Open Access

ARTICLE

Deep Learning-Accelerated Extended Finite Element Method for Crack Propagation Analysis in Composite Structures

Shamsh Parveen¹, Mehtab Alam^2,*, Ashraf Ali³, Abdullah Alourani⁴

1 Department of Mechanical Engineering, Mahamaya Institute of Information Technology, Kushinagar, India
2 Department of Computer Science, Acharya Narendra Dev College, University of Delhi, Delhi, India
3 Faculty of Computer Studies, Arab Open University, A'ali, Bahrain
4 Department of Management Information Systems, College of Business and Economics, Qassim University, Buraydah, Saudi Arabia

* Corresponding Authors: Mehtab Alam. Email: ,

(This article belongs to the Special Issue: Advanced Security and Privacy for Future Mobile Internet and Convergence Applications: A Computer Modeling Approach)

Computer Modeling in Engineering & Sciences 2026, 148(2), 9 https://doi.org/10.32604/cmes.2026.083687

Received 23 April 2026; Accepted 25 June 2026; Issue published 28 August 2026

Abstract

Accurate prediction of complex failure modes in anisotropic composite structures—specifically matrix cracking, fiber rupture, and delamination (stratification)—remains a central challenge in computational fracture mechanics. The primary goal of this work is to bridge the gap between high-fidelity physical modeling and computational efficiency. While the extended finite element method (XFEM) enables mesh-independent crack modeling, its computational cost limits scalability. This work proposes a deep learning–accelerated extended finite element framework (DL-XFEM) that couples physically admissible XFEM fields with a neural network surrogate to predict incremental crack growth. XFEM is employed to generate stress-intensity factors and fracture-consistent state variables, which are used to train the network to replace the most computationally intensive crack-update steps while preserving fracture-mechanics constraints. The proposed framework achieves an 88.3% reduction in runtime relative to standard XFEM. Quantitatively, the method delivers an average trajectory prediction error of RMSE = 0.083 MPa√m and MAE = 0.071 mm, reducing prediction error by 61.2% compared to standard Physics-Informed Neural Network (PINN) baselines. Generalization is demonstrated across multiple stacking sequences and loading scenarios, and predictions are validated against experimental crack-growth data for aluminum, titanium, and carbon/epoxy laminate specimens. The results indicate that DL-XFEM provides a scalable and physically consistent approach for accelerating fracture simulations in composite structures without sacrificing predictive reliability.

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