Search

Carbon fiber reinforced plastic (CFRP) structures are vulnerable to low-speed impacts, which will lead to almost invisible impact damage. Therefore, the timely localization of impact is of great significance to damage detection and maintenance of the structure. In this article, a low velocity impact supervisory and testing system based on fiber Bragg grating (FBG) sensors was built up for CFRP laminates to obtain the low velocity impact strain sensitivity model. Meanwhile, genetic algorithm was applied to optimize the configuration of the FBG sensing network. The eigenvectors of the impact signals were extracted by applying fast Fourier transform (FFT) transform and principal component analysis (PCA) technology used as the input of the back propagation (BP) neural network model, while the corresponding impact coordinates were used as the output, to train the model. After training, the impact position prediction model based on BP neural network was obtained, thereby achieving the impact localization for CFRP laminates successfully with an average localization error of 2.1 cm. [ABSTRACT FROM AUTHOR]

Carbon Fiber Reinforced Plastic (CFRP) shaft tube structure is widely applied in different fields, including aerospace, automotive, and wind power. Since CFRP shaft tube is often subjected to bending fatigue loads, it is of great significance to research its bending fatigue characteristics. Because of its unique advantages, such as a smaller size, lighter weight, and the outstanding ability to form a sensor network, the Fiber Bragg Grating (FBG) sensor is very applicable for health monitoring research of composite material structures. Taking the CFRP shaft tube under bending load as the research object, based on the theory of composite material mechanics and applying the research idea of combining simulation analysis and experiment, the fatigue life, residual stiffness, and fatigue damage evolution of CFRP tubes under three-point bending fatigue loading were studied. Moreover, the fatigue characteristics of CFRP tubes under different fatigue loading were analyzed. At the same time, the ultrasonic phased array was used to obtain the fatigue damage evolution rule by scanning and analyzing the damage to the CFRP shaft tube after different fatigue loading times. Through the application of the FBG sensors, the whole process of fatigue evolution of the CFRP shaft tube was fully monitored. [ABSTRACT FROM AUTHOR]

• In view of the lack of online damage detection of CFRP driveshaft, this paper aims to establish a damage detection system for CFRP driveshaft and establishes a damage identification model for CFRP driveshaft based on strain and damage mechanism. • Considering the shortcomings of the existing research, this paper makes full use of the unique advantages of FBG sensors, combined with GWO-BP neural network, constructs a CFRP driveshaft damage identification system, and realizes the damage identification and localization of the CFRP driveshaft. • Combining theoretical methods, simulation calculations and experimental studies, torsion experiments were carried out on CFRP driveshafts, and damage identification was carried out by using the identification and localization system constructed in this paper to compare the actual damage and damage identification. Carbon Fiber Reinforced Plastic (CFRP) has the advantages of light weight, high strength, small coefficient of thermal expansion, good vibration damping performance, etc., the use of its production drive shaft can reduce weight, improve the intrinsic frequency, transmission efficiency and precision, and has been used in helicopters, heavy duty machine tools, wind turbines, ships and other high-end equipment. Once the damage occurs, it will cause adverse effects such as reduction of mechanical efficiency of the system, degradation of transmission system performance, and decrease of reliability and safety. The existing non-destructive testing technology for carbon fiber reinforced plastic has limitations such as inconvenient use, complex equipment, and no real-time online monitoring. To address this problem, this paper adopts Fiber Bragg Gratings (FBG) sensors to obtain real-time strain field data of CFRP drive shaft, and uses the linear and nonlinear mapping ability of Back Propagation Neural Network (BP neural network) to identify and locate the damage of CFRP drive shaft. At the same time, the GWO optimization algorithm is used to optimize the BP neural network to construct a CFRP drive shaft damage identification and localization system based on FBG sensing network and GWO-BP neural network. The experiments show that the CFRP drive shaft damage identification and localization system constructed in this paper can obtain the CFRP drive shaft strain field information in real time and accurately identify the damage and its location. [ABSTRACT FROM AUTHOR]

Compared with metal materials, Carbon Fiber Reinforced Plastic (CFRP) is more excellent in performance, with high specific strength and high specific modulus. And among various CFRP structural elements, CFRP laminated plates have widespread applications. However, some indiscernible and hidden primary damage always appears on the CFRP laminated plate and will continuously evolve and expand after being loaded. This work starts with the analysis of the relationship between the changes of the strain distribution and damage expansion of CFRP laminated plates and proposes the damage recognition method of CFRP laminated plates based on strain information. Then, the CFRP laminate damage monitoring system based on Fiber Bragg Grating (FBG) sensor is established, and the CFRP laminate damage identification experiment based on FBG sensor is made to verify the accuracy of the CFRP laminate damage identification method. The results show that the maximum error between the load when the damage appears on the FBG sensor and that when the damage of CFRP laminated plates expands based on the simulated analysis does not exceed 16%. The accuracy of the damage recognition method of CFRP laminated plates is verified and the damage recognition of CFRP laminated plates and their expansion process is achieved. [ABSTRACT FROM AUTHOR]

• The localization of low velocity impact on CFRP laminate was conducted by using a normalized error outlier-based algorithm. • An experimental system for low-velocity impact monitoring of CFRP laminates based on FBG sensing network was constructed. • A reference database of impact location-impact response normalized characteristics quantities was constructed. • The low velocity impact on CFRP plate was successfully located with an average localization error of 8.19 mm. • The algorithm used in this paper is better than that of the PCA and BP neural network-based localization algorithms. This study conducted the localization of low velocity impact on Carbon Fiber Reinforced Plastic (CFRP) laminate using a normalized error outlier-based algorithm cooperating with Daubechies3 (Db3)-wavelet threshold noise reduction. The monitoring of CFRP plate impact was conducted by multiplex Fiber Bragg Grating (FBG) sensors connected to its upper surface, and the impact response signal was collected using an interrogator at a frequency of 2 kHz. Results showed that the Db3-wavelet threshold noise reduction algorithm could improve the impact localization performance of CFRP plate structure. By setting an appropriate outlier threshold and using a normalized error outlier-based algorithm, the low velocity impact on CFRP plate was successfully located with a maximum localization error of 21.35 mm and an average localization error of 8.19 mm. [ABSTRACT FROM AUTHOR]

In this paper, an analytical model for the flexural vibration damping of Carbon Fiber Reinforced Plastics (CFRP) cantilever beams was proposed, which is based on the Lamination Theory and Euler–Bernoulli Beam Theory. By using a finite element analysis and an analytical model, four sets of specific damping capacity with different pavement schemes were predicted, and flexural vibration test and damping analysis were carried out. Comparing the analytical model, finite element analysis, and test results, it could be found that the analytical model had relatively good accuracy in predicting the first-order natural frequency and specific damping capacity of the bending vibration of CFRP beams. The maximum error of the first-order natural frequency between the analysis result and the experimental result was 7.05%; the maximum specific damping capacity error was only 5.65%. Comparing the finite element analysis method and the experiment results, the maximum error of the first-order natural frequency was 7.8%, the error of the specific damping capacity was bigger, and the [±30°]5S specimen was as high as 18.7%. However, there was a significant error when the analytical model was used to predict the second-order natural frequency and the specific damping capacity of CFRP beam's flexural vibration. [ABSTRACT FROM AUTHOR]