A New Tool Wear Condition Monitoring Method Based on Deep Learning under Small Samples

Measurement ◽  
2021 ◽  
pp. 110622
Author(s):  
Yuqing Zhou ◽  
Gaofeng Zhi ◽  
Wei Chen ◽  
Qijia Qian ◽  
Dedao He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Small Samples ◽  
Monitoring Method ◽  
Wear Condition

scholarly journals A Novel Order Analysis and Stacked Sparse Auto-Encoder Feature Learning Method for Milling Tool Wear Condition Monitoring

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2878 ◽  
Author(s):  
Jiayu Ou ◽  
Hongkun Li ◽  
Gangjin Huang ◽  
Qiang Zhou
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Manufacturing Industry ◽  
Feature Learning ◽  
Variable Speed ◽  
Monitoring Methods ◽  
State Monitoring ◽  
Monitoring Method ◽  
Order Analysis ◽  
Wear Condition

Milling is a main processing mode of the modern manufacturing industry, which seriously affects the quality and precision of the machined workpiece. However, it is difficult to monitor the tool wear condition in the continuous cutting process, especially under a variable speed condition. The existing tool wear condition monitoring methods only carry out analysis with a constant engine speed. Different from the general monitoring methods, this paper put forward a milling cutter wear condition monitoring method based on order analysis (OA) and stacked sparse autoencoder (SSAE). The methodology in the research include signals feature extraction and tool wear state monitoring and were designed to analyze the three-phase spindle current signals instead of the traditional force signals and vibration signals. The variable speed signals were transformed into angle domain stationary signals by order analysis, and the SSAE neural network was used to monitor the tool wear state. The proposed method was verified on the laboratory signals and the results showed a better performance than the other methods and a better applicability in actual industrial manufacturing.

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.

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
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