Metamodel-based optimization of a control arm considering strength and durability performance

Song, Xue Guan; Jung, Ji Hoon; Son, Hwan Jung; Park, Joon Hong; Lee, Kwon Hee; Park, Young Chul

doi:10.1016/j.camwa.2010.03.019

Cited by 42 publications

(19 citation statements)

References 3 publications

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“…As an alternative, the metamodels or surrogate modeling techniques have proven effective to replace full simulations for the optimization [41][42][43][44]. In this regard, the Kriging method has exhibited a fairly good accuracy for highly nonlinear design responses [45,46] and will be employed to construct the surrogate models for crashworthiness indicators in this study.…”

Section: Kriging Metamodelmentioning

confidence: 99%

Crashworthiness design of foam-filled bitubal structures with uncertainty

Fang

Gao

Sun

et al. 2014

International Journal of Non-Linear Mechanics

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Section: Kriging Metamodelmentioning

confidence: 99%

Crashworthiness design of foam-filled bitubal structures with uncertainty

Fang

Gao

Sun

et al. 2014

International Journal of Non-Linear Mechanics

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“…Kaya et al [4] re-designed a failed vehicle component subjected to cyclic loading by combining topology optimization with PRS based shape optimization. Song et al [5] adopted the PRS and KRG surrogate models to optimize a control arm by considering strength and durability performance.…”

Section: Introductionmentioning

confidence: 99%

Fatigue optimization with combined ensembles of surrogate modeling for a truck cab

et al. 2014

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In this study, we propose the ensembles of surrogates for an industrial application of fatigue optimization problem that aims to maximize a truck cab's fatigue life. After validating the numerical model, different ensembles of surrogates comprised polynomial response surface (PRS), radial basis function (RBF) and Kriging (KRG) models are established to approximate the fatigue life function. A hybrid PSO algorithm, which integrates the standard PSO with sequential quadratic programming (SQP), is implemented here to seek a quasiglobal optimum. Compared with individual surrogates, the ensembles of surrogates can attain more competent optima and yield a smaller surrogate error at the optimal point. Moreover, the hybrid PSO technique proves to search the better optima than the standard PSO in the fatigue optimization problem considered. Finally, it is found that a more accurate surrogate model may not necessarily produce a better optimum for the ensembles of surrogates, thus multiple ensembles are recommended without increasing much extra computational cost.

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“…Relative change in design domain volume has been considered as the convergence criterion. Song and Jung [19] presented an optimization of ADI upper control arm, based on FEM analysis, using both response surface method and Kriging model. They have revealed that although these methods are not the global optimal solution, the RSM and Kriging model leads to very closed optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Structural optimization of MacPherson control arm under fatigue loading

Marzbanrad

Hoseinpour

2017

Teh. vjesn.

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Preliminary communicationIn this research, the topology and shape optimization of a MacPherson control arm has been accomplished to achieve lighter weight. Present automotive market demands low cost and light weight component to meet the need of fuel efficient and cost effective vehicle. This in turn gives the rise to more effective use of materials for automotive parts which can reduce the mass of vehicle. Since automotive components are under dynamic loads which cause fatigue damage, considering fatigue criteria seems to be essential in designing automotive components. At first, in order to create severe loading condition for control arm some rough roads are generated through power spectral density. Then, the most critical loading conditions are obtained through multi body dynamics analysis of a full vehicle model. Then, the topology optimization is performed based on fatigue life criterion using HyperMesh software, which resulted in 50 % mass reduction. In the next step a CAD model is created using CATIA software and shape optimization is performed to achieve accurate dimensions with less mass. Keywords: fatigue life; MacPherson control arm; shape optimization; topology optimization Konstrukcijska optimizacija MacPherson upravljačke poluge pod zamornim opterećenjemPrethodno priopćenje U ovom istraživanju izvršena je optimizacija topologije i oblika MacPherson upravljačke poluge u svrhu postizanja manje težina. Postojeće automobilsko tržište traži dijelove niske cijene i male težine, a za potrebe energetski učinkovitog, a jeftinog vozila. To zahtijeva učinkovitiju uporabu materijala za automobilske dijelove koji mogu dovesti do smanjene mase vozila. Budući da su automobilski dijelovi izloženi dinamičkim opterećenjima koja uzrokuju oštećenja zbog zamora, uzimanje u obzir kriterija zamora čini se bitnim u dizajniranju automobilskih dijelova. Kako bi se stvorili što teži uvjeti opterećenja upravljačke poluge, najprije su spektralnom gustoćom snage generirane neravne ceste. Zatim su, dinamičkom analizom karoserije kompletnog modela vozila, dobiveni najkritičniji uvjeti opterećenja. Nakon toga, izvršena je optimizacija topologije pomoću kriterija vijeka trajanja do zamora primjenom HyperMesh softvera, što je rezultiralo smanjenjem mase od 50 %. U sljedećem koraku kreiran je CAD model primjenom CATIA softvera i provedena optimizacija oblika kako bi se dobile točne dimenzije s manje mase.Ključne riječi: MacPherson upravljačka poluga; optimizacija oblika; optimizacija topologije; vijek trajanja do zamora

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Metamodel-based optimization of a control arm considering strength and durability performance

Cited by 42 publications

References 3 publications

Crashworthiness design of foam-filled bitubal structures with uncertainty

Crashworthiness design of foam-filled bitubal structures with uncertainty

Fatigue optimization with combined ensembles of surrogate modeling for a truck cab

Structural optimization of MacPherson control arm under fatigue loading

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