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firstname.lastname@example.org Laboratoire de micromécanique To identify mechanical properties in heterogeneous materials, the local stress ﬁelds et intégrité des structures (MIST), have to be estimated. The recent developments in imaging techniques allow reaching IRSN-CNRS-Université de Montpellier, Montpellier, France precise and spatially dense kinematic ﬁelds (e.g. displacement, strain ...). In this paper, Full list of author information is an iterative procedure is used to identify the distribution of elastoplastic material available at the end of the article parameters and the local stress ﬁelds. The formulation and the principle of the method are brieﬂy presented while attention is paid to check its reliability and eﬃciency on ﬁnite element simulation data as reference full-ﬁeld measurements. The method is also applied to noisy measured displacement ﬁelds to assess its robustness. Keywords: Inverse method, Identiﬁcation, Elastoplasticity, Digital image correlation Introduction Various identiﬁcation techniques have been developed to identify mechanical behaviours and stress ﬁelds using kinematic ﬁeld variables such as displacements or strains obtained by full-ﬁeld measurement techniques (e.g. digital image correlation, interferometric tech- niques, grid methods, etc.): the ﬁnite element model updating method (FEMU) [1–4], the reciprocity gap method (RGM) [5,6], the constitutive equation gap method (CEGM) [7–9], the virtual ﬁeld method (VFM) [10–18] and the equilibrium gap method (EGM) [19,20]. An overview of these identiﬁcation procedures and their applications on experimental data can be found in . More recently, some authors proposed to further integrate the displacement measurements and the identiﬁcation procedures leading to so-called integrated-DIC (or I-DIC) formalism [22,23]. In this work, we extend and adapt the approach developed in  to identify the con- stitutive laws and their mechanical parameters for heterogeneous materials. Since the method proposed in  is based on Airy functions, the approach is limited to simple geometries and regular meshes. This limitation is here removed and any geometry can be addressed. Moreover, the initial work  was limited to elastoplastic behaviours with a linear hardening. For sake of simplicity, the present paper also focuses on linear hardening but it is now straightforward to deal with any kinematic hardening law. The last improve- ment presented here concerns the identiﬁcation of the yield stress and of the hardening modulus. The formulation was modiﬁed in order to allow the simultaneous identiﬁcation © The Author(s) 2017. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 0123456789().,–: vol Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 2 of 13 of the two plastic parameters: the initial requirement of a plastic zone whose size remains constant on two successive load steps is no more needed. The new formulation thus allows to deal with multilinear hardening behaviours on complex geometries and several load steps. The simultaneous use of several load steps for the identiﬁcation signiﬁcantly decreases the sensitivity to measurement noise. The class of models that we have in mind belongs to J2 elastoplasticity with hardening. The Constitutive Equation Gap Method (CEGM) originally used as an error estimator for ﬁnite element simulations is here adopted in order to identify the stress ﬁelds and the con- stitutive parameters of heterogeneous materials. The change from an Airy’s type approach to a Finite-Element approach to the CEGM allows investigating enhanced boundary con- ditions and more complex geometries. In this application, we introduce the elastoplastic secant stiﬀness tensor B . For a Prager’s linear kinematic hardening model, the tensor B is directly expressed as a function of the material properties (Young’s modulus E, Poisson’s ratio v for isotropic elasticity and shear modulus G for cubic elasticity, yield stress σ and hardening coeﬃcient h) and of the load- ing history. In the following, we brieﬂy present the identiﬁcation procedure, we illustrate its performance on various simulated data obtained under small perturbations and plane stress assumptions. Finally, we check its robustness with respect to measurement noise. Inverse method Identiﬁcation procedure Since we have interest in the sequel