Abstract
This paper describes the utilisation of multi sensor fusion model using force, vibration, acoustic emission, strain and sound sensors for monitoring tool wear in end milling operations. The paper applies the ASPS approach (Automated Sensor and Signal Processing Selection) method for signal processing and sensor selection [1]. The sensory signals were processed using different signal processing methods to create a wide range of Sensory Characteristic Features (SCFs). The sensitivity of these SCFs to tool wear is investigated. The results indicate that the sensor fusion system is capable of detecting machining faults in comparison to a single sensor using the suggested approach.
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.
