Optimization Design of Cruciform Specimens for Biaxial Testing Based on Genetic Algorithm

Yang, Xiuhai; Wu, Z. R.; Yang, Yun; Pan, Y.; Wang, S. Q.; Lei, H.

doi:10.1007/s11665-022-07258-6

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…The biaxial tensile test on specimens with a cruciform geometry has been the subject of growing interest [20][21][22][23] due to its potential to identify the constitutive parameters using a single test. For instance, in [19], an inverse analysis methodology for determining plastic constitutive model parameters in biaxial tensile tests of metal sheets was implemented through the use of finite element simulations and comparison with experimental data.…”

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

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“…To solve the composite delamination phenomenon of airship propellers, Meng et al [ 23 ] performed both local optimization and global optimization to enhance the stiffness of the propeller through genetic algorithm. Based on the multi-objective genetic algorithm, Yang et al [ 24 ] designed and optimized the structure of a biaxial tensile test piece of carbon fiber laminates so that the failure occurred at the middle position. Bai et al [ 25 ] obtained the best practice of a genetic algorithm by using the quantitative analysis method, and they optimized the stiffness and weight of woven composite springs.…”

Section: Introductionmentioning

confidence: 99%

Modeling Analysis of Complex Deformation of Woven Coating Film during the Cyclic Tensile Process

Cai,

Zhang,

Cai

et al. 2024

Polymers

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It is difficult for the existing Burgers model to accurately depict the off-axis cyclic drawing process of woven coatings. In this paper, the mechanical deformation of woven PVC (polyvinyl chloride)-coated film at different temperatures is investigated. One-dimensional (1D) and two-dimensional (2D) constitutive models were established to characterize cyclic deformation processes. The 1D model is an improved Burgers model. The effects of the time dependence of the viscosity coefficient and the ratio of elastic to viscous deformation are considered simultaneously. The accuracy of the 1D model for predicting the cyclic nonlinear deformation at different temperatures and loading rates is improved. The 2D model is a nonlinear orthotropic model using polynomials. On the basis of the single-objective genetic algorithm, the inverse algorithm is used to obtain the shear polynomial coefficients in the tension phase and the shear modulus in the unloading phase, which circumvents performing the difficult shear test. UMAT subroutines of off-axis stretching and off-axis cyclic stretching are written separately. The intelligent inverse algorithm program consists of a single-objective genetic algorithm program, a finite element parametric modelling program, and a UMAT subroutine. The simulation results are compared with the off-axis cyclic tensile test data to validate the effectiveness and accuracy of the proposed 2D model for the analysis of the woven PVC-coated films in the tension–shear coupling state.

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“…Genetic Algorithms have been used by Bauer et al, 10 in order to optimize the shape of test specimens for sheet molding compounds. Recently, Yang et al 11 used Genetic Algorithms for a two-objective optimization of a cruciform test specimen with the aim of maximizing the stress level and minimizing the stress inhomogeneity in the specimen center. Manual optimizations of cruciform specimens for aluminum alloys and steel using Finite Element analyses have been presented by Andrusca et al 12,13 and Xiao et al 14 Shao et al 15 conducted experimental and simulative analyses in order to optimize the geometry of cruciform specimens for evaluation of aluminum alloys under hot stamping conditions.…”

Section: Introductionmentioning

confidence: 99%

Numerical optimization-based design studies on biaxial tensile tests

Dexl

Hauffe

Markmiller

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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Biaxial tensile tests are of great significance for the characterization of materials. For ductile materials, they allow the representation of the yield strength curve separating elastic and plastic material behavior. Contrary demands regarding the specimen design arise, if a biaxial stress state with the equivalent stress equal or close to the yield strength shall be obtained. On one hand, the specimen shall be of constant thickness and stiffness, in order to allow an unconstrained deformation without undesired lateral strains. On the other hand, stress concentrations arising from the load introduction make a thickened border region necessary, in order to avoid a premature failure of the specimen. The present paper addresses this problem by a multi-objective structural optimization using Evolutionary Algorithms. Well-defined parametric models of biaxial tensile test specimens allow to systematically investigate the influence of the thickness distribution and the sizing of the border region. Additionally, the influence of actuators of constant force compared to actuators of constant displacement are quantified. In all cases, the minimization of the stress difference as well as the actuation effort are considered as objectives. The results allow a deep insight to the optimal definition of biaxial tensile test specimens and are summed up in well-defined design proposals.

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Optimization Design of Cruciform Specimens for Biaxial Testing Based on Genetic Algorithm

Cited by 5 publications

References 22 publications

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

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

Modeling Analysis of Complex Deformation of Woven Coating Film during the Cyclic Tensile Process

Numerical optimization-based design studies on biaxial tensile tests

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