Impact Localization System of CFRP Structure Based on EFPI Sensors

Area of Science:

• Materials Science
• Structural Health Monitoring
• Composite Materials Engineering

Background:

• Carbon fiber reinforced polymer (CFRP) structures are susceptible to impact damage, often undetectable visually, posing safety risks.
• Microscopic impact-induced damage can compromise the structural integrity of CFRP components.
• Reliable methods for detecting and localizing impacts are crucial for CFRP safety and maintenance.

Purpose of the Study:

• To develop and validate an advanced impact localization system for CFRP structures.
• To utilize extrinsic Fabry-Perot interferometric (EFPI) sensors for high-speed impact signal detection.
• To propose a novel localization algorithm combining deep learning and attention mechanisms for improved accuracy.

Main Methods:

• Implementation of EFPI sensors for capturing impact-induced strain waves.
• High-speed signal demodulation using an intensity modulation technique.
• Development of an impact localization model integrating energy-entropy ratio endpoint detection with a CNN-BIGRU-Attention network.
• Optimization of model parameters using the sand cat swarm optimization algorithm.

Main Results:

• The proposed CNN-BIGRU-Attention model effectively utilizes the time difference of arrival (TDOA) from EFPI sensors.
• An attention mechanism was integrated to prioritize TDOA data from nearby sensors.
• Experimental validation on a 400 mm × 400 mm CFRP plate yielded an average impact localization error of 8.14 mm.
• The system demonstrated effective and satisfactory performance in localizing impacts.

Conclusions:

• The developed EFPI-based impact localization system offers a promising solution for monitoring CFRP structures.
• The combination of EFPI sensing and the CNN-BIGRU-Attention model provides high accuracy in impact detection and localization.
• This technology enhances the safety and reliability of CFRP components by identifying hidden damage.