Tool Wear Monitoring in Milling Process with Laser Scan Micrometer

2005 ◽  
pp. 764-768
Author(s):  
Takashi Matsumura ◽  
Takahiro Murayama ◽  
Eiji Usui
Keyword(s):  
Tool Wear ◽  
Milling Process ◽  
Tool Wear Monitoring ◽  
Wear Monitoring

scholarly journals A Neuro-Fuzzy Approach Based Tool Condition Monitoring in AISI H13 Milling

2018 ◽  
Author(s):  
Md. Shafiul Alam ◽  
Maryam Aramesh ◽  
Stephen Veldhuis
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Manufacturing Industry ◽  
Cutting Tool ◽  
Milling Process ◽  
Tool Condition Monitoring ◽  
Tool Wear Monitoring ◽  
Tool Condition ◽  
Wear Monitoring

In the manufacturing industry, cutting tool failure is a probable fault which causes damage to the cutting tools, workpiece quality and unscheduled downtime. It is very important to develop a reliable and inexpensive intelligent tool wear monitoring system for use in cutting processes. A successful monitoring system can effectively maintain machine tools, cutting tool and workpiece. In the present study, the tool condition monitoring system has been developed for Die steel (H13) milling process. Effective design of experiment and robust data acquisition system ensured the machining forces impact in the milling operation. Also, ANFIS based model has been developed based on cutting force-tool wear relationship in this research which has been implemented in the tool wear monitoring system. Prediction model shows that the developed system is accurate enough to perform an online tool wear monitoring system in the milling process.

Tool Wear Monitoring in Milling Processes Based on Cointegration Modeling

2010 ◽  
Vol 34-35 ◽  
pp. 1746-1751 ◽  
Author(s):  
Yin Hu Cui ◽  
Guo Feng Wang ◽  
Dong Biao Peng
Keyword(s):  
Tool Wear ◽  
Milling Process ◽  
Support Vector ◽  
Tool Wear Monitoring ◽  
Machining Processes ◽  
Machine Model ◽  
Force Signal ◽  
Frequency Components ◽  
Wear Monitoring ◽  
Cointegration Relationship

Tool wear monitoring plays an important role in the automatic machining processes. Therefore, a reliable method is necessary for practical application. In this paper, a new method based on cointegration theory was introduced to extract features from the cutting force signal in the milling process. Cointegration relationship between cutting forces of different directions could be found and the corresponding cointegration vector could also be calculated. In order to improve the reliability of pattern recognition, the cointegration vectors combined with the energy of the high-frequency components of the acoustic emission signals were used as features. Once all the features are extracted, they were trained and tested through a support vector machine model. Experiments were performed to verify this method and the results showed that it could efficiently recognize the tool wear status.

Tool wear monitoring using acoustic emission in the existence of chatter

1990 ◽  
Vol 28 (10) ◽  
pp. 1861-1869 ◽  
Author(s):  
YOICHI MATSUMOTO ◽  
NGUN TJIANG ◽  
BOBBIE FOOTE ◽  
YNGVE NAERHEIMH
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Tool Wear Monitoring ◽  
Wear Monitoring
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