Meshless methods for the inverse problem related to the determination of elastoplastic properties from the torsional experiment

Kołodziej, Jan Adam; Jankowska, Małgorzata A.; Mierzwiczak, Magdalena

doi:10.1016/j.ijsolstr.2013.08.025

Cited by 22 publications

(5 citation statements)

References 46 publications

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“…Because of the semi-analytical nature of the MFS, this method can result in very accurate results with low computational cost. The MFS has already been used for characterization of material properties of isotopic elastoplastic materials [19,20]; however, to the authors' best knowledge, the inverse formulation of the MFS for identification of elastic constants of 2D anisotropic solids has not been presented yet.…”

Section: -Introductionmentioning

confidence: 99%

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

Hematiyan,

Khosravifard,

Mohammadi

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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There are six elastic constants for an anisotropic body in plane strain/stress conditions. In the inverse problem of this study, it is assumed that the elastic constants of an anisotropic body are unknown, while the displacements or strains at several sampling points of the body under static loading are provided. For the first time, a boundary-type meshfree method, i.e. the method of fundamental solutions, is used for solving the problem, where the sensitivity analysis is performed by direct differentiation of the discretized equations. For that, the closed-form relations for sensitivity of the displacements/strains with respect to the elastic constants are analytically derived.Using a numerical study, it is shown that the proposed sensitivity analysis is much more advantageous compared to traditional finite difference approximation. A simple method for proper selection of initial guesses is also proposed. Two different example problems under plane strain and plane stress conditions are provided to investigate the accuracy of the proposed inverse method.Moreover, the effects of the number of measurement data, the measurement error, and the configuration of sampling points on the solution of the inverse problem are studied. It is observed that the solutions are more accurate in the cases where the sampling points are located at different parts of the body.

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Section: -Introductionmentioning

confidence: 99%

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

Hematiyan,

Khosravifard,

Mohammadi

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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show abstract

“…Typically, different optimization algorithms are used to solve these inverse problems. Grabski et al [4] applied the metaheuristic-based virus optimization algorithm in combination with meshless methods [5]. In contrast, Jones et al [2] used the virtual fields method with the gradient-based algorithm fmincon to solve the inverse problem for full field data.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations

2022

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Increasing product requirements in the mechanical engineering industry and efforts to reduce time-to-market demand highly accurate and resource-efficient finite element simulations. The required parameter calibration of the material models is becoming increasingly challenging with regard to the growing variety of available materials. Besides the classical iterative optimization-based parameter identification method, novel machine learning-based methods represent promising alternatives, especially in terms of efficiency. However, the machine learning algorithms, architectures, and settings significantly affect the resulting accuracy. This work presents a comparative study of different machine learning algorithms based on virtual datasets with varying settings for the direct inverse material parameter identification method. Multilayer perceptrons, convolutional neural networks, and Bayesian neural networks are compared; and their resulting prediction accuracies are investigated. Furthermore, advantages in material parameter identification by uncertainty quantification using the Bayesian probabilistic approach are examined and discussed. The results show increased prediction quality when using convolutional neural networks instead of multilayer perceptrons. The assessment of the aleatoric and epistemic uncertainties when using Bayesian neural networks also demonstrated advantages in evaluating the reliability of the predicted material parameters and their influences on the subsequent finite element simulations.

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“…The effective method is accurate, even with various noise levels. Kolodziej et al. (2013) used the meshless method and Levenberg–Marquardt (LM) method to solve the inverse problem related to the identification of elastoplastic properties based on the torsional experiment.…”

Section: Introductionmentioning

confidence: 99%

“…The effective method is accurate, even with various noise levels. Kolodziej et al (2013) used the meshless method and Levenberg-Marquardt (LM) method to solve the inverse problem related to the identification of elastoplastic properties based on the torsional experiment. Cooreman et al (2007) identified the metal's physical property parameters during plastic deformation by comparing an experimentally measured strain field with computed data from a finite element model, and an analytical method was used to calculate the sensitivity coefficients.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous identification of multi-parameter for power hardening elastoplastic problems in three-dimensional geometries

Zhang

Mei

Bai

et al. 2022

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PurposeThe purpose of this study is to simultaneously determine the constitutive parameters and boundary conditions by solving inverse mechanical problems of power hardening elastoplastic materials in three-dimensional geometries.Design/methodology/approachThe power hardening elastoplastic problem is solved by the complex variable finite element method in software ABAQUS, based on a three-dimensional complex stress element using user-defined element subroutine. The complex-variable-differentiation method is introduced and used to accurately calculate the sensitivity coefficients in the multiple parameters identification method, and the Levenberg–Marquardt algorithm is applied to carry out the inversion.FindingsNumerical results indicate that the complex variable finite element method has good performance for solving elastoplastic problems of three-dimensional geometries. The inversion method is effective and accurate for simultaneously identifying multi-parameters of power hardening elastoplastic problems in three-dimensional geometries, which could be employed for solving inverse elastoplastic problems in engineering applications.Originality/valueThe constitutive parameters and boundary conditions are simultaneously identified for power hardening elastoplastic problems in three-dimensional geometries, which is much challenging in practical applications. The numerical results show that the inversion method has high accuracy, good stability, and fast convergence speed.

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Meshless methods for the inverse problem related to the determination of elastoplastic properties from the torsional experiment

Cited by 22 publications

References 46 publications

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

An inverse method of fundamental solutions for the identification of 2D elastic properties of anisotropic solids

Comparative Study of Various Neural Network Types for Direct Inverse Material Parameter Identification in Numerical Simulations

Simultaneous identification of multi-parameter for power hardening elastoplastic problems in three-dimensional geometries

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