SAM
https://sam.ensam.eu:443
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 13 Jul 2024 19:14:08 GMT2024-07-13T19:14:08ZDetermination of the characteristic parameters of tension-compression asymmetry of Shape Memory Alloys using full-field measurements
http://hdl.handle.net/10985/11190
Determination of the characteristic parameters of tension-compression asymmetry of Shape Memory Alloys using full-field measurements
CHEMISKY, Yves; ECHCHORFI, Rachid; BOURGEOIS, Nadine; PIOTROWSKI, Boris; MERAGHNI, Fodil
In this work, a method for the identification of the transformation surface of Shape Memory Alloys based on full field measurements is presented. An inverse method coupled with a gradient-based algorithm has been developed to determine the characteristic parameters of the transformation surface. The constitutive equations of the chosen model that capture the macroscopic behavior of Shape Memory Alloys are first presented. The material parameters, to be identified, that are characteristic of the tension-compression asymmetry of the alloy are detailed. The identification algorithm, based on full field measurements obtained by Digital Image Correlation (DIC) and numerical simulation by Finite Element Analysis are introduced. The identification algorithm is validated using a numerically generated strain field on a Meuwissen-type specimen.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/111902013-01-01T00:00:00ZCHEMISKY, YvesECHCHORFI, RachidBOURGEOIS, NadinePIOTROWSKI, BorisMERAGHNI, FodilIn this work, a method for the identification of the transformation surface of Shape Memory Alloys based on full field measurements is presented. An inverse method coupled with a gradient-based algorithm has been developed to determine the characteristic parameters of the transformation surface. The constitutive equations of the chosen model that capture the macroscopic behavior of Shape Memory Alloys are first presented. The material parameters, to be identified, that are characteristic of the tension-compression asymmetry of the alloy are detailed. The identification algorithm, based on full field measurements obtained by Digital Image Correlation (DIC) and numerical simulation by Finite Element Analysis are introduced. The identification algorithm is validated using a numerically generated strain field on a Meuwissen-type specimen.Analysis of the deformation paths and thermomechanical parameter identification of a shape memory alloy using digital image correlation over heterogeneous tests
http://hdl.handle.net/10985/9969
Analysis of the deformation paths and thermomechanical parameter identification of a shape memory alloy using digital image correlation over heterogeneous tests
CHEMISKY, Yves; BOURGEOIS, Nadine; CORNELL, Stephen; ECHCHORFI, Rachid; PATOOR, Etienne; MERAGHNI, Fodil
With the design of new devices with complex geometry and to take advantage of their large recoverable strains, shape memory alloys components (SMA) are increasingly subjected to multiaxial loadings. The development process of SMA devices requires the prediction of their thermomechanical response, for which the calibration of the material parameters for the numerical model is an important step. In this work, the parameters of a phenomenological model are extracted from tests performed on specimens with non-uniform geometry, which induce heterogeneous strain fields carried out on specimens with the same thermomechanical loading history. The digital image correlation technique is employed to measure the strain fields on the surface of the specimen and to analyze the strain paths of chosen points. Finite element analysis enables the computation of numerical strain fields using a thermodynamical constitutive model for shape memory alloys previously implemented in a finite element code. The strain fields computed numerically are compared with experimental ones obtained by DIC to find the model parameters which best match experimental measurements using a newly developed parallelized mixed genetic/gradient-based optimization algorithm. These numerical simulations are carried out in parallel using a supercomputer to reduce the time necessary to identify the set of model parameters. The major features of this new algorithm is its ability to identify the material parameters which describe the thermomechanical behavior of shape memory alloys from full-field measurements for various loading conditions (different temperatures, multiaxial behavior, heterogeneous test configurations). It is demonstrated that model parameters for the simulation of SMA structures are thus obtained based on a reduced number of heterogeneous tests at different temperatures.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/99692015-01-01T00:00:00ZCHEMISKY, YvesBOURGEOIS, NadineCORNELL, StephenECHCHORFI, RachidPATOOR, EtienneMERAGHNI, FodilWith the design of new devices with complex geometry and to take advantage of their large recoverable strains, shape memory alloys components (SMA) are increasingly subjected to multiaxial loadings. The development process of SMA devices requires the prediction of their thermomechanical response, for which the calibration of the material parameters for the numerical model is an important step. In this work, the parameters of a phenomenological model are extracted from tests performed on specimens with non-uniform geometry, which induce heterogeneous strain fields carried out on specimens with the same thermomechanical loading history. The digital image correlation technique is employed to measure the strain fields on the surface of the specimen and to analyze the strain paths of chosen points. Finite