Wavelet analysis of acoustic emission signals in boring

2000 ◽  
Vol 214 (5) ◽  
pp. 421-424 ◽  
Author(s):  
X Li ◽  
J Wu
Keyword(s):  
Acoustic Emission ◽  
Wavelet Analysis ◽  
Tool Wear ◽  
Metal Cutting ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Experimental Results ◽  
Cutting Processes ◽  
Ae Signals

Using acoustic emission (AE) signals to monitor tool wear states is one of the most effective methods used in metal cutting processes. As AE signals contain information on cutting processes, the problem of how to extract the features related to tool wear states from these signals needs to be solved. In this paper, a wavelet packet transform (WPT) method is used to decompose continuous AE signals during cutting; then the features related to tool wear states are extracted from decomposed AE signals. Experimental results verified the feasibility of using the WPT method to extract features related to tool wear states in boring.

Acoustic emission waveforms for damage monitoring in composite materials: Shifting in spectral density, entropy and wavelet packet transform

2021 ◽  
pp. 147592172110446
Author(s):  
Claudia Barile ◽  
Caterina Casavola ◽  
Giovanni Pappalettera ◽  
Vimalathithan Paramsamy Kannan
Keyword(s):  
Acoustic Emission ◽  
Spectral Density ◽  
Flicker Noise ◽  
Wavelet Packet ◽  
Research Work ◽  
Wavelet Packet Transform ◽  
Spectral Energy ◽  
Emission Data ◽  
Cfrp Composites ◽  
Damage Process

Signal-based acoustic emission data are analysed in this research work for identifying the damage modes in carbon fibre–reinforced plastic (CFRP) composites. The research work is divided into three parts: analysis of the shifting in the spectral density of acoustic waveforms, use of waveform entropy for selecting the best wavelet and implementation of wavelet packet transform (WPT) for identifying the damage process. The first two methodologies introduced in this research work are novel. Shifting in the spectral density is introduced in analogous to ‘flicker noise’ which is popular in the field of waveform processing. The entropy-based wavelet selection is refined by using quadratic Renyi’s entropy and comparing the spectral energy of the dominating frequency band of the acoustic waveforms. Based on the method, ‘dmey’ wavelet is selected for analysing the waveforms using WPT. The slope values of the shifting in spectral density coincide with the results obtained from WPT in characterising the damage modes. The methodologies introduced in this research work are promising. They serve the purpose of identifying the damage process effectively in the CFRP composites.

scholarly journals Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

2020 ◽  
Vol 29 ◽  
pp. 2633366X2097468
Author(s):  
Qiufeng Li ◽  
Tiantian Qi ◽  
Lihua Shi ◽  
Yao Chen ◽  
Lixia Huang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Glass Fiber ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Feature Vectors ◽  
Reinforced Plastics ◽  
Glass Fiber Reinforced ◽  
Fiber Reinforced Plastics ◽  
Intelligent Recognition ◽  
Ae Signals

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.

Acoustic Emission Sensing of Tool Wear in Face Milling

1987 ◽  
Vol 109 (3) ◽  
pp. 234-240 ◽  
Author(s):  
E. N. Diei ◽  
D. A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Cutting Forces ◽  
Flank Wear ◽  
Face Milling ◽  
Processing Scheme ◽  
Acoustic Emission Sensing ◽  
Vertical Milling Machine ◽  
Time Domain Averaging ◽  
Ae Signals

Acoustic Emission (AE) signal analysis was applied to on-line sensing of tool wear in face milling. Cutting tests were conducted on a vertical milling machine. AE signals, feed and normal components of cutting force and flank wear were measured and compared. A signal processing scheme for intermittent cutting forces and AE signals, based on the concept of time domain averaging (TDA) is proposed. The results indicate that both AE and cutting forces have parameters that correlate closely with flank wear.

Attenuation of Acoustic Emission Signals in Structural Interfaces

2012 ◽  
Vol 569 ◽  
pp. 343-346
Author(s):  
Xiang Hong Wang ◽  
Hong Wei Hu ◽  
Zhi Yong Zhang
Keyword(s):  
Acoustic Emission ◽  
Real World ◽  
Simple Structure ◽  
Size Structure ◽  
Experimental Results ◽  
Signal Energy ◽  
Real World Applications ◽  
Structural Interfaces ◽  
Ae Signals

Received acoustic emission (AE) signals are transmitted across structural interfaces in many real-world applications. This paper studies attenuation of the signals across two common structural interfaces. The experimental results indicate that interface has effects on attenuation, which depends on the relative scales of structures. Signal energy is strengthened due to multiple flections of signals on the small-size structure when an interface is constructed by different scales. Thus the received signals are distorted worse than the original signals. So it is a better way to mount sensors on a simple structure with a size as much as a structure incurred AE sources.

