creating a metamodel or approximation of each response based on these design points; and (c) estimating future design point responses using the 5 metamodel. This method is computationally efficient, but it only creates estimates of future results based on previously collected data [14,15].…”

Section: Sheet-stamping Process Design Backgroundmentioning

confidence: 99%

Sheet-stamping process simulation and optimization

Tamasco

Rais‐Rohani

Buijk³

2011

52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference

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This thesis presents the development and implementation of a generalized optimization framework for use in sheet-stamping process simulation by finite element analysis. The generic framework consists of three main elements: a process simulation program, an optimization code, and a response filtering program. These elements can be filled by any combination of applicable software packages. Example sheet-stamping process simulations are presented to demonstrate the usage of the framework in various forming scenarios. Each of the example simulations is presented with a sensitivity analysis. These examples include analysis of a 2-dimensional single-stage forming, a 2-dimensional multi-stage forming, and two different 3-dimensional single-stage forming processes. A forming limit diagram is used to define failure in the 3-dimensional process simulations. Optimization results are presented using damage minimization, thinning minimization, and springback minimization with aluminum alloy 6061-T6 blanks.

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Optimization of sheet metal forming processes by adaptive response surface based on intelligent sampling method

Cited by 102 publications

References 37 publications

Parallel surrogate-assisted global optimization with expensive functions – a survey

Parallel surrogate-assisted global optimization with expensive functions – a survey

Concurrent Process-Product Design Optimization Using Coupled Nonlinear Finite-Element Simulations

Sheet-stamping process simulation and optimization

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