Measuring stress field without constitutive equation
•Data Driven Identification technique is applied to measured displacements and forces.•Experimental stress field is determined without presupposing constitutive equation.•Practical solutions to use Digital Image Correlation raw data are provided.•The technique is applied to a perforated elastomer sh...
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| Published in: | Mechanics of materials Vol. 136; p. 103087 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.09.2019
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| ISSN: | 0167-6636, 1872-7743 |
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| Abstract | •Data Driven Identification technique is applied to measured displacements and forces.•Experimental stress field is determined without presupposing constitutive equation.•Practical solutions to use Digital Image Correlation raw data are provided.•The technique is applied to a perforated elastomer sheet under large strain.
The present paper proposes a coupled experimental-numerical protocol to measure heterogeneous stress fields in a model-free framework. This work contributes to developing the emerging realm of mechanics without constitutive equation. In fact, until now these types of simulations have been missing database of real and rich material behaviour. The technique consists in coupling Digital Image Correlation (DIC) measurements and the Data Driven Identification (DDI) algorithm, recently developed by Leygue et al. (Data–based derivation of material response. Computer Methods in Applied Mechanics and Engineering, 331, 184–196 (2018)). This algorithm identifies the mechanical response of a material, i.e. stress–strain data, without postulating any constitutive equation, but with the help of a large database of displacement fields and loading conditions. The only governing equation used in this algorithm is the mechanical equilibrium. The bias induced by the choice and the calibration of a constitutive model is thus removed. In the above-mentioned paper, the relevance of the method has been demonstrated with synthetic data issued from finite element computations. Here, its efficiency is assessed with “real” experimental data. A multi-perforated elastomer membrane is uniaxially stretched in large strain. Practical challenges are addressed when using the DDI algorithm, especially those due to unmeasured data during experiments. Easy-to-implement solutions are proposed and the DDI technique is successfully applied: heterogeneous stress fields are computed from real data. Considering that the material is elastic, its strain energy density is then measured. Good agreement with standard uniaxial tensile experimental results validates the approach on real data. |
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| AbstractList | The present paper proposes a coupled experimental-numerical protocol to measure heterogeneous stress fields in a model-free framework. This work contributes to developing the emerging realm of mechanics without constitu-tive equation. In fact, until now these types of simulations have been missing database of real and rich material behaviour. The technique consists in coupling Digital Image Correlation (DIC) measurements and the Data Driven Identification (DDI) algorithm, recently developed by Leygue et al.(Data-based derivation of material response. Computer Methods in Applied Mechanics and Engineering 331, 184-196 (2018)). This algorithm identifies the mechanical response of a material, i.e. stress-strain data, without postulating any constitutive equation, but with the help of a large database of displacement fields and loading conditions. The only governing equation used in this algorithm is the mechanical equilibrium. The bias induced by the choice and the calibration of a constitutive model is thus removed. In the above-mentioned paper, the relevance of the method has been demonstrated with synthetic data issued from finite element computations. Here, its efficiency is assessed with "real" experimental data. A multi-perforated elastomer membrane is uniaxially stretched in large strain. Practical challenges are addressed when using the DDI algorithm, especially those due to unmeasured data during experiments. Easy-to-implement solutions are proposed and the DDI technique is successfully applied: heterogeneous stress fields are computed from real data. Considering that the material is elastic, its strain energy density is then measured. Good agreement with standard uniaxial tensile experimental results validates the approach on real data. •Data Driven Identification technique is applied to measured displacements and forces.