element analysis enables the computation of numerical strain fields using a thermodynamical constitutive model for shape memory alloys previously implemented in a finite element code. The strain fields computed numerically are compared with experimental ones obtained by DIC to find the model parameters which best match experimental measurements using a newly developed parallelized mixed genetic/gradient-based optimization algorithm. These numerical simulations are carried out in parallel using a supercomputer to reduce the time necessary to identify the set of model parameters. The major features of this new algorithm is its ability to identify the material parameters which describe the thermomechanical behavior of shape memory alloys from full-field measurements for various loading conditions (different temperatures, multiaxial behavior, heterogeneous test configurations). It is demonstrated that model parameters for the simulation of SMA structures are thus obtained based on a reduced number of heterogeneous tests at different temperatures.Parameter identification of a thermodynamic model for superelastic shape memory alloys using analytical calculation of the sensitivity matrix
http://hdl.handle.net/10985/9966
Parameter identification of a thermodynamic model for superelastic shape memory alloys using analytical calculation of the sensitivity matrix
CHEMISKY, Yves; PIOTROWSKI, Boris; ECHCHORFI, Rachid; BOURGEOIS, Nadine; PATOOR, Etienne; MERAGHNI, Fodil
This paper presents an identification procedure for the parameters of a thermodynamically based constitutive model for Shape memory Alloys (SMAs). The proposed approach is a gradient-based method and utilizes an analytical computation of the sensitivity matrix. For several loading cases, including superelasticity, that are commonly utilized for the model parameters identification of such a constitutive model, a closed-form of the total infinitesimal strain is derived. The partial derivatives of this state variable are developed to find the components of the sensitivity matrix. A LevenbergeMarquardt algorithm is utilized to solve the inverse problem and find the best set of model parameters for specific SMA materials. Moreover, a pre-identification method, based on the second derivative of the total strain components is proposed. This provides a suitable initial set of model parameters, which increases the efficiency of the inverse method. The proposed approach is applied for the simultaneous identification of the non-linear constitutive parameters for two superelastic SMAs. The comparison between experimental and numerical curves obtained for different temperatures shows the capabilities of the developed identification approach. The robustness and the efficiency of the developed approach are then experimentally validated
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Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10985/99662014-01-01T00:00:00ZCHEMISKY, YvesPIOTROWSKI, BorisECHCHORFI, RachidBOURGEOIS, NadinePATOOR, EtienneMERAGHNI, FodilThis paper presents an identification procedure for the parameters of a thermodynamically based constitutive model for Shape memory Alloys (SMAs). The proposed approach is a gradient-based method and utilizes an analytical computation of the sensitivity matrix. For several loading cases, including superelasticity, that are commonly utilized for the model parameters identification of such a constitutive model, a closed-form of the total infinitesimal strain is derived. The partial derivatives of this state variable are developed to find the components of the sensitivity matrix. A LevenbergeMarquardt algorithm is utilized to solve the inverse problem and find the best set of model parameters for specific SMA materials. Moreover, a pre-identification method, based on the second derivative of the total strain components is proposed. This provides a suitable initial set of model parameters, which increases the efficiency of the inverse method. The proposed approach is applied for the simultaneous identification of the non-linear constitutive parameters for two superelastic SMAs. The comparison between experimental and numerical curves obtained for different temperatures shows the capabilities of the developed identification approach. The robustness and the efficiency of the developed approach are then experimentally validatedIdentification and interpretation of material parameters of a shape memory alloy (SMA) model
http://hdl.handle.net/10985/10575
Identification and interpretation of material parameters of a shape memory alloy (SMA) model
PIOTROWSKI, Boris; CHEMISKY, Yves; ECHCHORFI, Rachid; BOURGEOIS, Nadine; PATOOR, Etienne; MERAGHNI, Fodil
The thermomechanical behavior of Shape Memory Alloys (SMAs) is described by many micromechanical and phenomenological models. The first ones have material parameters whose physical meaning is based on the crystallography of the phase transformation related to the studied alloy. In contrast, phenomenological models often have material parameters whose physical meaning is not obvious and that makes them difficult to identify, some of which are based on mathematical considerations. In this paper, we propose to use the formulation of the phenomenological model of Chemisky et al., and to consider the particular case of a superelastic SMA. In this case, the constitutive equation should be easily expressed analytically through the strain tensor as a function of applied load direction and material parameters. The behavior is then characterized by a complete and proportional loading. This analytical model contains 7 material parameters, 1 related to the elasticity and 6 to the phase transformation. Based on several isothermal tensile tests at various temperatures, material parameters of this model are identified using the Levenberg-Marquardt algorithm and an analytical calculation of the sensitivity matrix. Their physical meaning and their influence on the thermomechanical behavior of the studied alloy are highlighted and discussed.