DAMAGE IDENTIFICATION OF CFRP SHAFT BASED ON BACK-PROPAGATION NEURAL NETWORK

Abstract

Fiber Bragg Grating (FBG) sensor is a new type of optical sensor that is light in quality, small in volume, easy to achieve distributed measurement, and easy to embed. When FBG sensor technology is introduced to the damage detection of the CFRP shaft, the shortcomings of conventional damage detection technologies, including ultrasonic testing and radiographic testing, can be effectively overcome. Considering the working load, operating environment and life requirements of the CFRP shaft , combined with the relevant national and industry standards for composite material testing, Prepare three CFRP shaft with embedded fiber gratings (non-destructive CFRP shaft, single hole CFRP shaft , double hole CFRP shaft), the hole diameter is 1mm, and the layering of CFRP shaft is ([0 / 90 / 45 / 0 / 0 / 45 / -45 / 0 / -45 / 0 / 90 / 90 / 0 / -45 / 0 / -45 / 45 / 0 / 0 / 45 / 90 / 0 ]). FBG is embedded between 18 and 19 layers, pasted on the surface of CFRP shaft with 10 FBG sensors (named FBG1, FBG2... FBG10). The strain information of the CFRP shaft is obtained through fiber grating to obtain the deformation of the CFRP shaft under a three-point bending load. Each CFRP shaft is loaded 12 times (A1, A2, A3, B1, B2, B3, C1, C2, C3, D1, D2, D3). Through the BP neural network method, the mapping relationship between strain distribution and damage model was established. In the model, the FBG sensor measures the strain of the CFRP shaft and forms the input of the neural network. Combined with the damage situation of the composite material, the learning samples of the network formed. After learning and training, the neural network can damage recognition. To better verify the accuracy of the neural network to distinguish and identify the CFRP shaft non-destructive, single-hole, and double-hole damage patterns. The finite element analysis was used to simulate the damage of three CFRP shafts under the same experimental conditions, and extract the eigenvalues at the same position to form the input of the network, combined with the damage situation of the composite material, constitutes a learning sample of the network. The experiment shows that the CFRP shaft experiment based on BP neural network verifies the reliability of the CFRP shaft damage recognition method. The finite element analysis is used to extract the strain information of CFRP shaft measuring points with different damage modes. The damage position information and strain information were input into the BP neural network. The experimental results are in good agreement with the simulation results, which verifies the accuracy of the BP neural network to distinguish the CFRP shaft non-destructive, single-hole, and doublehole damage patterns.