The Study on Buckling Deformation of Composite Pressure Vessel Based on Acoustic Emission Signals

2009 ◽  
Vol 87-88 ◽  
pp. 445-450
Author(s):  
Zhao Hui Hu ◽  
Hong Jun Liu ◽  
Rong Guo Wang ◽  
Xiao Dong He ◽  
Li Ma
Keyword(s):  
Acoustic Emission ◽  
Pressure Vessel ◽  
Composite Layer ◽  
High Amplitude ◽  
Experimental Results ◽  
Compression Stress ◽  
Composite Pressure Vessel ◽  
Deformation Compatibility ◽  
Ae Signals

The buckling deformation of the liner within composite pressure vessel is investigated using acoustic emission (AE) signals. The liner will fail with buckling deformation which is casued by compression stress induced by deformation compatibility beween composite layer and the liner. The experimental results show that these high-amplitude signals higher than 80dB are responsible for the buckling deformation of the liner within composite pressure vessel during unloading process.

On-Line Monitoring the Hard Turning Using Distribution Parameters of Acoustic Emission Signal

2015 ◽  
Vol 787 ◽  
pp. 907-911
Author(s):  
J. Bhaskaran
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Hard Turning ◽  
Metal Cutting ◽  
Cubic Boron Nitride ◽  
Emission Signal ◽  
Distribution Parameters ◽  
On Line ◽  
Ae Signal ◽  
Wear Monitoring

In hard turning, tool wear of cutting tool crossing the limit is highly undesirable because it adversely affects the surface finish. Hence continuous, online tool wear monitoring during the process is essential. The analysis of Acoustic Emission (AE) signal generated during conventional machining has been studied by many investigators for understanding the process of metal cutting and tool wear phenomena. In this experimental study on hard turning, the skew and kurtosis parameters of root mean square values of AE signal (AERMS) have been used for online monitoring of a Cubic Boron Nitride (CBN) tool wear.

A study of tool wear using statistical analysis of metal-cutting acoustic emission

Wear ◽  
1982 ◽  
Vol 76 (2) ◽  
pp. 247-261 ◽  
Author(s):  
E. Kannatey-Asibu ◽  
D.A. Dornfeld
Keyword(s):  
Acoustic Emission ◽  
Statistical Analysis ◽  
Tool Wear ◽  
Metal Cutting

Wavelet Packet analyses of Acoustic Emission Signal for Tool Wear in High Speed Milling

2013 ◽  
Vol 589-590 ◽  
pp. 600-605
Author(s):  
Shun Xing Wu ◽  
Peng Nan Li ◽  
Zhi Hui Yan ◽  
Li Na Zhang ◽  
Xin Yi Qiu ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
High Speed ◽  
Feature Vector ◽  
Wavelet Packet ◽  
High Speed Milling ◽  
Band Energy ◽  
Emission Signal ◽  
Frequency Band Energy

Tool wear condition monitoring technology is one of the main parts of advanced manufacturing technology and is a hot research direction in recent years. A method based on the characteristics of acoustic emission signal and the advantages of wavelet packets decomposition theory in the non-stationary signal feature extraction is proposed for tool wear state monitoring with monitor the change of acoustic emission signal feature vector. In this paper, through the method, firstly, acoustic emission signal were decomposed into 4 layers with wavelet packet analysis, secondly, the frequency band energy of the have been decomposed signal were extracted, thirdly, the frequency band energy that are sensitive to tool wear were selected as feature vector, and then the corresponding relation between feature vector and tool wear was established , finally, the state of the tool wear can be distinguished according to the change of feature vector. The results show that this method can be feasibility used to monitor tool wear state in high speed milling.

Multiple Blind Watermark Algorithm Based on Wavelet Packet Transform and Two-Dimensional Chaos Scramble

2012 ◽  
Vol 532-533 ◽  
pp. 1846-1850 ◽  
Author(s):  
Tian Zhong Zhao ◽  
Tao Chen ◽  
Jia Xu ◽  
Tao Wang ◽  
Wei Yi Shi
Keyword(s):  
Image Processing ◽  
Contrast Enhancement ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Experimental Results ◽  
Two Dimensional ◽  
Hidden Information ◽  
Image Contrast Enhancement ◽  
Multiple Watermarks ◽  
Watermark Algorithm

In this paper, multiple blind watermark algorithm based on wavelet packet transform (WPT) and two-dimensional chaos image scramble is put forward. In the sub-images of WPT, multiple watermarks are embedded and distilled blindly. Meantime, the image contrast enhancement to hidden information before hand can improve its robustness effectively. In order to improve the security, two-dimensional chaos image scramble algorithm is designed. Experimental results show that the multiple blind watermark algorithm has good invisibleness, security and robustness to common image processing and noise attack.

Sign in / Sign up

Export Citation Format

Share Document