Remote milling tool-wear monitoring and direct wear features extraction by image processing

2008 ◽  
Vol 1 (3) ◽  
pp. 246 ◽  
Author(s):  
Shahnawaz Hussain ◽  
Xun Chen
Keyword(s):  
Image Processing ◽  
Tool Wear ◽  
Features Extraction ◽  
Tool Wear Monitoring ◽  
Milling Tool ◽  
Wear Monitoring

Research on the Technique of Tool Wear Monitoring in Plunge Milling

2010 ◽  
Vol 426-427 ◽  
pp. 468-471
Author(s):  
Xu Da Qin ◽  
X.L. Ji ◽  
X. Yu ◽  
S. Hua ◽  
Wei Cheng Liu ◽  
...  
Keyword(s):  
Tool Wear ◽  
Cutting Condition ◽  
Mean Square ◽  
Tool Wear Monitoring ◽  
Plunge Milling ◽  
Vibration Signals ◽  
Bp Artificial Neural Network ◽  
Milling Tool ◽  
Wear Monitoring ◽  
The Mean

The technique of tool wear monitoring in plunge milling is studied. The mean of cutting force signals and the root mean square (RMS) of vibration signals are selected as characteristic quantities. The model between tool wear and the characteristic quantities is built using BP artificial neural network. The result of experiment shows that the module is fit for plunge milling wear’s testing under cutting condition, and it is helpful to monitoring plunge milling tool strong wear.

The milling tool wear monitoring using the acoustic spectrum

2011 ◽  
Vol 61 (5-8) ◽  
pp. 457-463 ◽  
Author(s):  
C. S. Ai ◽  
Y. J. Sun ◽  
G. W. He ◽  
X. B. Ze ◽  
W. Li ◽  
...  
Keyword(s):  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Acoustic Spectrum ◽  
Milling Tool ◽  
Wear Monitoring

scholarly journals An Intelligent Milling Tool Wear Monitoring Methodology Based on Convolutional Neural Network with Derived Wavelet Frames Coefficient

2019 ◽  
Vol 9 (18) ◽  
pp. 3912 ◽  
Author(s):  
Xincheng Cao ◽  
Binqiang Chen ◽  
Bin Yao ◽  
Shiqiang Zhuang
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Convolutional Neural Network ◽  
Single Step ◽  
High Signal ◽  
Work Piece ◽  
Wavelet Frames ◽  
Tool Wear Monitoring ◽  
Milling Tool ◽  
Wear Monitoring

Tool wear and breakage are inevitable due to the severe stress and high temperature in the cutting zone. A highly reliable tool condition monitoring system is necessary to increase productivity and quality, reduce tool costs and equipment downtime. Although many studies have been conducted, most of them focused on single-step process or continuous cutting. In this paper, a high robust milling tool wear monitoring methodology based on 2-D convolutional neural network (CNN) and derived wavelet frames (DWFs) is presented. The frequency band of high signal-to-noise ratio is extracted via derived wavelet frames, and the spectrum is further folded into a 2-D matrix to train 2-D CNN. The feature extraction ability of the 2-D CNN is fully utilized, bypassing the complex and low-portability feature engineering. The full life test of the end mill was carried out with S45C steel work piece and multiple sets of cutting conditions. The recognition accuracy of the proposed methodology reaches 98.5%, and the performance of 1-D CNN as well as the beneficial effects of the DWFs are verified.

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