Acoustic emission signal analysis during chip formation process in high speed machining of 7050-T7451 aluminum alloy and Inconel 718 superalloy

2017 ◽  
Vol 27 ◽  
pp. 114-125 ◽  
Author(s):  
Bing Wang ◽  
Zhanqiang Liu
Keyword(s):  
Acoustic Emission ◽  
Aluminum Alloy ◽  
Acoustic Emission Signal ◽  
Formation Process ◽  
Chip Formation ◽  
Signal Analysis ◽  
High Speed ◽  
High Speed Machining ◽  
Emission Signal ◽  
Inconel 718 Superalloy

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.

Sign in / Sign up

Export Citation Format

Share Document