An in situ prepared fiber-based piezoresistive film for ultrasonic wave detection on nonplanar carbon fiber-reinforced composites Xiaoying Cheng, Mingming Zheng, Zhenyu Wu, Lin Shi, Xudong Hu Structural Health Monitoring, 2026 Owing to the flexibility and high-frequency responsiveness, nanocomposite piezoresistive sensors are suitable for Lamb wave detection on composite materials. Various types of nanocomposite films have been developed for this purpose. However, the inelasticity trait of commonly used electrodes for the nanocomposite films impedes their application on the nonplanar surface of composites. Herein, an in situ prepared piezoresistive film based on graphene and polymethyl methacrylate nanocomposite has been proposed for ultrasonic wave detection on nonplanar carbon fiber-reinforced composites. Electrospinning method is employed to fabricate piezoresistive fiber film onto carbon fiber-reinforced polymer (CFRP) composites, and the sensitivity along the thickness direction is utilized via employing the CFRP layer as one electrode. The fabricated sensitive films exhibit the capability to detect up to 150 kHz ultrasonic signals on curved surfaces. With the help of Hilbert energy spectrum, the precise damage localization on CFRP is achieved by the sensitive film array with a relative error of only 1%. This innovation provides a new in situ preparation method for ultrasonic sensitive film on nonplanar-shaped CFRP components for nondestructive evaluation task.
Decision fusion for damage localization in CFRP laminate using Lamb wave and acoustic emission Xiaoying Cheng, Tengkai Wang, Liang Jin, Zhenyu Wu, Kehong Zheng, Hongjun Li, Duncan Camilleri, Xudong Hu Mechanical Systems and Signal Processing, 2026 Carbon fiber reinforced polymer (CFRP) structures are particularly vulnerable to barely visible impact damage during service, requiring advanced methods for assessment. This study presents a novel damage localization approach that synergistically combines Lamb wave (LW) and acoustic emission (AE) techniques through decision-level fusion. Experiment using low-velocity impacts generated complementary active LW and passive AE datasets, which were processed using Hilbert transform and envelope extraction for feature extraction. A deep learning regression model was developed to predict damage coordinates, augmented by an optimized weighted-average fusion strategy. However, due to signal distortion and sparse sensor coverage, single-modality methods suffer from degraded performance in certain regions, particularly near structural edges. Feature-level fusion struggles to reconcile the nonlinear differences between LW and AE signals, often leading to information redundancy or loss. To overcome these limitations, this work proposes a novel decision-level weighted fusion framework that leverages the complementary strengths of LW and AE while preserving their individual signal characteristics. Comparative analysis shows that the localization errors in the x and y coordinates are reduced by 41.49 % and 38.36 %, respectively, compared to single-modality methods. In the edge regions, the errors in the x and y coordinates are reduced by 18.04 % and 14.44 %, respectively. The methodology demonstrates significant potential in practical implementation in CFRP structural health monitoring systems, offering enhanced reliability for impact damage assessment.
In-situ sensing of carbon fibers in fiber reinforced polymer composite tubes for bending detection Iccm International Conferences on Composite Materials, 2017
Kinematic modeling for realistic internal architectural of 3D woven CFRP reinforcement unit cell Iccm International Conferences on Composite Materials, 2017
The mechanical response of braided composite tube under three points bending loading Iccm International Conferences on Composite Materials, 2017
