Fiber Reinforced Plastics/Composites ›› 2019, Vol. 0 ›› Issue (6): 30-36.

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EXPERIMENTAL STUDY ON BENDING DEFORMATION AND FAILURE OF LAMINATED DAMAGE COMPOSITES

QIN Reng1, LI Xiao-tong2, SHANG Ya-jing1, ZHOU Wei1*   

  1. 1.Non-destructive Testing Laboratory, Hebei University, Baoding 071002, China;
    2.Patent Examination Cooperation Henan Center of the Patent Office, SIPO, Zhengzhou 450000, China
  • Received:2018-09-11 Online:2019-06-25 Published:2019-06-28

Abstract: In order to study the damage characteristic of composite with symmetric delamination, the buckling failure behavior and deformation field information of specimen containing symmetric multilayered unidirectional glass fiber reinforced composite specimens under three-point bending test were studied by using acoustic emission (AE) and digital image correlation (DIC) method. Then, the damage pattern of specimens was identified by k-means clustering analysis. The results showed that lamination damage led to the reduction of the bearing capacity of the material and the weakening of the resistance to deformation, and the layered damage under compression load was more obvious than that under tensile load. The layered damage composite material produced more AE damage signals during loading. The AE signals can be divided into three categories: low frequency, medium frequency and high frequency by cluster analysis, which were corresponding to matrix cracking and delamination, fiber debonding and fiber fracture of the symmetrical layered specimens. The displacement field of the specimen with stratified damage was unstable and the distribution of strain field was more dispersed than that of the blank control sample. AE technology can monitor internal damage information in real time, and DIC method can analyze the changes of specimen surface displacement field. Therefore, the combination of the two technologies is of great value for the structural health monitoring of composite materials.

Key words: composites, symmetric multiple delamination, acoustic emission, digital image correlation, cluster analysis

CLC Number: