Tool Wear Monitoring at Turning

1994 ◽  
Vol 116 (4) ◽  
pp. 521-524 ◽  
Author(s):  
E. Waschkies ◽  
C. Sklarczyk ◽  
K. Hepp

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).

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH

2011 ◽  
Vol 141 ◽  
pp. 574-577
Author(s):  
Lu Zhang ◽  
Guo Feng Wang ◽  
Xu Da Qin ◽  
Xiao Liang Feng

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.


Sadhana ◽  
2008 ◽  
Vol 33 (3) ◽  
pp. 227-233 ◽  
Author(s):  
M. T. Mathew ◽  
P. Srinivasa Pai ◽  
L. A. Rocha

Author(s):  
D. Kondala Rao, Et. al.

In machining processes generally tool wear will be obtained with varying proportions. In the present work, the number of dominant features, which affect the tool wear, are studied and computed on Inconel 718 as work material with varying hardness (51, 53&55HRC) levels. The condition monitoring was done on three tools namely uncoated carbide, coated carbide and ceramic tools. By using L9 Taguchi’s orthogonal array, speed, feed, depth of cut (DOC) and hardness are considered as input operating parameters. By indirect method of Acoustic emission (AE) technique, signals were collected using Lab VIEW software and dominating features were calculated using the MATLAB. The features were trained in neural network and got the relation between tool wear, surface roughness, temperature and features. The simulated data was analyzed by Grey relational analysis (GRA) and the dominating features ranking sequence was obtained   for all the three tools and same ranking was also observed with ANOVA. Since there are no common influencing features among these three tools and hence further investigation continued with statistical mathematical modeling. With Akaike information criterion a mathematical model is developed to find the dominant features. By mathematical modeling the sequence in evaluating tool wear was found to be Kurtosis, Frequency, Variance, Mean and RMS and also a relation between tool wear and dominant features was developed which can be readily used by layman for calculating the tool wear.


2010 ◽  
Author(s):  
Yinhu Cui ◽  
Guofeng Wang ◽  
Dongbiao Peng ◽  
Xiaoliang Feng ◽  
Lu Zhang ◽  
...  

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