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F.J. Montans, V.J. Amores, I. Ben-Yelun, L. Moreno, J.M. Benítez, "Data-driven macro-micro-macro modelling of rubber-like materials", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 2.2, 2022, doi:10.4203/ccc.3.2.2

Keywords: hyperelasticity, B-splines, machine learning, polymers.

Abstract

The characterization of the multiaxial mechanical behaviour of polymers has been challenging, as the vast number of proposed models demonstrates. These models are based on analytical expressions of sate variables (invariants or principal stretches). Machine learning brings new tools to characterize polymers from macroscopic experiments. However, classical machine learning modelling as classical Neural Networks have several drawbacks, as the need for extensive data, the lack of robustness, and the lack of compliance with physical principles. In polymers, physics-based machine learning brings the best of both worlds by performing data-driven characterization considering physical principles and reducing the number of needed tests. In this work, using a simple procedure for crossing scales, we present a new data-driven procedure to characterize the entropic behaviour of a representative macromolecule directly from any single macroscopic test on the polymer by solving a linear system of equations. This single test may be homogeneous like a tensile test or a biaxial test, or it can also be a nonhomogeneous test where the deformation map is measured through digital image correlation and the cell load is recorded. The resulting macromolecule behaviour fully characterizes the reversible behaviour of the polymer and can be used in an efficient manner in finite elements to perform accurate simulations of polymers.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 3 PROCEEDINGS OF THE FOURTEENTH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STRUCTURES TECHNOLOGY Edited by: B.H.V. Topping and J. Kruis Paper 2.2 Data-driven macro-micro-macro modelling of rubber-like materials F.J. Montans^1,2, V.J. Amores¹, I. Ben-Yelun¹, L. Moreno¹ and J.M. Benítez¹ ¹E.T.S. de Ingeniería Aeronáutica y del Espacio, Universidad Politécnica de Madrid, Spain ²Department of Mechanical and Aerospace Engineering, University of Florida, USA doi:10.4203/ccc.3.2.2 Full Bibliographic Reference for this paper F.J. Montans, V.J. Amores, I. Ben-Yelun, L. Moreno, J.M. Benítez, "Data-driven macro-micro-macro modelling of rubber-like materials", in B.H.V. Topping, J. Kruis, (Editors), "Proceedings of the Fourteenth International Conference on Computational Structures Technology", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 3, Paper 2.2, 2022, doi:10.4203/ccc.3.2.2 Keywords: hyperelasticity, B-splines, machine learning, polymers. Abstract The characterization of the multiaxial mechanical behaviour of polymers has been challenging, as the vast number of proposed models demonstrates. These models are based on analytical expressions of sate variables (invariants or principal stretches). Machine learning brings new tools to characterize polymers from macroscopic experiments. However, classical machine learning modelling as classical Neural Networks have several drawbacks, as the need for extensive data, the lack of robustness, and the lack of compliance with physical principles. In polymers, physics-based machine learning brings the best of both worlds by performing data-driven characterization considering physical principles and reducing the number of needed tests. In this work, using a simple procedure for crossing scales, we present a new data-driven procedure to characterize the entropic behaviour of a representative macromolecule directly from any single macroscopic test on the polymer by solving a linear system of equations. This single test may be homogeneous like a tensile test or a biaxial test, or it can also be a nonhomogeneous test where the deformation map is measured through digital image correlation and the cell load is recorded. The resulting macromolecule behaviour fully characterizes the reversible behaviour of the polymer and can be used in an efficient manner in finite elements to perform accurate simulations of polymers. download the full-text of this paper (PDF, 6 pages, 526 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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