Damage Modes Recognition and Hilbert-Huang Transform Analyses of CFRP Laminates Utilizing Acoustic Emission Technique
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Damage Modes Recognition and Hilbert-Huang Transform Analyses of CFRP Laminates Utilizing Acoustic Emission Technique Han WenQin 1,2 & Luo Ying 1 & Gu AiJun 3 & Fuh-Gwo Yuan 4
Received: 9 April 2015 / Accepted: 19 June 2015
# Springer Science+Business Media Dordrecht 2015
Abstract Discrimination of acoustic emission (AE) signals related to different damage modes is of great importance in carbon fiber-reinforced plastic (CFRP) composite materials. To gain a deeper understanding of the initiation, growth and evolution of the different types of damage, four types of specimens for different lay-ups and orientations and three types of specimens for interlaminar toughness tests are subjected to tensile test along with acoustic emission monitoring. AE signals have been collected and post-processed, the statistical results show that the peak frequency of AE signal can distinguish various damage modes effectively. After a AE signal were decomposed by Empirical Mode Decomposition (EMD) method, it may separate and extract all damage modes included in this AE signal apart from damage mode corresponding to the peak frequency. Hilbert-Huang Transform (HHT) of AE signals can clearly illustrate the frequency distribution of Intrinsic Mode Functions (IMF) components in timescale in different damage stages, and can calculate accurate instantaneous frequency for damage modes recognition to help understanding the damage process. Keywords CFRP . Acoustic emission . Peak frequency . EMD . HHT
* Luo Ying [email protected] 1
Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, China
2
School of Material Engineering, Jiangsu University of Technology, Changzhou, China
3
School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou, China
4
Dpartment of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA
Appl Compos Mater
1 Introduction Carbon fiber-reinforced plastic (CFRP) composite materials have been incorporated in increasing amounts in various engineering applications due to their excellent specific strength and specific stiffness in comparison to conventional materials [1]. Hence the knowledge of the damage behaviour and the transition of damage from a subcritical stage to a critical stage are of considerable interest in the case of composite materials. Composite structure failure often manifest as macro-damage generated by a few accumulative meso-damages along with time, which include matrix cracking, fiber breakage, delamination, and debonding. Acoustic emission (AE) technique is an efficient non-destructive method for detection and identification of various damage types in composite materials [2, 3]. Various signal processing and pattern recognition techniques have been performed for damage feature extraction from AE signals [4–9], which have four main methods such as: (1) damage classification according to a single AE parameter (amplitude, frequency, etc.), (2) wavelet level, (3) pattern recognition using several AE parameters, (4) cla
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