for thin and ﬂat samples observed via in-plane DIC techniques, we focus on the identiﬁcation of elastoplastic constitutive laws in a 2D frame- work (plane stress). In this context, a maximum of three parameters can be locally iden- tiﬁed because we have only access to three local in-plane strain measurements related to three in-plane stress components. The CEGM is based on the minimization of an energetic functional depending on two sets of parameters: the stress ﬁeld and the mechanical material properties. This procedure can be applied to any identiﬁcation problem and can be used both with data extracted from numerical simulations and with experimental measurements. For a sequence of successive load steps (subscript 1 ≤ n ≤ N for each step), we denote − → by u the measured displacement ﬁeld on a given region of interest and we consider an elastoplastic body governed by the set of Eqs. (1–4): div σ = 0 in (1) − → c s c σ = B : ε u in (2) n n n R = σ ·nds on ∂ n j (3) σ ·n = 0 on ∂ − → − → c m u = u on ∂ (4) n n where σ represents a statically admissible stress ﬁeld associated with the displacement − → u via the fourth order secant elastoplastic tensor B (corresponding to the Hooke tensor n n B for an elastic step) for each load step n. It is worth noticing that for a heterogeneous material, all these quantities depend on the position. The overall forces R are known for each time step n on the boundary ∂ of .The n j free boundaries ∂ satisfy the relations: ∂ ∪ ∂ ∪ ∂ = ∂ and ∂ ∩ ∂ =∅, i j i u j i Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 3 of 13 ∂ ∩ ∂ =∅ and ∂ ∩ ∂ =∅. On some boundaries ∂ , we also impose that the j u i u u − → − → c m average displacement u is equal to the average displacement u which not only allows to n n eliminate the rigid body motion but also to further constrain the identiﬁcation problem and to reduce the inﬂuence of measurement noise. The energetic functional can be expressed in its simplest form (small strain hypothesis, equilibrium): − → c s E u , B rc n n n∈[1,N] n∈[1,N] − → − → − → − → s c m c m = B : ε u − ε u : ε u − ε u d (5) n n n n n n=1 − → Here N is the overall number of time steps used for the identiﬁcation and u a displace- ment ﬁeld compatible with the equilibrium of the studied domain at load step n. The identiﬁcation procedure consists in minimizing E with respect to its two argu- rc − → c s ments u and B . The method can be applied to any behaviour for which an expression n n of the secant tensor B is available. Consequently, it can be used on reversible behaviours (linear and non-linear elasticity) or irreversible behaviours (viscoelasticity, elastoplasticity ...). When dealing with irreversible behaviours, it is necessary to take the loading history into account. In the case of elastoplasticity, this amounts to separate elastic loading steps from plastic ones. The method was numerically implemented to deal with elastoplastic behaviours and monotonic loadings. The consistency between the numerical implemen- tation and the main hypotheses underlying the description of plastic ﬂow (e.g. existence of a yield stress, isochoric plastic strain, normality rule) is ensured from the formulation, thus minimizing the set of parameters to identify. Inthecaseofcubicmaterial,theHooketensor B depends only on three elastic constants: E, v and G. According to , the fourth order secant elastoplastic tensor can be written at load step n as: −1 s e −1 B = B + P (6) 1 + kγ where P is the constant mapping matrix: ⎡ ⎤ 2 −10 ⎢ ⎥ P = −12 0 (7) ⎣ ⎦ and γ is the plastic multiplier that depends, for linear kinematic hardening, on the material parameters: 3 3 α γ σ ,k = − 1 (8) ( ) n 0 2k 2 σ with α the positive part of a, α the second invariant of the eﬀective stress, X the n n backstress tensor reached at the current load step: 2 c c α = σ − X .P. σ − X (9) n n n n n The expression of the secant modulus B plays a central role in the proposed method since it governs the deﬁnition of the plastic parameter K which involves the plastic parameters n Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 4 of 13 to be identiﬁed. The secant tensor at time step n can also be expressed with respect to material parameters: ⎡ ⎤ E(1+2K E) E(ν+K E) n n 2 2 2 2 2 2 3K E −2K E(ν−2)+1−ν 3K E −2K E(ν−2)+1−ν n n n n ⎢ ⎥ ⎢ ⎥ s E(ν+K E) E(1+2K E) n n B = ⎢ 0 ⎥ (10) 2 2 2 2 2 2 3K E −2K E ν−2 +1−ν 3K E −2K E ν−2 +1−ν ( ) ( ) n n n n ⎣ ⎦ 2G 1+12K G −1 e c Finally, since the plastic deformation at load step n is equal to ε = ε − B : σ ,the plastic parameter K , can be expressed as a function of two material parameters a and b: K = a (11) b + ε 1 σ with a = and b = in the case of a linear kinematic hardening. Note that when the 2h h load step n is purely elastic, the plastic strain is vanishing and the plastic parameter K is s e equal to zero: the plastic secant tensor B is thus equal to the elastic tensor B . Furthermore, the Eqs. (10)and (11) show that the plastic secant tensor depends on set of the elastoplastic parameters p = [E, v, G, σ ,h] and also on the norm of the plastic deformation ε so: s s p p B = B q , ε where q are the phases (material domains) of the specimen and q n n the material parameters of phase q. To compute the ERC functional, several ﬁelds are to be deﬁned: (1) the phase distribution (related to the material heterogeneity), (2) the stress ﬁelds (related to the development of structural eﬀects in the specimen), and ﬁnally (3) the experimental displacement ﬁelds obtained by DIC. These three ﬁelds are discretized using diﬀerent meshes with adapted mesh size and shape functions. The meshes are “nested”, the coarser being the material mesh, the ﬁner being the DIC mesh, and the intermediate being the stress mesh. These three meshes are described with diﬀerent shape functions. The stresses are determined through a FE computation using bilinear displacement elements and bilinear shape functions are used to perform the local DIC computation. The continuity of the measured displacement ﬁeld is enforced by averaging the displacement vector on the mesh vertices and the mechanical properties are constant on each material domain. Moreover, it is possible to use diﬀerent stress meshes in both elastic and plastic identiﬁcation. The ‘plastic’ mesh was a subdivision of the ‘elastic’ mesh in order to reduce the inﬂuence of the noise on the identiﬁcation while maintaining a convenient description of the stress gradients. As conform meshes, these nested meshes simplify the transfers of ﬁelds from one mesh to another. Finally, the direct simulations are performed by imposing constant vertical displacement on the upper boundary and blocking vertical displacement on the lower boundary. The left and right boundaries are stress free. Moreover we set the displacement of one point of the lower boundary to zero in order to suppress the possible horizontal rigid body motion. Numerical method Due to the convexity of the E functional, the minimization is performed in two steps, rc leading to the relaxation algorithm presented in Fig. 1a: the function is minimized with − → respect to the displacement ﬁeld u associated with a statically admissible stress ﬁeld, and then with respect to the secant tensor B . n Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 5 of 13 Fig. 1 a Example of E minimization algorithm used for the step (i) and step (iii), b global identiﬁcation rc algorithm As already mentioned, we focus on a J2 elastoplastic model with kinematic hardening and no more than three material constants can be locally identiﬁed at each load step. The identiﬁcation algorithm involves three steps (see Fig. 1b): (i) an elastic identiﬁcation, (ii) a plasticity detection and (iii) a plastic identiﬁcation. The elastic and plastic parameters are thus identiﬁed separately and are based on the minimization of the E cost function. Nevertheless, in either situation, the ﬁrst minimiza- rc tion is identical: it consists in a classical direct ﬁnite element computation for a known heterogeneous material under given boundary conditions. This minimization will thus not be discussed in the following. The elastic constants (i) are identiﬁed by minimizing the functional E with respect to the elastic tensor B . The iterative minimization process rc starts with an initial set of parameters B chosen by the user at n = 0 and taken equal to the elastic parameters identiﬁed on the previous load step when n > 1. The proce- dure is stopped using a convergence criterion deﬁned on the norm of the correction in secant tensor. The plastic identiﬁcation problem consists in determining the elastoplastic parameters involved in the secant stiﬀness tensor B . The plastic identiﬁcation (step (iii)) is less direct since the secant tensor expression requires knowledge