Hydrogels are a class of bio-inspired elastomeric material with the ability of high stretch-ability, toughness, and water absorption. Due to their bio-compatibility, high water content, unique mechanical behavior, softness, and flexibility, though hydrogels are utilized in diverse biological and pharmaceutical such as drug delivery, bone regeneration, spinal cord surgery, wound dressing, wearable epidermal sensors and cartilage. Double-network hydrogels exhibit nonlinear features such as stress softening, permanent set, cyclic energy dissipation and induced anisotropy in their mechanical responses to external loading. Besides, the hydrogels that are used in biomedical devices are designed to sustain continuous cyclic loading during their service life. While most studies assume that the majority of stress softening occurs in the first cycle, they exclude the damage in the next cycles as being zero. However, experimental studies suggest that the material exhibits a progressive damage accumulation and the stiffness of the material decreases significantly at a high number of cyclic loading. Accordingly, the material sustains a significant amount of damage and will not return to its primary loading curve after it reaches the maximum stretch experienced. This progressive stress softening can lead to an unpredicted response of the material in dynamic behavior as it is a function of material stiffness. The behavior of the material will eventually approach a steady-state after thousands of cycles, which suggests that the design protocols should consider both transient and steady-state response of the gels. However, there are only a few constitutive models developed to address this accumulative damage in the gels. Thus, in this study, we developed a data-driven method to predict the behavior of hydrogels subjected to a high number of cycles.   The developed model can be particularly used to estimate material response in each cycle and its steady-state response due to three-dimensional deformation. To this end, we assumed that material is anisotropic material in the virgin state. So, its total energy can be calculated by summation of the response of the material in each direction with respect to directional deformation applied in that direction. The strain energy of the material is the same in the undeformed state and damage evolve in each direction based on its deformation. Moreover, the energy of the material in each direction estimated through an artificial neural network, which is trained against the experimental data. The proposed model shows good agreement with cyclic uni-axial tensile test data in different cycles of the loading and steady-state response of material.


Data-Driven Constitutive Modeling of the Progressive Softening in Elastomeric Gels With Interpenetrating Networks

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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