Abstract
Tool condition monitoring (TCM) in advanced manufacturing is concerned with cutting tool operational status monitoring and damage diagnosis. In the present study, an innovative TCM approach based on sensor data modeling and model frequency analysis is proposed. The new approach creates a paradigmatic shift to the conventional TCM techniques and can potentially realize autonomous cutting tool anomaly diagnosis satisfying the requirement of advanced manufacturing. When applying the proposed approach, the data from sensors are not directly utilized for monitoring cutting tool status. Instead, the data from sensors are utilized to build a dynamic process model. This allows the unique frequency-domain properties of the machining process to be extracted and used to reveal, in real time, cutting tool health conditions. Experimental studies are conducted to verify the effectiveness of the proposed approach and to demonstrate the superiority of the new approach over conventional TCM techniques.
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: IEEE Transactions on Systems, Man, and Cybernetics: Systems
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.
