A hybrid electromechanical phase-field and deep learning framework for predicting fracture in dielectric nanocomposites

TL;DR

This paper introduces a hybrid framework combining phase-field fracture modeling with deep learning to accurately and efficiently predict crack propagation in dielectric nanocomposites, reducing computational costs.

Contribution

It develops a novel integrated approach that leverages high-fidelity simulations and CNNs to improve fracture prediction accuracy and efficiency in dielectric materials.

Findings

Electric potential data improves crack segmentation accuracy.

The framework reduces computational costs significantly.

Electric potential fields enable better generalization.

Abstract

The accurate and efficient prediction of crack propagation in dielectric materials is a critical challenge in structural health monitoring and the design of smart systems. This work presents a hybrid modeling framework that combines an electromechanical phase-field fracture model with deep learning-based surrogate modeling to predict fracture evolution in dielectric nanocomposite plates. The underlying finite element simulations capture the coupling between mechanical deformation and electrical field perturbations caused by cracks, using a variational phase-field formulation. High-fidelity simulation outputs - namely, phase-field damage variables and electric potential fields -- are used to train convolutional neural networks (CNNs) with ResNet-U-Net architectures for pixel-wise segmentation of crack paths. The study systematically compares the performance of CNNs trained on phase-field versus electric potential data across multiple ResNet backbones. The results reveal that electric potential fields, although they encode damage indirectly, offer superior segmentation accuracy, faster convergence, and enhanced generalization, owing to their smoother gradient distribution and global spatial coverage. The proposed framework significantly reduces computational costs while preserving high accuracy, offers potential when appropriately adapted for sensor-based input data.