Novel criteria for determination of material model parameters

Andrade‐Campos, A.; de-Carvalho, R.; Valente, R. A. F.

doi:10.1016/j.ijmecsci.2011.11.010

Cited by 33 publications

(18 citation statements)

References 22 publications

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“…The objective function goes down to zero if a perfect correspondence between numerical and experimental points is obtained. W abs is a weighting factor that should be adapted to the identification problem in order to standardize different units or scales of the data (Andrade-Campos et al, 2012). A normalized weighting factor W abs was introduced in the definition of the objective function.…”

Section: Materials Parameter Identificationmentioning

confidence: 99%

Material parameter identification within an integrated methodology considering anisotropy, hardening and rupture

Souto

Andrade‐Campos

Thuillier

2015

Journal of Materials Processing Technology

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Section: Materials Parameter Identificationmentioning

confidence: 99%

Material parameter identification within an integrated methodology considering anisotropy, hardening and rupture

Souto

Andrade‐Campos

Thuillier

2015

Journal of Materials Processing Technology

Self Cite

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“…A wide number of cost function formulations for the identification of constitutive parameters have been proposed in literature (e.g., [57,58]). According to Cao and Lin [57], the cost function should operate as an "efficient guide" of the optimization procedure, in order to search for the best fit to the experimental results; therefore, the ideal cost function should comprise the following conditions:…”

Section: Cost Functionmentioning

confidence: 99%

Inverse Strategies for Identifying the Parameters of Constitutive Laws of Metal Sheets

Prates

Pereira

Сахарова

et al. 2016

Advances in Materials Science and Engineering

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This article is a review regarding recently developed inverse strategies coupled with finite element simulations for the identification of the parameters of constitutive laws that describe the plastic behaviour of metal sheets. It highlights that the identification procedure is dictated by the loading conditions, the geometry of the sample, the type of experimental results selected for the analysis, the cost function, and optimization algorithm used. Also, the type of constitutive law (isotropic and/or kinematic hardening laws and/or anisotropic yield criterion), whose parameters are intended to be identified, affects the whole identification procedure.

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“…For instance, Springmann and Kuna, and Shterenlikht and Alexander used the Levenberg‐Marquardt method to identification of inelastic parameters for the Gurson–Tvergaard–Needleman (GTN) plasticity model . The same optimization method was adopted by Andrade‐Campos et al to investigate different objective functions aiming to determine parameters of elasto‐viscoplastic materials . The conjugate gradient method was adopted by Fraś et al to determine viscoplastic parameters of high strength steels .…”

Section: Parameter Identification and Optimization Strategiesmentioning

confidence: 99%

Identification of constitutive parameters – optimization strategies and applications

Vaz

Cardoso

Muñoz-Rojas

et al. 2015

Materialwissenschaft Werkst

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The pursuit to reduce manufacturing costs and increase product quality has led industries to use commercial codes and appropriate material models to simulate a wide range of metal forming operations. This scenario has prompted a healthy discussion on the strategies to obtaining constitutive parameters able to yield accurate numerical predictions. Optimization‐based parameter identification techniques have opened completely new routes to determine material parameters for this class of forming problems. Notwithstanding, the most appropriate optimization strategy (or development of new ones) for the trinomial forming operation – constitutive model – constitutive parameters is still open to debate. This work highlights the important role that optimization strategies play to determine parameters of constitutive models. A brief description of gradient‐based, gradient‐free and hybrid optimization approaches is presented within the framework of parameter identification. Comparative studies and applications to classical and damaged material models are also discussed.

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Novel criteria for determination of material model parameters

Cited by 33 publications

References 22 publications

Material parameter identification within an integrated methodology considering anisotropy, hardening and rupture

Material parameter identification within an integrated methodology considering anisotropy, hardening and rupture

Inverse Strategies for Identifying the Parameters of Constitutive Laws of Metal Sheets

Identification of constitutive parameters – optimization strategies and applications

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