Abstract
Tool condition monitoring (TCM) plays an important role in milling process. An accurate TCM system not only improves the quality of products, but also helps improving the processing efficiency. Distinguishing the condition of tool wear effectively is a central part in the TCM. In this paper, a tool condition classification based on Bandpass Filter and Kernel Extreme Learning Machine (KELM) is proposed. Firstly, the Bandpass Filter is used to enhance the noise-signal ratio of original signals which are detected by cutting force and acoustic emission sensors. Then several statistical features of time and frequency domain for the preprocessed data are calculated. Finally, the KELM classifier is applied to identify the tool wear state. Experiment shows that the proposed method has outperformed the KELM-based method with different type's signals.
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 callDisclaimer: 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.
