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

doi:10.1007/978-3-031-26193-0_23

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 proceeding › Conference contribution › peer-review

2 Citations (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 language	English
Title of host publication	Proceedings of TEPEN 2022
Subtitle of host publication	Efficiency and Performance Engineering Network
Editors	Hao Zhang, Yongjian Ji, Tongtong Liu, Xiuquan Sun, Andrew David Ball
Publisher	Springer, Cham
Pages	268-281
Number of pages	14
Volume	129
ISBN (Electronic)	9783031261930
ISBN (Print)	9783031261923, 9783031261954
DOIs	https://doi.org/10.1007/978-3-031-26193-0_23
Publication status	Published - 4 Mar 2023
Event	International Conference of The Efficiency and Performance Engineering Network 2022 - Baotou, China Duration: 18 Aug 2022 → 21 Aug 2022 https://tepen.net/ https://tepen.net/conference/tepen2022/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	129 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	International Conference of The Efficiency and Performance Engineering Network 2022
Abbreviated title	TEPEN 2022
Country/Territory	China
City	Baotou
Period	18/08/22 → 21/08/22
Internet address	https://tepen.net/ https://tepen.net/conference/tepen2022/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-031-26193-0_23Licence: Unspecified

Cite this

Li, C., Li, B., Wang, H., Shi, D., Gu, F., & Ball, A. D. (2023). Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method. In H. Zhang, Y. Ji, T. Liu, X. Sun, & A. D. Ball (Eds.), Proceedings of TEPEN 2022 : Efficiency and Performance Engineering Network (Vol. 129, pp. 268-281). (Mechanisms and Machine Science; Vol. 129 MMS). Springer, Cham. https://doi.org/10.1007/978-3-031-26193-0_23

Li, Chun ; Li, Bing ; Wang, Hongjun et al. / Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method. Proceedings of TEPEN 2022 : Efficiency and Performance Engineering Network. editor / Hao Zhang ; Yongjian Ji ; Tongtong Liu ; Xiuquan Sun ; Andrew David Ball. Vol. 129 Springer, Cham, 2023. pp. 268-281 (Mechanisms and Machine Science).

@inproceedings{a27f067ab9d64699b4d6e146973187e4,

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

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.",

keywords = "CNC milling, On-rotor sensing, Tool condition monitoring, Tool wear, Vibration signal",

author = "Chun Li and Bing Li and Hongjun Wang and Dawei Shi and Fengshou Gu and Ball, {Andrew D.}",

note = "Funding Information: Acknowledgment. This work was supported by the Special Projects in Key Areas in Fundamental and Foundational Applied Research of Guangdong Provincial Education Department (No. 2021ZDZX1072), Fundamental and Foundational Applied Research Projects, Zhuhai (No. ZH22017003200019PWC), Open Fund Project of Key Laboratory of Modern Measurement and Control Technology, Ministry of Education (No. KF20211123205). Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference of The Efficiency and Performance Engineering Network 2022, TEPEN 2022 ; Conference date: 18-08-2022 Through 21-08-2022",

year = "2023",

month = mar,

day = "4",

doi = "10.1007/978-3-031-26193-0_23",

language = "English",

isbn = "9783031261923",

volume = "129",

series = "Mechanisms and Machine Science",

publisher = "Springer, Cham",

pages = "268--281",

editor = "Hao Zhang and Yongjian Ji and Tongtong Liu and Xiuquan Sun and Ball, {Andrew David}",

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Li, C, Li, B, Wang, H, Shi, D, Gu, F & Ball, AD 2023, Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method. in H Zhang, Y Ji, T Liu, X Sun & AD Ball (eds), Proceedings of TEPEN 2022 : Efficiency and Performance Engineering Network. vol. 129, Mechanisms and Machine Science, vol. 129 MMS, Springer, Cham, pp. 268-281, International Conference of The Efficiency and Performance Engineering Network 2022, Baotou, China, 18/08/22. https://doi.org/10.1007/978-3-031-26193-0_23

Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method. / Li, Chun; Li, Bing; Wang, Hongjun et al.

Proceedings of TEPEN 2022 : Efficiency and Performance Engineering Network. ed. / Hao Zhang; Yongjian Ji; Tongtong Liu; Xiuquan Sun; Andrew David Ball. Vol. 129 Springer, Cham, 2023. p. 268-281 (Mechanisms and Machine Science; Vol. 129 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method

AU - Li, Chun

AU - Li, Bing

AU - Wang, Hongjun

AU - Shi, Dawei

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Funding Information: Acknowledgment. This work was supported by the Special Projects in Key Areas in Fundamental and Foundational Applied Research of Guangdong Provincial Education Department (No. 2021ZDZX1072), Fundamental and Foundational Applied Research Projects, Zhuhai (No. ZH22017003200019PWC), Open Fund Project of Key Laboratory of Modern Measurement and Control Technology, Ministry of Education (No. KF20211123205). Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2023/3/4

Y1 - 2023/3/4

N2 - 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.

AB - 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.

KW - CNC milling

KW - On-rotor sensing

KW - Tool condition monitoring

KW - Tool wear

KW - Vibration signal

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UR - https://link.springer.com/book/10.1007/978-3-031-26193-0

U2 - 10.1007/978-3-031-26193-0_23

DO - 10.1007/978-3-031-26193-0_23

M3 - Conference contribution

AN - SCOPUS:85151130604

SN - 9783031261923

SN - 9783031261954

VL - 129

T3 - Mechanisms and Machine Science

SP - 268

EP - 281

BT - Proceedings of TEPEN 2022

A2 - Zhang, Hao

A2 - Ji, Yongjian

A2 - Liu, Tongtong

A2 - Sun, Xiuquan

A2 - Ball, Andrew David

PB - Springer, Cham

T2 - International Conference of The Efficiency and Performance Engineering Network 2022

Y2 - 18 August 2022 through 21 August 2022

ER -

Li C, Li B, Wang H, Shi D, Gu F , Ball AD. Tool Wear Monitoring in CNC Milling Process Based on Vibration Signals from an On-Rotor Sensing Method. In Zhang H, Ji Y, Liu T, Sun X, Ball AD, editors, Proceedings of TEPEN 2022 : Efficiency and Performance Engineering Network. Vol. 129. Springer, Cham. 2023. p. 268-281. (Mechanisms and Machine Science). Epub 2023 Mar 3. doi: 10.1007/978-3-031-26193-0_23

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

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