of the stress state. For all the plastic load steps, the secant tensor is initialized using the results of an elastic estimation B , and gives a statically admissible 0,n stress state. The plastic identiﬁcation consists in minimizing E with respect to the plastic param- rc eters (a and b for a linear kinematic hardening). Once the procedure has converged, we get the stress ﬁeld σ and the optimal values (a ,b ) that are directly related to the opt opt n Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 6 of 13 material parameters σ and h. In this case, the minimization is performed numerically, due to the lack of analytical solution. For a given behaviour (elasticity or plasticity), and a given load step n, the identiﬁcation algorithm presented in Fig. 1a is performed until convergence. In both cases, the con- vergence criterion is computed on the secant tensor B identiﬁed at iteration i and on the one identiﬁed at the previous (i − 1) iteration: B , with typical values of the i−1 convergence criterion of 0.001: s s s B − B < B (12) n n n i i−1 i 2 2 The plasticity detection (step (ii)) consists in comparing the secant tensor B identiﬁed at the current load step N and the one identiﬁed at the previous (N − 1) load step B : N −1 s s s B − B > B (13) N N −1 N with about 0.05. If this criterion is validated, we assume that the plasticity has started developing during the current load step N. The last elastic load step is denoted by N .The plastic identiﬁcation (step (iii)) is performed only for the load steps greater than N (the overall loading is supposed to be monotonous). Once the procedure has been completed, we get the statically admissible stress ﬁeld for all the load steps and the set of identiﬁed material properties. Validations In this section, the eﬃciency of the proposed procedure is examined using reference simulated measurements obtained with the ﬁnite element code Comsol Multiphysics. Only simulated measurements are considered in this paper in order to focus only on the identiﬁcation procedure performance and on the enhanced features of the formulation. The in-plane components of the displacement ﬁeld are extracted at the nodes of the ﬁnite element mesh and the global load levels are extracted on the outer boundaries. We have used diﬀerent meshes for the direct computation and for the deﬁnition of the “measurement grid”. The errors on the identiﬁed parameters can have diﬀerent origins. They can be related to errors on the measured displacement (i.e. DIC errors), on the geometry description (domain and boundaries), on the behavior law (description of the secant tensor B ), on the phase description (material mesh), or on the stress description (stress mesh and boundary conditions used for the stress computation). In this paper we address the inﬂuence of DIC errors, of phase description errors and, in a lesser extent of stress description errors. Since conform meshes are used, phase description errors are related both to stress description errors and to measured displacement errors. The inﬂuence of the measured displacement ﬁeld characteristics (mesh size and noise level) on the identiﬁcation results is illustrated on numerical examples corresponding to homogeneous and heterogeneous materials subjected to a tensile test. It is well known that several error regimes can be encountered using DIC, noticeably the shape function mismatch regime and the ultimate error regime . The shape function error is prepon- derant when the shape function used in the DIC formulation does not match the actual displacement ﬁeld. It is governed by the ﬁrst neglected term in the shape function and Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 7 of 13 it tends to increase when the subset size is increased. The ultimate error is encountered when the shape function is rich enough to describe the real displacement ﬁeld. It is gov- erned by the image noise σ , the average image gradient and the subset size d and it tends to decrease when the subset size increases . These two aspects were separately investigated. The inﬂuence of the shape function mismatch error is examined using diﬀer- ent displacement meshes leading to diﬀerent mismatch error levels. The inﬂuence of the ultimate error is investigated solely by randomly perturbing the measured displacement ﬁeld. Results Elastic identiﬁcation The ﬁrst test (specimen 1) is performed