Abstract
Tool wear state classification has good potential to play a critical role in ensuring the dimensional accuracy of the work piece and prevention of damage to cutting tool in machining process. During machining process, tool wear is an important factor which contributes to the variation of spindle motor current, speed, feed and depth of cut. In the present work, online tool wear state detecting method with spindle motor current in turning operation for Al/SiC composite material is presented. By analyzing the effects of tool wear as well as the cutting parameters on the current signal, the models on the relationship between the current signals and the cutting parameters are established with partial design taken from experimental data and regression analysis. The fuzzy classification method is used to classify the tool wear states so as to facilitate defective tool replacement at the proper time.
Highlights
The development of an effective means to monitor the wear condition of cutting tools is one of the most important issues in the automation of the cutting process [1]
A total of 55 tool wear cutting tests were conducted under various cutting conditions. 35 samples were randomly picked as learning samples and 21 samples were used as the test samples in the classification phase
The model which shows the relationship between the current signals and cutting parameters for different tool wear states (0.3 to 0.9) are established through experimental study and regression analysis
Summary
The development of an effective means to monitor the wear condition of cutting tools is one of the most important issues in the automation of the cutting process [1]. Mannan and Nilsson [8] presented a method using motor current measured from the spindle motor and feed motor to estimate the static torque and thrust in drilling and to monitor the tool condition. Research to date has shown that there are four parameters including cutting force, acoustic emission, motor current and vibration, which could be used to monitor tool, wear condition during turning operation. Yao et al, [14] have proposed a new method for tool wear detection with different cutting conditions and detected signals which includes the model of wavelet fuzzy neural network with acoustic emission (AE) and the model of fuzzy classification with motor current. The essence of the method is to establish a simple model relating the measured current value and the flank wear state under different cutting conditions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Modeling and Numerical Simulation of Material Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
