scholarly journals Possibility of applying the RBF neural network to the on-line tool condition monitoring systems

Mechanik ◽  
2016 ◽  
pp. 1056-1057
Author(s):  
Sebastian Bombiński ◽  
Krzysztof Błażejak ◽  
Mirosław Nejman
Keyword(s):  
Neural Network ◽  
Condition Monitoring ◽  
Rbf Neural Network ◽  
Tool Condition Monitoring ◽  
Monitoring Systems ◽  
Tool Condition ◽  
Condition Monitoring Systems ◽  
On Line

scholarly journals A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 108
Author(s):  
Mustafa Kuntoğlu ◽  
Abdullah Aslan ◽  
Danil Yurievich Pimenov ◽  
Üsame Ali Usca ◽  
Emin Salur ◽  
...  
Keyword(s):  
Tool Wear ◽  
Condition Monitoring ◽  
Metal Cutting ◽  
Cutting Tool ◽  
Complex Structure ◽  
Tool Condition Monitoring ◽  
Monitoring Systems ◽  
Tool Condition ◽  
Condition Monitoring Systems ◽  
Cutting Zone

The complex structure of turning aggravates obtaining the desired results in terms of tool wear and surface roughness. The existence of high temperature and pressure make difficult to reach and observe the cutting area. In-direct tool condition, monitoring systems provide tracking the condition of cutting tool via several released or converted energy types, namely, heat, acoustic emission, vibration, cutting forces and motor current. Tool wear inevitably progresses during metal cutting and has a relationship with these energy types. Indirect tool condition monitoring systems use sensors situated around the cutting area to state the wear condition of the cutting tool without intervention to cutting zone. In this study, sensors mostly used in indirect tool condition monitoring systems and their correlations between tool wear are reviewed to summarize the literature survey in this field for the last two decades. The reviews about tool condition monitoring systems in turning are very limited, and relationship between measured variables such as tool wear and vibration require a detailed analysis. In this work, the main aim is to discuss the effect of sensorial data on tool wear by considering previous published papers. As a computer aided electronic and mechanical support system, tool condition monitoring paves the way for machining industry and the future and development of Industry 4.0.

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