•Experimental stress field is determined without presupposing constitutive equation.•Practical solutions to use Digital Image Correlation raw data are provided.•The technique is applied to a perforated elastomer sheet under large strain. The present paper proposes a coupled experimental-numerical protocol to measure heterogeneous stress fields in a model-free framework. This work contributes to developing the emerging realm of mechanics without constitutive equation. In fact, until now these types of simulations have been missing database of real and rich material behaviour. The technique consists in coupling Digital Image Correlation (DIC) measurements and the Data Driven Identification (DDI) algorithm, recently developed by Leygue et al. (Data–based derivation of material response. Computer Methods in Applied Mechanics and Engineering, 331, 184–196 (2018)). This algorithm identifies the mechanical response of a material, i.e. stress–strain data, without postulating any constitutive equation, but with the help of a large database of displacement fields and loading conditions. The only governing equation used in this algorithm is the mechanical equilibrium. The bias induced by the choice and the calibration of a constitutive model is thus removed. In the above-mentioned paper, the relevance of the method has been demonstrated with synthetic data issued from finite element computations. Here, its efficiency is assessed with “real” experimental data. A multi-perforated elastomer membrane is uniaxially stretched in large strain. Practical challenges are addressed when using the DDI algorithm, especially those due to unmeasured data during experiments. Easy-to-implement solutions are proposed and the DDI technique is successfully applied: heterogeneous stress fields are computed from real data. Considering that the material is elastic, its strain energy density is then measured. Good agreement with standard uniaxial tensile experimental results validates the approach on real data. |
| ArticleNumber | 103087 |
| Author | Coret, Michel Dalémat, Marie Verron, Erwan Leygue, Adrien |
| Author_xml | – sequence: 1 givenname: Marie surname: Dalémat fullname: Dalémat, Marie email: marie.dalemat@ec-nantes.fr – sequence: 2 givenname: Michel surname: Coret fullname: Coret, Michel – sequence: 3 givenname: Adrien surname: Leygue fullname: Leygue, Adrien – sequence: 4 givenname: Erwan surname: Verron fullname: Verron, Erwan |
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| Cites_doi | 10.1016/j.cma.2015.09.004 10.1007/s11831-016-9197-9 10.1016/j.cma.2013.04.007 10.1016/j.polymertesting.2008.05.011 10.1016/j.cma.2017.08.027 10.1007/s11340-017-0329-4 10.1016/j.cma.2017.11.013 10.1016/j.optlaseng.2008.05.005 10.1016/j.ijsolstr.2012.09.002 10.1016/j.cma.2019.02.016 10.1016/j.jbiomech.2013.05.003 10.1002/(SICI)1097-0207(19980930)43:2<195::AID-NME418>3.0.CO;2-6 10.1016/j.cma.2017.07.039 10.5254/1.3547969 10.1002/nme.5721 10.1007/s11340-012-9603-7 10.1002/nme.5716 10.1016/j.compstruc.2017.07.031 10.1016/j.jcp.2016.05.003 10.1002/nme.905 10.1016/j.cma.2016.02.001 10.1016/j.ijnonlinmec.2004.10.005 10.1007/s11340-008-9148-y |
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| Keywords | Data Driven Identification Digital image correlation Stress measurements Elastomer |
