Analytical sensitivity matrix for the inverse identification of hardening parameters of metal sheets

Prates, Pedro A.; Pereira, André F. G.; Oliveira, M. C.; Fernandes, J.V.

doi:10.1016/j.euromechsol.2019.01.010

Cited by 4 publications

(2 citation statements)

References 21 publications

(37 reference statements)

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“…Biaxial tensile tests on specimens specifically designed to obtain heterogeneous stress/strain fields and a high sensitivity to the anisotropy of the material have been successfully applied in inverse analysis methods [12,[24][25][26][27]. However, the application of inverse analysis methods is typically associated with significant computational and time costs, which are necessary to compute the sensitivity matrix through numerical simulations of the mechanical test used [28].…”

Section: Introductionmentioning

confidence: 99%

Identification of Sheet Metal Constitutive Parameters Using Metamodeling of the Biaxial Tensile Test on a Cruciform Specimen

Parreira,

Marques,

Sakharova

et al. 2024

Metals

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An identification strategy based on a machine learning approach is proposed to identify the constitutive parameters of metal sheets. The main novelty lies in the use of Gaussian Process Regression with the objective of identifying the constitutive parameters of metal sheets from the biaxial tensile test results on a cruciform specimen. The metamodel is intended to identify the constitutive parameters of the work hardening law and yield criterion. The metamodel used as input data the forces along both arms of the cruciform specimen and the strains measured for a given set of points. The identification strategy was tested for a wide range of virtual materials, and it was concluded that the strategy is able to identify the constitutive parameter with a relative error below to 1%. Afterwards, an uncertainty analysis is conducted by introducing noise to the force and strain measurements. The optimal strategy is able to identify the constitutive parameters with errors inferior to 6% in the description of the hardening, anisotropy coefficients and yield stresses in the presence of noise. The study emphasizes that the main strength of the proposed strategy relies on the judicious selection of critical areas for strain measurement, thereby increasing the accuracy and reliability of the identification process.

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Section: Introductionmentioning

confidence: 99%

Identification of Sheet Metal Constitutive Parameters Using Metamodeling of the Biaxial Tensile Test on a Cruciform Specimen

Parreira,

Marques,

Sakharova

et al. 2024

Metals

View full text Add to dashboard Cite

show abstract

“…Finite Element Model Updating (FEMU) [45,46,47,48] stands out as the most widely used algorithm for such purposes. With FEMU, the calculated displacement fields and resultant forces are compared to the measured quantities to determine the sensitivity matrices, i.e., how the change in parameters affects them [49,50,51]. FEMU was employed for simple (e.g., uniaxial [52]) and more complex (e.g., biaxial [53]) mechanical tests.…”

Section: Introductionmentioning

confidence: 99%