scholarly journals Tool Wear Compensation in End Milling Based on Cutting Force Measuring and Tool Visual Monitoring

Author(s):  
Franc Čuš ◽  
Tomaž Irgolič ◽  
Uroš Župerl
2013 ◽  
Vol 70 (9-12) ◽  
pp. 1835-1845 ◽  
Author(s):  
Junzhan Hou ◽  
Wei Zhou ◽  
Hongjian Duan ◽  
Guang Yang ◽  
Hongwei Xu ◽  
...  

2017 ◽  
Vol 867 ◽  
pp. 165-170
Author(s):  
Isha Srivastava ◽  
Ajay Batish

The aim of this study were to evaluate the performance of PVD (TiAlN+TiN) and CVD (TiCN+Al2O3+TiN) coated inserts in end milling of EN–31 hardened die steel of 43±1 HRC during dry and MQL (Minimum quantity lubrication) machining. The experiments were conducted at a fixed feed rate, depth of cut and varying cutting speed to measure the effect of cutting speed on cutting force and tool wear of CVD and PVD-coated inserts. The performance of CVD and PVD-coated inserts under dry and MQL condition by measuring the tool wear and cutting force were compared. During cutting operation, it was noticed that PVD inserts provide less cutting force and tool wear as compared to the CVD inserts under both dry as well as the MQL condition because PVD inserts have a thin insert coating and CVD inserts have a thick insert coating, but PVD inserts experience catastrophic failure during cutting operation whereas CVD inserts have a capability for continuous machining under different machining. Tool wear has measured by SEM analysis. The result shows that MQL machining provides the optimum results as compared to the dry condition. MQL machining has the ability to work under high cutting speed. As the cutting speed increases the performance of dry machining was decreased, but in MQL machining, the performance of the inserts was increased with increases of cutting speed. MQL machining generates less cutting force on the cutting zone and reduces the tool wear which further increase the tool life.


2016 ◽  
Vol 78 (6-10) ◽  
Author(s):  
N.H.M. Tahir ◽  
R. Muhammad ◽  
J. A. Ghani ◽  
M. Z. Nuawi ◽  
C. H. C. Haron

Tool condition monitoring (TCM) system in the industry are mainly used to detect tool wear, breakage and chatter on the tool. Tool wear of AISI P20 under various cutting conditions have been investigated in end milling using cutting force signals due flank wear progression. This study is focused on the piezoelectric sensor system which is integrated on rotating cutting tool for tool wear monitoring system in milling process. The signal captured by piezoelectric sensors are analyzed in time and frequency domain. The signal amplitudes of main cutting force, Fc in time domain are increased, while the peak of the amplitude in frequency domain is decreased as the flank wear and cutting speed increases. By using 3D I-kazTM statistical analysis method, the relationship and correlation between I-kaz coefficients, Z∞  values with resultant flank wear width data, VB are proved. The results show that 3D I-kazTM statistical analysis method can be effectively used to monitor tool wear progression using a wireless telemetry system during milling operations.


2001 ◽  
Vol 109 (3) ◽  
pp. 229-235 ◽  
Author(s):  
A Sarhan ◽  
R Sayed ◽  
A.A Nassr ◽  
R.M El-Zahry

2015 ◽  
Vol 799-800 ◽  
pp. 312-318 ◽  
Author(s):  
Somkiat Tangjitsitcharoen ◽  
Thanathip Jatinandana ◽  
Angsumalin Senjuntichai

This research proposed an in-process tool wear prediction during the ball-end milling process by utilizing the cutting force ratio. The dimensionless cutting force ratio is proposed to cut off the effects of the work material and the combination of cutting conditions. The in-process tool wear prediction model is developed by employing the exponential function, which consists of the spindle speed, the feed rate, the depth of cut, the tool diameter, and the cutting force ratio. The experimentally obtained results showed that the cutting force ratio can be utilized to predict the tool wear of ball-end milling tool. The new cutting tests have been employed to verify the model and the results run satisfaction. It has been proved that the in-process tool wear prediction model can be used to predict the tool wear regardless of the cutting conditions with the highly acceptable prediction accuracy.


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