Robust tool wear monitoring system development by sensors and feature fusion

2022 ◽  
Author(s):  
Yu‐Ru Lin ◽  
Ching‐Hung Lee ◽  
Ming‐Chyuan Lu
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Feature Fusion ◽  
System Development ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

Development of Sound Based Tool Wear Monitoring System in Micro-Milling

2010 ◽  
Author(s):  
Ming-Hsing Lee ◽  
Ming-Chyuan Lu ◽  
Jhy-Cherng Tsai
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
System Development ◽  
Micro Milling ◽  
Tool Wear Monitoring ◽  
Classification Rate ◽  
Linear Discriminant ◽  
Audible Sound ◽  
Micro Tool ◽  
Wear Monitoring

A micro tool wear monitoring system based on the audible sound signal was developed and studied in this report. Three modules featuring the signal transformation, feature selection and classification, respectively, were included in this system. A micro milling experiment was conducted on a research platform and the audible sound signals collected by the microphone during the cutting processes were obtained for system development and verification. In the system development, the audible sound was first transformed to the frequency domain and the best features for condition classification was selected based on the class scatter criteria. In classifier design, the Fisher Linear Discriminant (FLD) was used to identify the tool wear condition from the selected features. This study shows that the performance of system was affected by the bandwidth of the feature, as well as the number of features selected for classification. With carefully selecting the parameters, higher than 90% classification rate can be obtained by this system for micro tool condition monitoring.

scholarly journals A New Time-Space Attention Mechanism Driven Multi-Feature Fusion Method for Tool Wear Monitoring

2021 ◽  
Author(s):  
Tingting Feng ◽  
Liang Guo ◽  
Hongli Gao ◽  
Tao Chen ◽  
Yaoxiang Yu ◽  
...  
Keyword(s):  
Tool Wear ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Fusion Method ◽  
Tool Wear Monitoring ◽  
Time And Space ◽  
Time Space ◽  
Sensor Signals ◽  
Wear Monitoring ◽  
New Time

Abstract In order to accurately monitor the tool wear process, it is usually necessary to collect a variety of sensor signals during the cutting process. Different sensor signals in the feature space can provide complementary information. In addition, the monitoring signal is time series data, which also contains a wealth of tool degradation information in the time dimension. However, how to fuse multi-sensor information in time and space dimensions is a key issue that needs to be solved. This paper proposes a new time-space attention mechanism driven multi-feature fusion method to realize the tool wear monitoring. Firstly, lots of features are established from different sensor signals and selected preliminarily. Then, a new feature fusion model with time-space attention mechanism is constructed to fuse features in time and space dimensions. Finally, the tool degradation model is established according to the predicted wear, and the tool remaining useful life is predicted by particle filter. The effectiveness of this method is verified by a tool life cycle wear experiment. Through comparing with other feature fusion models, it is demonstrated that the proposed method realizes the tool wear monitoring more accurately and has better stability.

Development of a tool wear-monitoring system for hard turning

2003 ◽  
Vol 43 (10) ◽  
pp. 973-985 ◽  
Author(s):  
C. Scheffer ◽  
H. Kratz ◽  
P.S. Heyns ◽  
F. Klocke
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Hard Turning ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

THE ADAPTABILITY OF A TOOL WEAR MONITORING SYSTEM UNDER CHANGING CUTTING CONDITIONS

2000 ◽  
Vol 14 (2) ◽  
pp. 287-298 ◽  
Author(s):  
R.G. SILVA ◽  
K.J. BAKER ◽  
S.J. WILCOX ◽  
R.L. REUBEN
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Cutting Conditions ◽  
Tool Wear Monitoring ◽  
Wear Monitoring
Sign in / Sign up

Export Citation Format

Share Document