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Artificial neural network modeling to predict corrective stress of a two-layer composite plate under fully reversed
Ali Alkhafaji1, Mohammed I Khalaf2, Teeba Ismail Kh3
1Advanced Technical College, University of Warith Al-Anbiyaa, Karbala, Iraq.
Scientific Reports
|July 7, 2026
Summary
This study used an Artificial Neural Network (ANN) to predict composite plate stress. A 90° fiber orientation minimized stress, enhancing tensile strength, while identical angles increased stress concentration.
Area of Science:
- Materials Science
- Mechanical Engineering
- Computational Science
Background:
- Composite materials are crucial in engineering applications.
- Predicting stress in composite plates under cyclic loading is complex.
- Fiber orientation significantly influences composite material behavior.
Purpose of the Study:
- To develop an Artificial Neural Network (ANN) model for predicting maximum stress in two-layer composite plates.
- To analyze the effect of varying fiber orientations on stress distribution.
- To validate the ANN model's predictive accuracy against Finite Element Method (FEM) simulations.
Main Methods:
- A single-hidden-layer feedforward ANN was implemented.
- Finite Element Method (FEM) analysis was performed on Epoxy Carbon Woven plates.
- Various fiber orientations (0° to 90°) were simulated under cyclic loading using the Morrow correction method.
Main Results:
- A 90° fiber orientation in both layers minimized stress and enhanced tensile strength.
- Identical fiber angles in both layers led to increased stress magnitude and concentration.
- The ANN model achieved high predictive accuracy, with correlation coefficients near 1.0 for all datasets.
Conclusions:
- Fiber orientation is a critical parameter for optimizing composite plate performance.
- The developed ANN provides an accurate and efficient tool for predicting composite plate stress.
- Understanding stress distribution is key to improving the lifespan and reliability of composite structures.
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