Enhanced Low-Energy Impact Localization for Carbon-Fiber Honeycomb Sandwich Panels Using LightGBM

Area of Science:

• Materials Science
• Aerospace Engineering
• Structural Health Monitoring

Background:

• Low-energy impacts pose significant risks to aircraft structures, compromising load-bearing capacity and safety.
• Honeycomb sandwich composites are widely used in aerospace but are susceptible to impact damage.
• Effective damage detection and localization are crucial for maintaining structural integrity.

Purpose of the Study:

• To develop an innovative damage-monitoring method for honeycomb sandwich composites using fiber Bragg grating (FBG) sensors.
• To enhance the accuracy and real-time capability of impact localization, especially for low-energy events.
• To integrate a machine learning approach for optimizing the impact localization algorithm.

Main Methods:

• An impact localization algorithm was developed utilizing an array of multiplexed FBG sensors.
• A LightGBM (Light Gradient Boosting Machine) model was employed to optimize impact localization.
• Feature-parallel and data-parallel processing were integrated into the machine learning architecture for real-time analysis.

Main Results:

• The proposed FBG-based method demonstrated substantial localization accuracy for impacts on honeycomb sandwich composites.
• The LightGBM optimization successfully identified optimal branching points for real-time impact localization.
• The system effectively addressed the challenge of accurately localizing low-energy impacts at high sampling frequencies.

Conclusions:

• The developed FBG sensor system with LightGBM optimization offers a promising solution for real-time damage monitoring in aircraft composite structures.
• This method significantly improves the accuracy of impact localization, particularly for critical low-energy events.
• The findings contribute to enhanced structural health monitoring and improved safety in aerospace applications.