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S. Amutha,
R.K. Jeyauthmigha,
S. Tephillah,
N. Kanagavalli,
V.S. Prakash,
P.R. Therasa,
S. Gayathri,
- Professor, Department of Computer Science and Engineering, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, India
- Assistant Professor, Department of Computer Science and Design, SNS College of Technology, Saravanampatti, Coimbatore, Tamil Nadu, India
- Associate Professor, Department of Electronics and Communication Engineering, St. Joseph’s Institute of Technology, Chennai, Tamil Nadu, India
- Associate Professor, Department of Computer Science and Engineering, Vel Tech High Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Avadi, Chennai, Tamil Nadu, India
- Professor, Department of Computer Applications, Kristu Jayanti (Deemed to be University) Bengaluru, Karnataka, India
- Associate Professor, Department of Computer Science and Engineering, R.M.K. Engineering College, RSM Nagar, Kavaraipettai, Thiruvallur, Tamil Nadu, India
- Assistant Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
Abstract
Fiber Reinforced Polymer (FRP) composites have broad spread use in aerospace, automotive, marine and structural applications due to its high specific strength, stiffness and corrosion resistance. The various damage mechanisms such as matrix cracking, fiber breakage, delamination and interfacial failure, however, make the forecasting of damage particularly complex. In this work, a hybrid machine learning (ML) and finite element (FE) system is proposed for predicting the damage behavior of FRP composites. Progressive damage is simulated under different loading conditions using a complete FE model with suitable material constitutive and failure criteria. The numerical simulations are used to recover the primary aspects such as the fiber orientation, volume %, mechanical characteristics, loading circumstances and damage variables to provide a whole data set. The machine learning algorithms are then trained to detect the nonlinear relationships between the input parameters and the damage responses. The prediction capacity of ML models is evaluated using statistical measures such as coefficient of determination, root mean square error and mean absolute error. The proposed hybrid method aims at using the physical interpretability of FE simulations and the computational efficiency of ML model to quickly anticipate damage initiation and evolution. The proposed methodology is expected to be a useful tool for composite design, structural health assessment and failure analysis with minimal computation costs and reliable predictive accuracy. The proposed approach can also enable optimization and real time prediction of damage for sophisticated composite constructions.
Keywords: Finite element analysis, Damage prediction, Progressive damage, Delamination Failure analysis, Composite structures.
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Journal of Polymer & Composites
| Volume | 14 | |
| 04 | ||
| Received | 09/09/2026 | |
| Accepted | 19/09/2026 | |
| Published | 23/09/2026 | |
| Publication Time | 14 Days |