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| References | Sutton, Orteu, Schreier (bib0032) 2009 Ayensa-Jiménez, Doweidar, Sanz-Herrera, Doblaré (bib0002) 2018; 328 Kirchdoerfer, Ortiz (bib0014) 2016; 304 Leygue, Coret, Réthoré, Stainier, Verron (bib0018) 2018; 331 O’Leary, Doyle, McGloughlin (bib0027) 2013; 46 Avril, Bonnet, Bretelle, Grédiac, Hild, Ienny, Latourte, Lemosse, Pagano, Pagnacco, Pierron (bib0001) 2008; 48 Furukawa, Yagawa (bib0006) 1998; 43 Eggersmann, Kirchdoerfer, Reese, Stainier, Ortiz (bib0005) 2019; 350 MacQueen, others (bib0021) 1967 Réthoré, Elguedj, Coret, Chaudet, Combescure, others (bib0028) 2013; 50 Berry, Linoff (bib0003) 1997 Ibáñez Pinillo, Abisset-Chavanne, Aguado, González, Cueto, Chinesta (bib0011) 2018; 25 Kanno, Y., 2018. Mixed-integer programming formulation of a Data-Driven solver in computational elasticity, 2018, arXiv:1810.04394v1. Shen, Chandrashekhara, Breig, Oliver (bib0031) 2005; 40 Meng, Breitkopf, Raghavan, Mauvoisin, Bartier, Hernot (bib0023) 2015; 297 Sutton, Yan, Tiwari, Schreier, Orteu (bib0033) 2008; 46 Hild, Roux (bib0009) 2012; 52 Nguyen, Keip (bib0026) 2018; 194 Grédiac, Pierron, Avril, Toussaint (bib0007) 2006; 42 Kirchdoerfer, Ortiz (bib0015) 2017; 326 Leygue, Seghir, Réthoré, Coret, Verron, Stainier (bib0019) 2019 Hashash, Jung, Ghaboussi (bib0008) 2004; 59 Jones, Iadicola (bib0012) 2018 Ling, Jones, Templeton (bib0020) 2016; 318 Réthoré, Leygue, Coret, Stainier, Verron (bib0029) 2018; 113 Kirchdoerfer, Ortiz (bib0016) 2018; 113 Seghir, Pierron (bib0030) 2018; 58 Latorre, Montáns (bib0017) 2018 Millán, Arroyo (bib0025) 2013; 261–262 Marckmann, Verron (bib0022) 2006; 79 Dalémat, Coret, Leygue, Verron (bib0004) 2019 Meunier, Chagnon, Favier, Orgéas, Vacher (bib0024) 2008; 27 Holzapfel (bib0010) 2000 10.1016/j.mechmat.2019.103087_bib0013 Avril (10.1016/j.mechmat.2019.103087_bib0001) 2008; 48 Grédiac (10.1016/j.mechmat.2019.103087_bib0007) 2006; 42 Kirchdoerfer (10.1016/j.mechmat.2019.103087_bib0015) 2017; 326 Eggersmann (10.1016/j.mechmat.2019.103087_bib0005) 2019; 350 Shen (10.1016/j.mechmat.2019.103087_bib0031) 2005; 40 MacQueen (10.1016/j.mechmat.2019.103087_bib0021) 1967 Nguyen (10.1016/j.mechmat.2019.103087_bib0026) 2018; 194 Réthoré (10.1016/j.mechmat.2019.103087_bib0028) 2013; 50 Kirchdoerfer (10.1016/j.mechmat.2019.103087_bib0016) 2018; 113 Furukawa (10.1016/j.mechmat.2019.103087_bib0006) 1998; 43 Dalémat (10.1016/j.mechmat.2019.103087_bib0004) 2019 Meng (10.1016/j.mechmat.2019.103087_bib0023) 2015; 297 Réthoré (10.1016/j.mechmat.2019.103087_bib0029) 2018; 113 Sutton (10.1016/j.mechmat.2019.103087_bib0032) 2009 Leygue (10.1016/j.mechmat.2019.103087_bib0018) 2018; 331 Marckmann (10.1016/j.mechmat.2019.103087_bib0022) 2006; 79 Ibáñez Pinillo (10.1016/j.mechmat.2019.103087_bib0011) 2018; 25 Leygue (10.1016/j.mechmat.2019.103087_bib0019) 2019 Meunier (10.1016/j.mechmat.2019.103087_bib0024) 2008; 27 Sutton (10.1016/j.mechmat.2019.103087_bib0033) 2008; 46 O’Leary (10.1016/j.mechmat.2019.103087_bib0027) 2013; 46 Holzapfel (10.1016/j.mechmat.2019.103087_bib0010) 2000 Jones (10.1016/j.mechmat.2019.103087_bib0012) 2018 Kirchdoerfer (10.1016/j.mechmat.2019.103087_bib0014) 2016; 304 Hashash (10.1016/j.mechmat.2019.103087_bib0008) 2004; 59 Berry (10.1016/j.mechmat.2019.103087_bib0003) 1997 Seghir (10.1016/j.mechmat.2019.103087_bib0030) 2018; 58 Hild (10.1016/j.mechmat.2019.103087_bib0009) 2012; 52 Ayensa-Jiménez (10.1016/j.mechmat.2019.103087_bib0002) 2018; 328 Ling (10.1016/j.mechmat.2019.103087_bib0020) 2016; 318 Latorre (10.1016/j.mechmat.2019.103087_bib0017) 2018 Millán (10.1016/j.mechmat.2019.103087_bib0025) 2013; 261–262 |
| References_xml | – volume: 40 start-page: 875 year: 2005 end-page: 890 ident: bib0031 article-title: Finite element analysis of V-ribbed belts using neural network based hyperelastic material model publication-title: Int. J. Non-Linear Mech. – volume: 331 start-page: 184 year: 2018 end-page: 196 ident: bib0018 article-title: Data-based derivation of material response publication-title: Comput. Methods Appl. Mech. Eng. – volume: 79 start-page: 835 year: 2006 end-page: 858 ident: bib0022 article-title: Comparison of hyperelastic models for rubber-like materials publication-title: Rubber Chem. Technol. – reference: Kanno, Y., 2018. Mixed-integer programming formulation of a Data-Driven solver in computational elasticity, 2018, arXiv:1810.04394v1. – year: 2018 ident: bib0017 article-title: Experimental data reduction for hyperelasticity publication-title: Comput. Struct. – volume: 326 start-page: 622 year: 2017 end-page: 641 ident: bib0015 article-title: Data Driven computing with noisy material data sets publication-title: Comput. Methods Appl. Mech. Eng. – year: 2019 ident: bib0004 article-title: Reliability of the Data-Driven Identification algorithm with respect to incomplete input data publication-title: in Constitutive Models for Rubber XI, Huneau B., Le Cam J.