on an elastic bi-material sample: a soft circular isotropic inclusion (Young’s modulus 100 GPa, Poisson ratio 0.15) is embedded in a stiﬀ isotropic matrix (Young’s modulus 210 GPa, Poisson ratio 0.3). Two types of identiﬁcation mesh are used (Fig. 2): • An identiﬁcation mesh perfectly consistent with the material domains (two iden- tiﬁcation domains D and D corresponding respectively to the inclusion and the 1 2 matrix); • An identiﬁcation mesh that does not match the material heterogeneity by splitting it into 400 domains D with j = 1–400. Only one load level is used in the identiﬁcation procedure. The typical computing time on a Z820 workstation (Intel Xeon 2.40 GHz) computer is about 5 mins for a measurement mesh involving 1448 linear elements. For the ﬁrst identiﬁcation, Fig. 3a shows a good prediction of the parameter sets with a relative error of about 1% on the Young’s modulus. In this case, the shape function mismatch error is small since the meshes used for the direct computation (considered as the “ground truth”) and for the identiﬁcation are identical. Fig. 2 Geometry for the identiﬁcation: a two identiﬁcation domains respecting the material heterogeneity and b 400 identiﬁcation domains that do not respect the material heterogeneity Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 8 of 13 Fig. 3 Identiﬁed Young modulus distributions: a 2 “consistent” identiﬁcation domains and b 400 non-consistent identiﬁcation domains Fig. 4 Distribution of transversal stress ﬁelds: a “measured” stress ﬁeld σ (from FE simulation), b identiﬁed yy c c stress ﬁeld σ using 2 “consistent” identiﬁcation domains and c identiﬁed stress ﬁeld σ using 400 yy yy non-consistent identiﬁcation domains Figure 4b shows that the identiﬁed stress ﬁelds are very close to the reference values. The maximum error is about 4% of the maximum stress (295 MPa) and is located in the zones of maximum stress gradient. For the second identiﬁcation (involving 400 non-consistent material domains), the posi- tion of the inclusion is very well identiﬁed (see Fig. 3b). This result shows the ability of the method to identify heterogeneous elastic properties without any a priori knowledge of the phase distribution. The error increases with the stress and strain gradients where the shape function mismatch error is the most signiﬁcant. Furthermore, it can be observed that the error on the identiﬁed Young’s modulus is concentrated above and under the inclusion where the deformation energy is minimal. This error is also important on the material domains intersecting the actual boundary between the inclusion and the matrix where the method tends to average the elastic constants of the two phases. The computational time depends on the number of unknowns which increases here from 4 in the previous case up to 800 in the case of 400 non-consistent material domains. It goes up to 8 mins on the same computer for the elastic identiﬁcation. Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 9 of 13 a b Fig. 5 a Geometry and identiﬁcation domains and b the 5 load steps Elastoplastic identiﬁcation The second test (specimen 2) concerns a standard tensile test performed at constant velocity on an isotropic elastoplastic material (Young’s modulus 210 GPa, Poisson ratio 0.3, yield stress 300 MPa and hardening modulus 1 GPa) (Fig. 5a). The material parameters are identiﬁed using data associated with 5 load steps (2 steps in the elastic domain, and 3 in the plastic domain) (Fig. 5b). Although the material is homogeneous, the identiﬁcation is made on 4 material domains (Fig. 5a) in order to demonstrate the ability of the procedure to identify the elastoplastic properties in several domains. Identiﬁed parameters obtained from each zone are collected in Table 1.Ascan be noticed, the identiﬁed parameter values are very close to the reference values and are very similar from one identiﬁcation domain to another. The reference (“measured”) stress ﬁelds presented in Fig. 6a are obtained by solving the direct problem whereas the stress ﬁelds presented in Fig. 6b are obtained by the inverse method. We notice a close similarity between the distributions and the orders of magnitude for this stress component. The procedure converges in few iterations. The identiﬁcation of the parameters and of the given stress ﬁelds is very accurate. In this case, diﬀerent meshes were used for the