Abstract
Acoustic emission (AE) source localization is one of the important purposes of nondestructive testing. The localization accuracy reflects the degree of coincidence between the identified location and the actual damage location. However, the anisotropy of carbon fiber three-dimensional braided composites will have a great impact on the accuracy of AE source location. In order to solve this problem, the time-frequency domain characteristics of AE signals in a carbon fiber braided composite tensile test were analyzed by Hilbert–Huang transform (HHT), and the corresponding relationship between damage modes and AE signals was established. Then, according to the time-frequency characteristics of HHT of tensile acoustic emission signals, the two-step method was used to locate the damage source. In the first step, the sound velocity was compensated by combining the time-frequency analysis results with the anisotropy of the experimental specimens, and the four-point circular arc method was used to locate the initial position. In the second step, there is an improvement of the Drosophila optimization algorithm, using the ergodicity of the chaotic algorithm and congestion adjustment mechanism in the fish swarm algorithm. The smoothing parameters and function construction in the probabilistic neural network were optimized, the number of iterations was reduced, the location accuracy was improved, and the damage mode of composite materials was obtained. Then, the damage location was obtained to achieve the purpose of locating the damage source.
Highlights
Carbon fiber composites have the characteristics of high strength and modulus, small thermal expansion coefficient and fatigue resistance [1,2,3]
The results showed that DEEMD is the more effective solution for extracting all damage modes existing in a single Acoustic emission (AE)
Change inofthe load-displacement corresponding with the acoustic emission (AE) ignal of theFigure tensile process, the tensile stress alongcurve the tensile direction of the sample increased rapidly, and energy parameter of carbon fiber braided composites
Summary
Carbon fiber composites have the characteristics of high strength and modulus, small thermal expansion coefficient and fatigue resistance [1,2,3]. AE nondestructive testing technology has become a research hotspot for defect and damage detection in composite materials [11,12]. In the research of AE detection technology, Barile proposed a method to detect and identify different types of damage in carbon fiber composites (CFRP) using AE technology [18]. Tested the ability of AE technology to detect unidirectional delamination of carbon fibre composites under double cantilever loads and found out the possible correlation between the frequency content of acoustic signals and damage evolution [27]. Drosophila the algorithm optimizes the analysis the tensile damage signal of carbon fiber braided composites, results show that the smoothingDrosophila parametersalgorithm and function construction of probabilistic neuraland network, reduces the number improved optimizes the smoothing parameters function construction of of iterations,neural and improves identification andoflocation accuracy of different damage of carbon probabilistic network,the reduces the number iterations, and improves the identification and fiber braided composites. Analysis of the Relationship between Mechanical Properties and Acoustic Signals
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