scholarly journals Comparative Study on Path Interval and Two Tool Condition Parameters in Ball and Filleted End Milling

2021 ◽  
Vol 15 (4) ◽  
pp. 311-320
Author(s):  
Tsutomu Sekine
Keyword(s):  
Comparative Study ◽  
End Milling ◽  
Tool Condition

Sensorless Monitoring of Cutting Force Variation With Fractured Tool Under Heavy Cutting Condition

2016 ◽  
Author(s):  
Yuki Yamada ◽  
Yasuhiro Kakinuma ◽  
Takamichi Ito ◽  
Jun Fujita ◽  
Hirohiko Matsuzaki
Keyword(s):  
Cutting Force ◽  
End Milling ◽  
Moving Average ◽  
Cutting Condition ◽  
Force Estimation ◽  
Signal Processing Technique ◽  
Tool Condition ◽  
Force Variation ◽  
Monitoring Performance ◽  
Peak Value

Cutting force is widely regarded as being the one of the most valuable information for tool condition monitoring. Considering sustainability, sensorless cutting force monitoring technique using inner information of machine tool attracts attention. Cutting force estimation based on motor current is one of the example, and it is applicable to detection of tool breakage with some signal processing technique. However, current signal could not capture fast variation of cutting force. By improving monitoring performance of cutting force, the hidden tool condition information is more accessible. In this study, monitoring performance of cutting force variation due to tool fracture was enhanced by using multi-encoder-based disturbance observer (MEDOB) and simple moving average. Friction force and torque which deteriorate monitoring performance was eliminated by moving average. First, monitoring accuracy of cutting force was verified through end milling test. Next, local peak value of estimated cutting force was extracted and the ratio of neighboring peak value was calculated to capture the tool fracture. Estimated value using MEDOB could capture the variation resulting from tool fracture.

Progressive tool condition monitoring of end milling from machined surface images

2016 ◽  
Vol 232 (2) ◽  
pp. 251-266 ◽  
Author(s):  
Samik Dutta ◽  
Surjya K Pal ◽  
Ranjan Sen
Keyword(s):  
Condition Monitoring ◽  
Flank Wear ◽  
End Milling ◽  
Tool Condition Monitoring ◽  
Discrete Wavelet ◽  
Tool Flank Wear ◽  
Tool Condition ◽  
Milled Surface ◽  
Occurrence Matrix ◽  
Surface Images

Indirect tool condition monitoring in end milling is inevitable to produce high-quality finished products due to the complexity of end-milling process. Among the various indirect tool condition monitoring techniques, monitoring based on image processing by analyzing the surface images of final product is gaining high importance due to its non-tactile and flexible nature. The advances in computing facilities, texture analysis techniques and learning machines make these techniques feasible for progressive tool flank wear monitoring. In this article, captured end-milled surface images are analyzed using gray level co-occurrence matrix–based and discrete wavelet transform–based texture analyses to extract features which have a good correlation with progressive tool flank wear. Contrast and second diagonal moment are extracted from gray level co-occurrence matrix and root mean square and energy are extracted from discrete wavelet decomposition of end-milled surface images as features. Finally, these four features are utilized to build support vector machine–based regression models for predicting progressive tool flank wear with 94.8% average correlation between predicted and measured tool flank wear values.

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