Description
- Abstract:
- Soft polymers constitute a broad class of materials with applications spanning biomedical systems, soft robotics, and impact mitigation. The mechanical properties of these polymers vary significantly depending on the crosslinking mechanisms and loading timescales. Despite their importance, deformation, damage, and fracture models that predict their strongly rate-dependent non-linear response are lacking. To address this gap, we experimentally characterize and develop a microstructure-informed constitutive framework and a model for the large deformation response of a rate-dependent polymer, polyborosiloxane (PBS), over a strain rate range of 0.001 - 10000 1/s. PBS's impact mitigating and dynamic crosslink governed autonomous self-healing behavior makes it an ideal system. We further develop a unified framework to predict damage initiation, growth, and failure using a multi-mechanism generalized gradient-damage formulation. A key contribution is the introduction of the energetic component of critical stress work as a physically motivated, loading rate and geometry independent damage initiation criterion. The models are implemented in the finite element software ABAQUS to simulate complex three-dimensional deformation and fracture. Validation is performed against experiments across multiple soft polymers and loading conditions, capturing rate-dependence, non-linear deformation, and diverse fracture modes. To address computational challenges in fracture modeling, we investigate physics-informed neural networks (PINNs) as a mesh-free alternative. Existing PINN-based approaches are restricted to small-strain regimes and rely on gradient-damage formulations. We propose a PINN framework for large deformation fracture in elastomers that eliminates the need for gradient-based damage regularization. Fracture evolution in elastomer-like materials is shown to be primarily governed by damage initiation criteria. The proposed PINN framework should apply to a broad class of materials. We extend the research to develop a PINN for large deformation contact modeling in elastomers. The mechanical and electrical response of PBS-based double network polymers and carbon nanotube-filled composites is also presented to guide the design of self-healing tunable materials. Finally, the unified framework is extended to model the rate-dependent failure of additively manufactured architected polymeric structures.
- Notes:
- Thesis (Ph. D.)--Brown University, 2026
Citation
Konale, Aditya Gurusidhappa,
"Deformation and Fracture of Soft Polymers: Experiments, Modeling and Computations"
(2026).
Mechanics of Solids Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://repository.library.brown.edu/studio/item/bdr:n9anu8hu/
Relations
Collection:
-
Mechanics of Solids Theses and Dissertations
Theses and Dissertations for the Mechanics of Solids department....