-B., Marco Y., Verron E. editors, 2019, CRC Press, London, 311–316. ISBN: 978-0-367-34258-6 – volume: 59 start-page: 989 year: 2004 end-page: 1005 ident: bib0008 article-title: Numerical implementation of a neural network based material model in finite element analysis publication-title: Int. J. Numer. Methods Eng. – volume: 43 start-page: 195 year: 1998 end-page: 219 ident: bib0006 article-title: Implicit constitutive modelling for viscoplasticity using neural networks publication-title: Int. J. Numer. Methods Eng. – year: 1997 ident: bib0003 article-title: Data Mining Techniques: For Marketing, Sales, and Customer Support – volume: 328 start-page: 752 year: 2018 end-page: 774 ident: bib0002 article-title: A new reliability-based Data-Driven approach for noisy experimental data with physical constraints publication-title: Comput. Methods Appl. Mech. Eng. – start-page: 1 year: 2019 end-page: 9 ident: bib0019 article-title: Non-parametric material state field extraction from full field measurements publication-title: Comput. Mech. – volume: 27 start-page: 765 year: 2008 end-page: 777 ident: bib0024 article-title: Mechanical experimental characterisation and numerical modelling of an unfilled silicone rubber publication-title: Polym. Test. – volume: 52 start-page: 1503 year: 2012 end-page: 1519 ident: bib0009 article-title: Comparison of local and global approaches to digital image correlation publication-title: Exp. Mech. – volume: 297 start-page: 239 year: 2015 end-page: 257 ident: bib0023 article-title: Identification of material properties using indentation test and shape manifold learning approach publication-title: Comput. Methods Appl. Mech. Eng. – volume: 350 start-page: 81 year: 2019 end-page: 99 ident: bib0005 article-title: Model-free Data-Driven inelasticity publication-title: Comput. Methods Appl. Mech. Eng. – volume: 46 start-page: 746 year: 2008 end-page: 757 ident: bib0033 article-title: The effect of out-of-plane motion on 2d and 3d digital image correlation measurements publication-title: Opt. Lasers Eng. – volume: 48 start-page: 381 year: 2008 ident: bib0001 article-title: Overview of identification methods of mechanical parameters based on full-field measurements publication-title: Exp. Mech. – volume: 318 start-page: 22 year: 2016 end-page: 35 ident: bib0020 article-title: Machine learning strategies for systems with invariance properties publication-title: J. Comput. Phys. – volume: 261–262 start-page: 118 year: 2013 end-page: 131 ident: bib0025 article-title: Nonlinear manifold learning for model reduction in finite elastodynamics publication-title: Comput. Methods Appl. Mech. Eng. – volume: 50 start-page: 73 year: 2013 end-page: 85 ident: bib0028 article-title: Robust identification of elasto-plastic constitutive law parameters from digital images using 3d kinematics publication-title: Int. J. Solids Struct. – volume: 304 start-page: 81 year: 2016 end-page: 101 ident: bib0014 article-title: Data-driven computational mechanics publication-title: Comput. Methods Appl. Mech. Eng. – volume: 113 start-page: 1810 year: 2018 end-page: 1826 ident: bib0029 article-title: Computational measurements of stress fields from digital images publication-title: Int. J. Numer. Methods Eng. – year: 2000 ident: bib0010 article-title: Nonlinear Solid Mechanics – year: 2018 ident: bib0012 article-title: A Good Practices Guide for Digital Image Correlation publication-title: Technical Report – volume: 46 start-page: 1955 year: 2013 end-page: 1960 ident: bib0027 article-title: Comparison of methods used to measure the thickness of soft tissues and their influence on the evaluation of tensile stress publication-title: J. Biomech. – volume: 42 start-page: 233 year: 2006 end-page: 253 ident: bib0007 article-title: The virtual fields method for extracting constitutive parameters from full-field measurements: a review publication-title: Strain – year: 2009 ident: bib0032 article-title: Image Correlation for Shape, Motion and Deformation Measurements: Basic Concepts,Theory and Applications – volume: 194 start-page: 97 year: 2018 end-page: 115 ident: bib0026 article-title: A data-Driven approach to nonlinear elasticity publication-title: Comput. Struct. – volume: 25 