Computational and experimental investigation of microcapsule-based self-healing polymers: Macro-mechanical finite element modeling and artificial neural network predictions
Reza Barbaz‐Isfahani, Hooman Dadras, Amir Teimouri, Saeed Saber‐Samandari, Manouchehr Salehi
Abstract
• Healing in polymers with multi-core microcapsules is evaluated by micro-mechanical FEM. • ANN is utilized to assess volume fraction, microcapsule properties, and healing. • SEM was employed to examine fracture surfaces in microcapsule self-healing polymers. • Despite higher cost, macro-mechanical FEM achieved superior accuracy in predictions. This study presents a comprehensive computational and experimental analysis of self-healing polymers containing multi-core microcapsules. A macro-mechanical finite element model (FEM) was developed to evaluate healing efficiency, incorporating homogenized and impacted regions simulated in Abaqus-Explicit with VUSDFLD subroutines to capture stress–strain responses. An artificial neural network (ANN) was employed to predict the effects of microcapsule volume fraction (VF) (5–10 %) and nanoparticle reinforcement (multi-walled carbon nanotubes (MWCNT), nanoclay, nanosilica) on tensile strength and healing performance. The FEM demonstrated high accuracy (<5% error) compared to experimental data, outperforming multi-scale models despite higher computational costs. The ANN revealed that increasing microcapsule content enhances healing efficiency, while nanoparticle reinforcement reduces it due to restricted healing agent release from mechanically stronger capsules. SEM analysis identified three healing mechanisms: localized healing near ruptured microcapsules, crack-path filling, and wide-crack bridging. Validation against experimental and multi-scale data confirmed the model’s reliability for optimizing self-healing material design. Key findings indicate that 10 vol% microcapsules maximize healing efficiency, though nanoparticle reinforcement trades mechanical strength for reduced self-repair capability. This work advances computational tools for autonomous healing materials, providing a framework for balancing mechanical performance and self-healing functionality in structural applications.