Towards Material Testing 2.0. A review of test design for identification of constitutive parameters from full‐field measurements

Pierron, Fabrice; Grédiac, Michel

doi:10.1111/str.12370

Cited by 88 publications

(66 citation statements)

References 133 publications

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“…In order to enable the training process of the constitutive ANN within a broad subset of the invariant space I ⊂ R 3×1 , the deformation states of the virtual samples have to be as heterogeneous as possible. With regard to real experiments [10,50], non-uniform thin discs, e. g., with holes, are chosen for this purpose. Thereby it has to be taken into account that only displacements on the surface of the sample are measurable in an experimental setting.…”

Section: Virtual Experimentsmentioning

confidence: 99%

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

et al. 2021

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Herein, an artificial neural network (ANN)-based approach for the efficient automated modeling and simulation of isotropic hyperelastic solids is presented. Starting from a large data set comprising deformations and corresponding stresses, a simple, physically based reduction of the problem’s dimensionality is performed in a data processing step. More specifically, three deformation type invariants serve as the input instead of the deformation tensor itself. In the same way, three corresponding stress coefficients replace the stress tensor in the output layer. These initially unknown values are calculated from a linear least square optimization problem for each data tuple. Using the reduced data set, an ANN-based constitutive model is trained by using standard machine learning methods. Furthermore, in order to ensure thermodynamic consistency, the previously trained network is modified by constructing a pseudo-potential within an integration step and a subsequent derivation which leads to a further ANN-based model. In the second part of this work, the proposed method is exemplarily used for the description of a highly nonlinear Ogden type material. Thereby, the necessary data set is collected from virtual experiments of discs with holes in pure plane stress modes, where influences of different loading types and specimen geometries on the resulting data sets are investigated. Afterwards, the collected data are used for the ANN training within the reduced data space, whereby an excellent approximation quality could be achieved with only one hidden layer comprising a low number of neurons. Finally, the application of the trained constitutive ANN for the simulation of two three-dimensional samples is shown. Thereby, a rather high accuracy could be achieved, although the occurring stresses are fully three-dimensional whereas the training data are taken from pure two-dimensional plane stress states.

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Section: Virtual Experimentsmentioning

confidence: 99%

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

et al. 2021

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“…Studying the metrological performance of full-field measurement techniques is the topic of many recent papers, in particular concerning Digital Image Correlation (DIC) thanks to the collaborative work carried out within the framework of the DIC-Challenge [1]. Displacement and strain maps are now widely used to reveal material heterogeneities [2,3], to track crack propagation in engineering materials [4,5], or to identify material properties [6,7], among other applications. In this context, determining reliable maps is a chief issue.…”

Section: Introductionmentioning

confidence: 99%

Influence of the sampling density on the noise level in displacement and strain maps obtained by processing periodic patterns

et al. 2021

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The aim of this study is to investigate the influence of the sampling density of periodic patterns such as checkerboards or 2D grids on the noise level observed in displacement and strain maps extracted from such images. A spectral method named Localized Spectrum Analysis (LSA) is used to process the images. It is shown that this parameter influences this noise level. This influence is quantified, and a power law is proposed to model it, with a value of the power identified with the measurements. It is also confirmed that checkerboards give a lower noise level than 2D grids, with an improvement which is also quantified, and which increases as the sampling density increases.

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“…This information was relevant to enhance finite element analysis by considering both real geometry and boundary conditions, in contrast with prescribing idealised displacements at the boundary domain [ 34 , 35 ]. Full-field data were conveniently used in other studies for advanced inverse material characterisation, particularly dealing with heterogeneous stress and strain states that can be generated by geometric discontinuities, loading configurations or material structure [ 16 , 32 , 36 ]. In situ DIC measurements have also been playing an important role in fracture and fatigue problems.…”

Section: In Situ Monitoring Using Full-field Measurement Techniquementioning

confidence: 99%

“…The impact of FFOTs in providing kinematic measurements across a whole region of interest has brought novel insights into different applied scientific areas. Among others, one may underline the measurement of gradient fields [ 14 , 15 , 16 ], fracture cracking evaluation [ 17 , 18 , 19 , 20 , 21 ], image-based approaches of high-strain rate dynamical behaviour of materials [ 22 , 23 , 24 , 25 , 26 ], numerical modelling validation from image-based measurements [ 27 , 28 , 29 ] and inverse material parameter identification from heterogeneous tests [ 30 , 31 , 32 , 33 ]. Advanced monitoring techniques have also been applied with the AM process in order to enhance the technology.…”

Section: Introductionmentioning

confidence: 99%

In Situ Monitoring of Additive Manufacturing Using Digital Image Correlation: A Review

2021

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This paper is a critical review of in situ full-field measurements provided by digital image correlation (DIC) for inspecting and enhancing additive manufacturing (AM) processes. The principle of DIC is firstly recalled and its applicability during different AM processes systematically addressed. Relevant customisations of DIC in AM processes are highlighted regarding optical system, lighting and speckled pattern procedures. A perspective is given in view of the impact of in situ monitoring regarding AM processes based on target subjects concerning defect characterisation, evaluation of residual stresses, geometric distortions, strain measurements, numerical modelling validation and material characterisation. Finally, a case study on in situ measurements with DIC for wire and arc additive manufacturing (WAAM) is presented emphasizing opportunities, challenges and solutions.

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Towards Material Testing 2.0. A review of test design for identification of constitutive parameters from full‐field measurements

Cited by 88 publications

References 133 publications

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Automated constitutive modeling of isotropic hyperelasticity based on artificial neural networks

Influence of the sampling density on the noise level in displacement and strain maps obtained by processing periodic patterns

In Situ Monitoring of Additive Manufacturing Using Digital Image Correlation: A Review

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