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Volume 25
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

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

by
Zifan He
1,
Jiyun Lu
1,
Shengming Cui
2,
Chunhua Zhou
3,
Yinuo Shao
4,
Qi Wu
2 and
Hongfu Zuo
1,*
1
Civil Aviation Key Laboratory of Aircraft Health Monitoring and Intelligent Maintenance, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2
State Key Laboratory of Mechanics and Control for Aerospace Structures, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
3
Shanghai Institute of Satellite Engineering, Shanghai 201109, China
4
College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
*
Author to whom correspondence should be addressed.
Sensors 2025, 25(24), 7570; https://doi.org/10.3390/s25247570 (registering DOI)
Submission received: 18 November 2025 / Revised: 9 December 2025 / Accepted: 10 December 2025 / Published: 12 December 2025
(This article belongs to the Section Optical Sensors)
Review Reports Versions Notes

Abstract

Low-energy impacts have been demonstrated to cause damage and failure in aircraft structures, thereby affecting the structural load-bearing performance and creating safety hazards. In this study, an innovative damage-monitoring method based on a fiber Bragg grating (FBG) is proposed for honeycomb sandwich composites. The proposed method is applicable to honeycomb sandwich composites and integrates a light gradient boosting machine (LightGBM)-optimized impact localization method with feature-parallel and data-parallel processing in the machine learning architecture. An impact localization algorithm is applied to honeycomb sandwich composites using an array of multiplexed FBG sensors. The proposed algorithm exhibited substantial localization accuracy. The LightGBM method was employed to identify the optimal branching points for impact localization in real time, addressing the low-accuracy challenge in localizing low-energy impacts on the board structure when the fiber grating sensing system operates at a high sampling frequency.
Keywords: honeycomb sandwich composites; low-energy impact localization; fiber Bragg grating sensor; layout optimization; machine learning honeycomb sandwich composites; low-energy impact localization; fiber Bragg grating sensor; layout optimization; machine learning

Share and Cite

MDPI and ACS Style

He, Z.; Lu, J.; Cui, S.; Zhou, C.; Shao, Y.; Wu, Q.; Zuo, H. Enhanced Low-Energy Impact Localization for Carbon-Fiber Honeycomb Sandwich Panels Using LightGBM. Sensors 2025, 25, 7570. https://doi.org/10.3390/s25247570

AMA Style

He Z, Lu J, Cui S, Zhou C, Shao Y, Wu Q, Zuo H. Enhanced Low-Energy Impact Localization for Carbon-Fiber Honeycomb Sandwich Panels Using LightGBM. Sensors. 2025; 25(24):7570. https://doi.org/10.3390/s25247570

Chicago/Turabian Style

He, Zifan, Jiyun Lu, Shengming Cui, Chunhua Zhou, Yinuo Shao, Qi Wu, and Hongfu Zuo. 2025. "Enhanced Low-Energy Impact Localization for Carbon-Fiber Honeycomb Sandwich Panels Using LightGBM" Sensors 25, no. 24: 7570. https://doi.org/10.3390/s25247570

APA Style

He, Z., Lu, J., Cui, S., Zhou, C., Shao, Y., Wu, Q., & Zuo, H. (2025). Enhanced Low-Energy Impact Localization for Carbon-Fiber Honeycomb Sandwich Panels Using LightGBM. Sensors, 25(24), 7570. https://doi.org/10.3390/s25247570

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