Abstract
In this present study, an experimental approach for detecting tool chatter in its early stages is proposed. Local mean decomposition based on cubic spline approach is invoked to analyse acquired machining sound data at varied cutting settings. The correlation coefficient and relative energy rule are used to identify the significant PFs. Furthermore, the characteristics of tool chatter are investigated by assessing six dimensional and six dimensionless indexes. Then, for every combination of characteristics, a threshold is defined to detect the aforementioned severity of chatter. At last, tool chatter online monitoring is carried out based on the obtained threshold.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.