Abstract
Abstract—On-line cutting tool condition monitoring becomes one of the most critical requirements in machining processes for improving the efficiency and the autonomy of CNC machine tools. The processes can be significantly improved by using an intelligent integration of sensor information to detect and identify accurately the tool condition under various cutting parameters. This paper presents a structured and comprehensive approach for on-line tool condition identification in metal cutting processes using ANN based multi-sensor fusion strategy. Various sensing techniques are combined with different preprocessing techniques to select suitable monitoring indices and then numerous models for on-line tool condition identification. The proposed approach is built progressively by examining monitoring indices from various aspects and making modeling decision step by step. The results indicate a significant improvement and a good reliability in identifying various tool conditions regardless of the variation in cutting parameters.
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.