direct computation and the identiﬁcation: the former is triangular and the latter is quadrangular (see Fig. 6a, b). The mesh used to describe the “measured” displacement ﬁeld (not represented here) is quadrangular but similar in size to the one used for the direct simulation thus limiting the bias introduced in the description of the “measured” displacement. The mesh used to compute the stress ﬁeld is coarser than the one used for the direct simulation thus introducing a shape function mismatch. Here, the fact that the identiﬁcation results are very close to the reference values show that the shape function mismatches is too small to generate signiﬁcant discrepancies in the identiﬁcation. The typical computing time on the same computer is about 6 mins for a measurement mesh involving 1448 linear elements. This computing time depends on the number of the load step used for the elasto-plastic identiﬁcation. Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 10 of 13 Table 1 Identiﬁed parameters: specimen 2 Parameters Reference values Z1 Z2 Z3 Z4 E (GPa) 210 209.80 210.05 210.12 209.86 Relative diﬀerence (%) 0.09 0.02 0.05 0.06 v 0.3 0.2998 0.3002 0.3001 0.2998 Relative diﬀerence (%) 0.06 0.06 0.03 0.06 k (GPa) 1 1.032 0.998 1.015 0.997 Relative diﬀerence (%) 3.20 0.20 1.50.30 σ (MPa) 300 298.49 299.51 299.38 299.60 Relative diﬀerence (%) 0.50 0.16 0.20 0.13 Sensitivity to the initial set of parameters To assess the sensitivity of the identiﬁcation results to initial values, diﬀerent starting values of the parameters are selected for the procedure. The identiﬁcation is performed on specimen 2 and we check the number of iterations required for the procedure to converge. Table 2 shows that the identiﬁed parameters are very stable with respect to the chosen initial values. As mentioned earlier, the initial set of parameters is only used for the elastic identiﬁcation of the ﬁrst load step. No ad hoc initiation is required for the identiﬁcation of plastic parameters. m c Fig. 6 Distribution of transversal stress ﬁelds: a σ from a FE simulation, b σ the identiﬁed and c von Mises yy yy norm of the absolute error on stress Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 11 of 13 Table 2 Sensitivity to initial set of parameters: specimen 2 Parameters E (GPa) vk (GPa) σ (MPa) Initial value 1 1.00E−09 1.00E−09 1.00E−09 1.00E−09 Identiﬁed values 209.74 0.3002 1.032 300.10 Numberofiterations 7788 Initial value 2 155.00 0.15 0.50 150.00 Identiﬁed values 209.77 0.3003 1.031 300.10 Numberofiterations 7766 Initial value 3 420.00 0.60 2.00 600.00 Identiﬁed values 209.84 0.3003 1.033 300.09 Numberofiterations 8866 Table 3 Sensitivity to noise: specimen 2 Parameters Reference values Random noise Random noise Random noise amplitude 0.1 ∗ γ amplitude γ amplitude 2 ∗ γ E (GPa) 210 209.79 206.96 189.56 Relative diﬀerence (%) 0.11 1.45 9.73 v 0.3 0.3000 0.3001 0.2324 Relative diﬀerence (%) 0 0.03 23.53 k (GPa) 1 0.998 0.998 0.963 Relative diﬀerence (%) 0.20 0.20 3.70 σ (MPa) 300 300.17 299.84 300.46 Relative diﬀerence (%) 0.06 0.05 0.15 Sensitivity to experimental noise The robustness of the CEGM approach with respect to noise is evaluated using a set of simulated displacement ﬁelds disturbed by a white noise at diﬀerent levels. For this purpose, we perform an identiﬁcation on a homogeneous isotropic material submitted to a tensile test. The reference FE-displacement ﬁelds are corrupted by a white Gaussian noise with the amplitude γ. The noise level is chosen to γ = 0.01 pixel while the maximum displacement is 1.5 pixels. This value was chosen to be consistent with our classical test conﬁgurations: image noise σ ≈ 0.54 grey levels (obtained using a HR16070MFLGEC camera, and a 16-bits acquisition mode), coarse speckle (speckle dots with 3-pixels radius, corresponding to an average image gradient ∇I ≈ 90), and 20-pixels subset size (d). This value is consistent with the value σ / d ∇I ≈ 0.003 given in . It can be seen that identiﬁcation of all parameters is stable in presence of noise. As expected, the elastic constants are more corrupted by the noise level as, for a ﬁxed noise level, the signal to noise ratio is smaller for the elastic identiﬁcation than for the plastic one. Furthermore, the Poisson ratio is more sensitive to measurement noise. The results presented in Table 3 are obtained using a stress identiﬁcation mesh equal to the