start-page: 47 year: 2018 end-page: 57 ident: bib0011 article-title: A manifold learning approach to Data-Driven computational elasticity and inelasticity publication-title: Arch. Comput. Methods Eng. – start-page: 281 year: 1967 end-page: 297 ident: bib0021 article-title: Some methods for classification and analysis of multivariate observations publication-title: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability – volume: 113 start-page: 1697 year: 2018 end-page: 1710 ident: bib0016 article-title: Data-Driven computing in dynamics publication-title: Int. J. Numer. Methods Eng. – volume: 58 start-page: 183 year: 2018 end-page: 206 ident: bib0030 article-title: A novel image-based ultrasonic test to map material mechanical properties at high strain-rates publication-title: Exp. Mech. – year: 1997 ident: 10.1016/j.mechmat.2019.103087_bib0003 – volume: 297 start-page: 239 year: 2015 ident: 10.1016/j.mechmat.2019.103087_bib0023 article-title: Identification of material properties using indentation test and shape manifold learning approach publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2015.09.004 – year: 2019 ident: 10.1016/j.mechmat.2019.103087_bib0004 article-title: Reliability of the Data-Driven Identification algorithm with respect to incomplete input data – volume: 25 start-page: 47 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0011 article-title: A manifold learning approach to Data-Driven computational elasticity and inelasticity publication-title: Arch. Comput. Methods Eng. doi: 10.1007/s11831-016-9197-9 – volume: 261–262 start-page: 118 year: 2013 ident: 10.1016/j.mechmat.2019.103087_bib0025 article-title: Nonlinear manifold learning for model reduction in finite elastodynamics publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2013.04.007 – volume: 27 start-page: 765 issue: 6 year: 2008 ident: 10.1016/j.mechmat.2019.103087_bib0024 article-title: Mechanical experimental characterisation and numerical modelling of an unfilled silicone rubber publication-title: Polym. Test. doi: 10.1016/j.polymertesting.2008.05.011 – volume: 328 start-page: 752 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0002 article-title: A new reliability-based Data-Driven approach for noisy experimental data with physical constraints publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2017.08.027 – year: 2009 ident: 10.1016/j.mechmat.2019.103087_bib0032 – volume: 58 start-page: 183 issue: 2 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0030 article-title: A novel image-based ultrasonic test to map material mechanical properties at high strain-rates publication-title: Exp. Mech. doi: 10.1007/s11340-017-0329-4 – volume: 331 start-page: 184 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0018 article-title: Data-based derivation of material response publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2017.11.013 – volume: 46 start-page: 746 issue: 10 year: 2008 ident: 10.1016/j.mechmat.2019.103087_bib0033 article-title: The effect of out-of-plane motion on 2d and 3d digital image correlation measurements publication-title: Opt. Lasers Eng. doi: 10.1016/j.optlaseng.2008.05.005 – volume: 50 start-page: 73 issue: 1 year: 2013 ident: 10.1016/j.mechmat.2019.103087_bib0028 article-title: Robust identification of elasto-plastic constitutive law parameters from digital images using 3d kinematics publication-title: Int. J. Solids Struct. doi: 10.1016/j.ijsolstr.2012.09.002 – volume: 350 start-page: 81 year: 2019 ident: 10.1016/j.mechmat.2019.103087_bib0005 article-title: Model-free Data-Driven inelasticity publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2019.02.016 – volume: 46 start-page: 1955 issue: 11 year: 2013 ident: 10.1016/j.mechmat.2019.103087_bib0027 article-title: Comparison of methods used to measure the thickness of soft tissues and their influence on the evaluation of tensile stress publication-title: J. Biomech. doi: 10.1016/j.jbiomech.2013.05.003 – year: 2000 ident: 10.1016/j.mechmat.2019.103087_bib0010 – volume: 43 start-page: 195 issue: 2 year: 1998 ident: 10.1016/j.mechmat.2019.103087_bib0006 article-title: Implicit constitutive modelling for viscoplasticity using neural networks publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/(SICI)1097-0207(19980930)43:2<195::AID-NME418>3.0.CO;2-6 – volume: 326 start-page: 622 year: 2017 ident: 10.1016/j.mechmat.2019.103087_bib0015 article-title: Data Driven computing with noisy material data sets publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2017.07.039 – volume: 79 start-page: 835 issue: 5 year: 2006 ident: 10.1016/j.mechmat.2019.103087_bib0022 article-title: Comparison of hyperelastic models for rubber-like materials publication-title: Rubber Chem. Technol. doi: 10.5254/1.3547969 – volume: 113 start-page: 1810 issue: 12 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0029 article-title: Computational measurements of stress fields from digital images publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/nme.5721 – volume: 52 start-page: 1503 issue: 9 year: 2012 ident: 10.1016/j.mechmat.2019.103087_bib0009 article-title: Comparison of local and global approaches to digital image correlation publication-title: Exp. Mech. doi: 10.1007/s11340-012-9603-7 – year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0012 article-title: A Good Practices Guide for Digital Image Correlation – volume: 113 start-page: 1697 issue: 11 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0016 article-title: Data-Driven computing in dynamics publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/nme.5716 – volume: 194 start-page: 97 year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0026 article-title: A data-Driven approach to nonlinear elasticity publication-title: Comput. Struct. doi: 10.1016/j.compstruc.2017.07.031 – volume: 42 start-page: 233 issue: 4 year: 2006 ident: 10.1016/j.mechmat.2019.103087_bib0007 article-title: The virtual fields method for extracting constitutive parameters from full-field measurements: a review publication-title: Strain – volume: 318 start-page: 22 year: 2016 ident: 10.1016/j.mechmat.2019.103087_bib0020 article-title: Machine learning strategies for systems with invariance properties publication-title: J. Comput. Phys. doi: 10.1016/j.jcp.2016.05.003 – year: 2018 ident: 10.1016/j.mechmat.2019.103087_bib0017 article-title: Experimental data reduction for hyperelasticity publication-title: Comput. Struct. – ident: 10.1016/j.mechmat.2019.103087_bib0013 – volume: 59 start-page: 989 issue: 7 year: 2004 ident: 10.1016/j.mechmat.2019.103087_bib0008 article-title: Numerical implementation of a neural network based material model in finite element analysis publication-title: Int. J. Numer. Methods Eng. doi: 10.1002/nme.905 – volume: 304 start-page: 81 year: 2016 ident: 10.1016/j.mechmat.2019.103087_bib0014 article-title: Data-driven computational mechanics publication-title: Comput. Methods Appl. Mech. Eng. doi: 10.1016/j.cma.2016.02.001 – volume: 40 start-page: 875 issue: 6 year: 2005 ident: 10.1016/j.mechmat.2019.103087_bib0031 article-title: Finite element analysis of V-ribbed belts using neural network based hyperelastic material model publication-title: Int. J. Non-Linear Mech. doi: 10.1016/j.ijnonlinmec.2004.10.005 – start-page: 1 year: 2019 ident: 10.1016/j.mechmat.2019.103087_bib0019 article-title: Non-parametric material state field extraction from full field measurements publication-title: Comput. Mech. – start-page: 281 year: 1967 ident: 10.1016/j.mechmat.2019.103087_bib0021 article-title: Some methods for classification and analysis of multivariate observations – volume: 48 start-page: 381 issue: 4 year: 2008 ident: 10.1016/j.mechmat.2019.103087_bib0001 article-title: Overview of identification methods of mechanical parameters based on full-field measurements publication-title: Exp. Mech. doi: 10.1007/s11340-008-9148-y |
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| Snippet | •Data Driven Identification technique is applied to measured displacements and forces.•Experimental stress field is determined without presupposing... The present paper proposes a coupled experimental-numerical protocol to measure heterogeneous stress fields in a model-free framework. This work contributes to... |
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| SubjectTerms | Data Driven Identification Digital image correlation Elastomer Engineering Sciences Materials Stress measurements |
| Title | Measuring stress field without constitutive equation |
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