Study on accurate tool wear monitoring based on acoustic emission signal

2010 ◽  
Author(s):  
Yinhu Cui ◽  
Guofeng Wang ◽  
Dongbiao Peng ◽  
Xiaoliang Feng ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Tool Wear Monitoring ◽  
Emission Signal ◽  
Wear Monitoring

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

Tool Wear Monitoring in Milling Processes Based on Time-Frequency Analysis of Acoustic Emission

2011 ◽  
Vol 141 ◽  
pp. 574-577
Author(s):  
Lu Zhang ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Xiao Liang Feng
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Frequency Domain ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Time Frequency ◽  
Svm Model ◽  
The Status ◽  
Wear Monitoring

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, it is necessary to establish a reliable method to predict tool wear status. In this paper, features of acoustic emission (AE) extracted from time-frequency domain are integrated with force features to indicate the status of tool wear. Meanwhile, a support vector machine (SVM) model is employed to distinguish the tool wear status. The result of the classification of different tool wear status proved that features extracted from time-frequency domain can be the recognize-features of high recognition precision.

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.

Identification Method of Tool Wear Based on Locally Linear Embedding and Support Vector Machine

2012 ◽  
Vol 246-247 ◽  
pp. 1289-1293
Author(s):  
Zheng Qiang Li ◽  
Peng Nie ◽  
Shu Guo Zhao
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
Support Vector ◽  
Emission Signal ◽  
Identification Method ◽  
Local Linear Embedding ◽  
Local Linear ◽  
Data Points ◽  
Linear Embedding

Aiming at the nonlinear characteristics of the tool wear Acoustic Emission signal, tool wear state identification method is proposed based on local linear embedding and vector machine supported. The local linear embedding algorithm makes high dimensional information down to low dimension feature space through commutation, and thus to compress the data for highlighting signal features. This algorithm well compensates for the weakness of linear dimension reduction failing to find datasets nonlinear structure. In this paper, acoustic emission signal is firstly made by phase space reconstruction. Using local linear embedding method, the high dimension space mapping data points are reflected into low-dimensional space corresponding data points, then extracting tool wear state characteristics, and using vector machine supported classifier to identify classification of the tool wear conditions. Experimental results show that this method is used for the exact recognition of the tool wear state, and has widespread tendency.

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.

Tool Wear Monitoring at Turning

1994 ◽  
Vol 116 (4) ◽  
pp. 521-524 ◽  
Author(s):  
E. Waschkies ◽  
C. Sklarczyk ◽  
K. Hepp
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Noise Signal ◽  
Depth Of Cut ◽  
Tool Wear Monitoring ◽  
Small Depth ◽  
Wear Monitoring ◽  
Flank Face ◽  
Automatic Tool ◽  
Finishing Processes

A new method for automatic tool wear monitoring at turning has been developed based on the analysis of the continuous acoustic emission (machining noise) generated by the tool during machining. Different wear types (wear of tool flank face and tool chipping) result in changes in the different characteristic values of the noise signal. In case of a uniform abrasion of the insert, e.g., flank face or crater wear, an increased mean signal level is observed, whereas for microbreakage at the edge, an increase of the crest factor with nearly constant mean signal level is found. The burst-like signals from collision between chip and tool and from chip breakage have to be eliminated from analysis to avoid the distortion of the signal parameters of the continuous acoustic emission. This method should be well suited especially for monitoring of finishing processes (small depth of cut).

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