Face Milling Tool Wear Condition Monitoring Based on Wavelet Transform and Shannon Entropy

2014 ◽  
Vol 541-542 ◽  
pp. 1419-1423 ◽  
Author(s):  
Min Zhang ◽  
Hong Qi Liu ◽  
Bin Li
Keyword(s):  
Wavelet Transform ◽  
Tool Wear ◽  
Condition Monitoring ◽  
Shannon Entropy ◽  
Spindle Motor ◽  
Face Milling ◽  
Motor Current ◽  
Milling Tool ◽  
Spindle Motor Current ◽  
Wear Condition

Tool condition monitoring is an important issue in the advanced machining process. Existing methods of tool wear monitoring is hardly suitable for mass production of cutting parameters fluctuation. In this paper, a new method for milling tool wear condition monitoring base on tunable Q-factor wavelet transform and Shannon entropy is presented. Spindle motor current signals were recorded during the face milling process. The wavelet energy entropy of the current signals carries information about the change of energy distribution associated with different tool wear conditions. Experiment results showed that the new method could successfully extract significant signature from the spindle-motor current signals to effectively estimate tool wear condition during face milling.

scholarly journals Tool Wear Condition Monitoring by Combining Variational Mode Decomposition and Ensemble Learning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6113
Author(s):  
Jun Yuan ◽  
Libing Liu ◽  
Zeqing Yang ◽  
Yanrui Zhang
Keyword(s):  
Tool Wear ◽  
Spindle Motor ◽  
Current Sensor ◽  
Processing Conditions ◽  
Variational Mode Decomposition ◽  
Current Signal ◽  
Motor Current ◽  
Tool Condition ◽  
Mode Decomposition ◽  
Spindle Motor Current

Most online tool condition monitoring (TCM) methods easily cause machining interference. To solve this problem, we propose a method based on the analysis of the spindle motor current signal of a machine tool. Firstly, cutting experiments under multi-conditions were carried out at a Fanuc vertical machining center, using the Fanuc Servo Guide software to obtain the spindle motor current data of the built-in current sensor of the machine tool, which can not only apply to the actual processing conditions but, also, save costs. Secondly, we propose the variational mode decomposition (VMD) algorithm for feature extraction, which can describe the tool conditions under different cutting conditions due to its excellent performance in processing the nonstationary current signal. In contrast with the popular wavelet packet decomposition (WPD) method, the VMD method was verified as a more effective signal-processing technique according to the experimental results. Thirdly, the most indicative features that relate to the tool condition were fed into the ensemble learning (EL) classifier to establish a nonlinear mapping relationship between the features and the tool wear level. Compared with existing TCM methods based on current sensor signals, the operation process and experimental results show that using the proposed method for the monitoring signal acquisition is suitable for the actual processing conditions, and the established tool wear prediction model has better performance in both accuracy and robustness due to its good generalization capability.

Real-Time Tool Wear Monitoring Based on Feed Motor Current in Chuck-Center Mounting Condition

2011 ◽  
Vol 341-342 ◽  
pp. 307-312 ◽  
Author(s):  
Hamed Tanabi ◽  
Naser Babaei ◽  
Alireza Babaei
Keyword(s):  
Tool Wear ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Machining Process ◽  
Depth Of Cut ◽  
Practical Applications ◽  
Elastic Deflection ◽  
Motor Current ◽  
On Line ◽  
Wear Condition

On-line tool wear condition monitoring is a significant area of research in automated machining process. This study concerns the development of an on-line tool wear condition monitoring system based on a fuzzy logic scheme. The feed motor current was measured and used as the input of a fuzzy process. Previously, most studies in this field have assumed the workpiece as rigid. However, in practice, cutting force causes the elastic deflection on workpiece and changes actual cutting depth. Therefore, due to coupling between actual depth of cut and feed motor current, the measured current values vary on cutting length according to workpiece elastic deflection pattern. This oscillation in current signal which is used as the input of monitoring system, can affect output results. Thus, in this study in order to achieve more accurate results, the workpiece clamping condition has been considered and through monitoring software a correction factor has been applied on outputs. Experimental results indicate that the proposed method can be reliable for practical applications of tool monitoring systems.

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