Online tool wear condition monitoring using binocular vision

2017 ◽  
Vol 59 (4) ◽  
pp. 203-210 ◽  
Author(s):  
Aibin Zhu ◽  
Dayong He ◽  
Jianwei Zhao ◽  
Hongling Wu
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Binocular Vision ◽  
Online Tool ◽  
Wear Condition

Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

2014 ◽  
Vol 541-542 ◽  
pp. 1419-1423 ◽  
Author(s):  
Min Zhang ◽  
Hong Qi Liu ◽  
Bin Li
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Shannon Entropy ◽  
Spindle Motor ◽  
Face Milling ◽  
Motor Current ◽  
Milling Tool ◽  
Spindle Motor Current ◽  
Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

Intelligent Tool Condition Monitoring System Based on Rough Sets and Mathematical Morphology

2007 ◽  
Vol 10-12 ◽  
pp. 722-726 ◽  
Author(s):  
Li Zhang ◽  
Shi Ming Ji ◽  
Yi Xie ◽  
Qiao Ling Yuan ◽  
Yin Dong Zhang ◽  
...  
Keyword(s):  
Tool Wear ◽  
Mathematical Morphology ◽  
Condition Monitoring ◽  
Rough Sets ◽  
Cutting Tool ◽  
Cutting Tools ◽  
Tool Condition Monitoring ◽  
Online Tool ◽  
Tool Condition ◽  
Condition Monitoring System

The image of cutting tools provides reliable information regarding the extent of tool wear. In this paper, we propose the theory of image processing based on rough sets and mathematical morphology to analyzing the flank faces which are chosen as our monitoring object. First, through plotting the appropriate subset, the rough sets filter is used to enhancement the image of tool wear. Then, the mathematical morphology theory is applied to process the translated binary image. Finally, tool condition monitoring is realized by measuring the area of tool wear. This paper gives the corresponding monitoring principal and proposes a new algorithm to process the cutting tool image. The algorithm is also flexible and fast enough to be implemented in real time for online tool wear or tool condition monitoring.

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