Identification of NOL-Ring Composite Materials' Damage Mechanism Based on the STOA-VMD Algorithm

Area of Science:

• Materials Science
• Mechanical Engineering
• Signal Processing

Background:

• Acoustic emission (AE) signals from damaged fiber-reinforced composites often suffer from aliasing, randomness, and poor robustness.
• Accurate identification of damage mechanisms is crucial for structural health monitoring of composite materials.

Purpose of the Study:

• To develop and validate an optimized signal processing technique for AE data from composite materials.
• To enhance the accuracy and robustness of damage detection and classification in fiber-reinforced composites.

Main Methods:

• Utilized the Sooty Tern Optimization Algorithm-Variational Mode Decomposition (STOA-VMD) for optimizing Variational Mode Decomposition (VMD) parameters (K and α).
• Reconstructed and analyzed acoustic emission signals from tensile tests on glass fiber/epoxy NOL-ring specimens.
• Developed a damage signal feature extraction and recognition algorithm based on a sample dataset.

Main Results:

• The STOA-VMD method demonstrated improved adaptive decomposition accuracy for AE signals.
• The damage recognition algorithm achieved high accuracy rates: 94.59% for matrix cracking, 94.26% for fiber fracture, and 96.45% for delamination.
• The study successfully characterized the damage process in NOL-ring specimens.

Conclusions:

• The STOA-VMD approach offers a highly efficient method for feature extraction and recognition of damage signals in polymer composites.
• This technique significantly improves the reliability of damage assessment in fiber-reinforced composite structures.
• The findings provide a robust framework for advanced structural health monitoring applications.