mesh used to obtain the direct problem and also without ﬁltering the noise. But in our minimization algorithm we have several types of meshes that can have equal or diﬀerent sizes. In order to reduce the problem of measurement noise in the identiﬁcation while maintaining a good description of the stress gradients, these meshes are not identical but they are imbricated i.e. the mesh of the plastic identiﬁcation is a subdivision of the elastic meshes. Madani et al. Adv. Model. and Simul. in Eng. Sci. (2017) 4:5 Page 12 of 13 Conclusions In the present work, we use full-ﬁeld measurements and the constitutive equation gap method to identify the spatial distribution of a set of material parameters associated with a J2 elastoplastic behaviour. The identiﬁcation approach is based on the minimization of the CEG energy norm and allows the identiﬁcation of a set of unknown parameters in any chosen zone without any prior knowledge of the distribution of the spatial heterogeneities. We validate this approach on diﬀerent materials in diﬀerent situations (heterogeneous elastic materials, homogeneous plastic material involving heterogeneous stress ﬁelds). These diﬀerent numerical tests give results which are in good agreement with the imposed value obtained using the direct problem. The identiﬁcation accuracy strongly depends on the accuracy of the input data (measured load and displacement) and of the representa- tiveness of the model (geometry, boundary conditions, material heterogeneity, behaviour law, stress distribution ...). Direct numerical simulations are performed in order to get the data required for the identiﬁcation in a “perfectly controlled” situation. The results of these simulations are considered as a “ground truth” (thus neglecting the simulation error). The sensitivity of the identiﬁcation results with respect to diﬀerent parameters are investigated by perturbing the numerical solution. To restrict the scope of the study, we have focused here on three contributions: the measured displacement error, the shape function mismatch in the stress distribution and the description of the material hetero- geneity. We have veriﬁed that typical error levels associated with the ultimate error regime of the displacement measurement led to relative errors smaller than 5% on the identiﬁca- tion results (the elastic parameters being more sensitive to noise due to smaller signal to noise ratio). As expected, the shape function mismatch error is larger in stress concentra- tion areas, and the identiﬁcation error is more important in stress concentration zones. Special attention should be taken to choose a material mesh consistent with the phase distribution and to deﬁne a stress mesh ﬁne enough to catch the stress concentrations. Finally, we have veriﬁed that the identiﬁcation results are not aﬀected by the choice of the initial guess on the elastic parameters required to start the procedure. Future works will focus on the use of experimental data and the improvement of the method to deal with respect to multi-linear problems. Authors’ contributions TM performed the research and wrote the paper. YM supervised the research and the paper. CP supervised the research and the paper. SP supervised the research and the paper. BW supervised the research and the paper. All authors read and approved the ﬁnal manuscript. Author details IRSN/PSN-RES/SEMIA/LPTM, Institut de Radioprotection et de Sûreté Nucléaire, BP3-13115, Saint-Paul-lez-Durance 2 3 Cedex, France, LMGC, Université de Montpellier, CNRS, Montpellier, France, Laboratoire de micromécanique et intégrité des structures (MIST), IRSN-CNRS-Université de Montpellier, Montpellier, France. Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional aﬃliations. Received: 19 May 2017 Accepted: 15 November 2017 References 1. Kavanagh KT, Clough RW. Finite element applications in the characterization of elastic solids. Int J Solids Struct. 1971;7:11–23. 2. Meuwissen MHH, Oomens CWJ, Baaijens FPT, Petterson R, Janssen JD. Determination of the elasto-plastic properties of aluminum using a mixed numerical-experimental method. J Mater Process Technol. 1998;75(1–3):204211. Madani et al. Adv. Model. and Simul. in Eng. 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"Advanced Modeling and Simulation in Engineering Sciences" – Springer Journals
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