The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials that undergo large deformations. In this paper, we propose a physics-informed deep-learning framework for the discovery of hyperelastic constitutive model parameterizations given full-field surface displacement data and global force-displacement data. Contrary to the majority of recent literature in this field, we work with the weak form of the governing equations rather than the strong form to impose physical constraints upon the neural network predictions. The approach presented in this paper is computationally efficient, suitable for irregular geometric domains, and readily ingests displacement data without the need for interpolation onto a computational grid. A selection of canonical hyperelastic material models suitable for different material classes is considered including the Neo–Hookean, Gent, and Blatz–Ko constitutive models as exemplars for general non-linear elastic behaviour, elastomer behaviour with finite strain lock-up, and compressible foam behaviour, respectively. We demonstrate that physics informed machine learning is an enabling technology and may shift the paradigm of how full-field experimental data are utilized to calibrate constitutive models under finite deformations.
We have characterized the three-dimensional evolution of microstructural anisotropy of a family of elastomeric foams during uniaxial compression via in-situ X-ray computed tomography. Flexible polyurethane foam specimens with densities of 136, 160 and 240 kg/m3 were compressed in uniaxial stress tests both parallel and perpendicular to the foam rise direction, to engineering strains exceeding 70%. The uncompressed microstructures show slightly elongated ellipsoidal pores, with elongation aligned parallel to the foam rise direction. The evolution of this microstructural anisotropy during deformation is quantified based on the autocorrelation of the image intensity, and verified via the mean intercept length as well as the shape of individual pores. Trends are consistent across all three methods. In the rise direction, the material remains transversely anisotropic throughout compression. Anisotropy initially decreases with compression, reaches a minimum, then increases up to large strains, followed by a small decrease in anisotropy at the largest strains as pores collapse. Compression perpendicular to the foam rise direction induces secondary anisotropy with respect to the compression axis, in addition to primary anisotropy associated with the foam rise direction. In contrast to compression in the rise direction, primary anisotropy initially increases with compression, and shows a slight decrease at large strains. These surprising non-monotonic trends and qualitative differences in rise and transverse loading are explained based on the compression of initially ellipsoidal pores. Microstructural anisotropy trends reflect macroscopic stress-strain and lateral strain response. These findings provide novel quantitative connections between three-dimensional microstructure and anisotropy in moderate density polymer foams up to large deformation, with important implications for understanding complex three-dimensional states of deformation.
The choice of model form used to represent the anisotropic yield response of metals can depend strongly on the type and amount of data available for calibration. This two-part contribution considers the calibration (part I) of three yield functions: von Mises, Hill-48 and Yld2004-18p by Barlat and co-workers. This is followed by model verification exercises (part II). The material used was a 7079 aluminum alloy extruded tube. The calibration data were measurements of yield stress and Lankford ratio from uniaxial tension specimens cut along 12 orientations. Given that the tube was relatively thick-walled, some of the orientations included through-thickness components. This allowed the calibrations to be based exclusively on test data, without the need for parameter assumptions or supplemental crystal plasticity calculations. The Yld2004-18p function provided the best fit to the data available due to its 18 anisotropy parameters plus an unspecified exponent, compared to the quadratic Hill function with 6 anisotropy parameters and to the isotropic von Mises function. Whereas the Yld2004-18p function did not warrant further exploration due to the excellent fit it provided, the results showed that care must be taken when using Hill’s function. Finally, due to its parametrization with only 6 anisotropy parameters, it can significantly misrepresent the yield behavior depending on the calibration data used, possibly rendering it less desirable than a simple isotropic function in some applications.