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10985/105752013-01-01T00:00:00ZPIOTROWSKI, BorisCHEMISKY, YvesECHCHORFI, RachidBOURGEOIS, NadinePATOOR, EtienneMERAGHNI, FodilThe thermomechanical behavior of Shape Memory Alloys (SMAs) is described by many micromechanical and phenomenological models. The first ones have material parameters whose physical meaning is based on the crystallography of the phase transformation related to the studied alloy. In contrast, phenomenological models often have material parameters whose physical meaning is not obvious and that makes them difficult to identify, some of which are based on mathematical considerations. In this paper, we propose to use the formulation of the phenomenological model of Chemisky et al., and to consider the particular case of a superelastic SMA. In this case, the constitutive equation should be easily expressed analytically through the strain tensor as a function of applied load direction and material parameters. The behavior is then characterized by a complete and proportional loading. This analytical model contains 7 material parameters, 1 related to the elasticity and 6 to the phase transformation. Based on several isothermal tensile tests at various temperatures, material parameters of this model are identified using the Levenberg-Marquardt algorithm and an analytical calculation of the sensitivity matrix. Their physical meaning and their influence on the thermomechanical behavior of the studied alloy are highlighted and discussed.Identification of Model Parameter for the Simulation of SMA Structures Using Full Field Measurements
http://hdl.handle.net/10985/10837
Identification of Model Parameter for the Simulation of SMA Structures Using Full Field Measurements
CHEMISKY, Yves; BOURGEOIS, Nadine; CORNELL, Stephen; ECHCHORFI, Rachid; PATOOR, Etienne; MERAGHNI, Fodil
With the design of new devices with complex geometry and to take advantage of their large recoverable strains, shape memory alloys components (SMA) are increasingly subjected to multiaxial loadings. The development process of SMA devices requires the prediction of their thermomechanical response, where the calibration of the material parameters for the numerical model is an important step. In this work, the parameters of a phenomenological model are extracted from multiaxial and heterogeneous tests carried out on specimens with the same thermomechanical loading history. Finite element analysis enables the computation of numerical strain fields using a thermodynamical constitutive model for shape memory alloys previously implemented in a finite element code. The strain fields computed numerically are compared with experimental ones obtained by DIC to find the model parameters which best matches experimental measurements using a newly developed parallelized mixed genetic/gradient-based optimization algorithm. These numerical simulations are carried out in parallel in a supercomputer to reduce the time necessary to identify the set of identified parameters. The major features of this new algorithm is its ability to identify material parameters of the thermomechanical behavior of shape memory alloys from full-field measurements for various loading conditions (different temperatures, multiaxial behavior, heterogeneous test configurations). It is demonstrated that model parameters for the simulation of SMA structures are thus obtained based on a reduced number of heterogeneous tests at different temperatures.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/108372015-01-01T00:00:00ZCHEMISKY, YvesBOURGEOIS, NadineCORNELL, StephenECHCHORFI, RachidPATOOR, EtienneMERAGHNI, FodilWith the design of new devices with complex geometry and to take advantage of their large recoverable strains, shape memory alloys components (SMA) are increasingly subjected to multiaxial loadings. The development process of SMA devices requires the prediction of their thermomechanical response, where the calibration of the material parameters for the numerical model is an important step. In this work, the parameters of a phenomenological model are extracted from multiaxial and heterogeneous tests carried out on specimens with the same thermomechanical loading history. Finite element analysis enables the computation of numerical strain fields using a thermodynamical constitutive model for shape memory alloys previously implemented in a finite element code. The strain fields computed numerically are compared with experimental ones obtained by DIC to find the model parameters which best matches experimental measurements using a newly developed parallelized mixed genetic/gradient-based optimization algorithm. These numerical simulations are carried out in parallel in a supercomputer to reduce the time necessary to identify the set of identified parameters. The major features of this new algorithm is its ability to identify material parameters of the thermomechanical behavior of shape memory alloys from full-field measurements for various loading conditions (different temperatures, multiaxial behavior, heterogeneous test configurations). It is demonstrated that model parameters for the simulation of SMA structures are thus obtained based on a reduced number of heterogeneous tests at different temperatures.