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Acoustic emission signal of fiber-reinforced composite grinding: frequency components and damage pattern recognition

  • Jinhua Wei
  • Haoji Wang
  • Bin LinEmail author
  • Tianyi Sui
  • Feifei Zhao
  • Sheng Fang
ORIGINAL ARTICLE
  • 23 Downloads

Abstract

Current researches of acoustic emission (AE) mainly put the focus on fault diagnosis of traditional isotropic materials machining process or analysis of fiber-reinforced composite (FRC) tensile or bending strength, while there are merely studies on AE research of FRC grinding process. Numerous kinds of damage occur during FRC grinding process owing to their complicated structure. The main purpose of this paper is to extract proper index to estimate AE signals of FRC grinding and to recognize damage patterns, thus realizing FRC processing on-line detection. AE signals are obtained by single-grain grinding experiments of quartz fiber–reinforced silicon dioxide matrix composite (SiO2/SiO2). The AE signal features are discussed, and the outstanding character of frequency components is proposed. The frequency of each damage pattern is analyzed and verified. AE effective voltage value (EVV) and event number percentage (ENP) of peak frequency (PF) of AE signals with processing parameters are researched. The results show that for the same kind of FRCs, frequency components of AE signals are only affected by damage patterns rather than processing parameters or grinding directions, thus being a proper estimate index. There are four main frequency bands during SiO2/SiO2 grinding. The frequency 6.4–9.8 KHz corresponds to fiber fracture, 14.8–17.9 KHz is fiber debonding, 23.6–26.4 KHz is debris rubbing with workpiece and tool, and 34–35.5 KHz is matrix crack. EVV has a similar changing trend to grinding force with machining parameters. AE ENP of PF that the maximum peak amplitude (PA) corresponds to could quantitatively confirm the main damage modes under each processing condition.

Keywords

Fiber-reinforced composites Acoustic emission Frequency component Grinding damage pattern recognition Effective voltage value Event number percentage of AE peak frequency 

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Notes

Acknowledgement

During the course of paper writing, thanks very much for the valuable suggestions from a young teacher Shuai Yan. His comments on this research is important. I am deeply influenced by his rigorous attitude to research and working manner in daily life.

Funding information

This work was financially supported by the National Natural Science Foundation of China (NO.51375333).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Jinhua Wei
    • 1
  • Haoji Wang
    • 1
  • Bin Lin
    • 1
    • 2
    Email author
  • Tianyi Sui
    • 1
  • Feifei Zhao
    • 1
  • Sheng Fang
    • 2
  1. 1.Key Laboratory of Advanced Ceramics and Machining Technology, Ministry of EducationTianjin UniversityTianjinChina
  2. 2.Key Laboratory of Advanced Functional Composites, CASCBeijingChina

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