Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method

Chun Li, Bing Li, Hongjun Wang, Dawei Shi, Fengshou Gu, Andrew D. Ball

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

A three-axis vibration wireless on-rotor sensing (ORS) was developed for milling tool wear condition monitoring. It is designed with a novel inclination fixture allowing vibration in different directions: axial, transverse and rotation to be perceived by one sensor. The sensing system could be mounted on the cutter arbor and rotate in sync with the spindle. Due to this innovative design and mounting position, the vibration signal obtained by this ORS showed a higher SNR than that of commercial accelerometers fixed on the spindle holder and the workpiece. After analyses of the waveform in the time domain, six different characteristics viz. Absolute Mean, Root Mean Square, Standard Variance, Pulse Factor, Skewness, and Kurtosis were calculated and extracted for tool wear stages recognition. The comparison results proved that the vibration signal acquired from the ORS was more effective and sensitive. Finally, different tool wear progression stages could be best monitored and recognized by the sensor output of the combination between axial and transverse responses.

Original languageEnglish
Title of host publicationProceedings of TEPEN 2022
Subtitle of host publicationEfficiency and Performance Engineering Network
EditorsHao Zhang, Yongjian Ji, Tongtong Liu, Xiuquan Sun, Andrew David Ball
PublisherSpringer, Cham
Pages268-281
Number of pages14
Volume129
ISBN (Electronic)9783031261930
ISBN (Print)9783031261923, 9783031261954
DOIs
Publication statusPublished - 4 Mar 2023
EventInternational Conference of The Efficiency and Performance Engineering Network 2022 - Baotou, China
Duration: 18 Aug 202221 Aug 2022
https://tepen.net/
https://tepen.net/conference/tepen2022/

Publication series

NameMechanisms and Machine Science
PublisherSpringer
Volume129 MMS
ISSN (Print)2211-0984
ISSN (Electronic)2211-0992

Conference

ConferenceInternational Conference of The Efficiency and Performance Engineering Network 2022
Abbreviated titleTEPEN 2022
Country/TerritoryChina
CityBaotou
Period18/08/2221/08/22
Internet address

Fingerprint

Dive into the research topics of 'Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method'. Together they form a unique fingerprint.

Cite this