Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing

Chun Li; Dawei Shi; Bing Li; Hongjun Wang; Guojin Feng; Fengshou Gu; Andrew D. Ball

doi:10.1007/978-3-030-99075-6_84

Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing

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

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

2 Citations (Scopus)

Abstract

Cutting tool plays an important role in modern manufacturing industry, however, tool wear is unavoidable during machining which could reduce the efficiency. Aiming at studying an appropriate and efficient tool condition monitoring method to improve the accuracy of finished parts, the roughness of the turned surface, a novel On-Rotor Sensing (ORS) is installed on the rotating workpiece to obtain vibration signals. To get an in-depth understand of the vibration data, a multi-degree-of-freedom (MDOF) system consisted of spindle, chuck and workpiece is established and its multi-mode natural frequency is obtained by finite element model (FEM) method. It is found that the dynamic response of the spindle rotor determines machining accuracy in the turning process and shows that the first several modes in the frequency range within 2000 Hz are the main responses of the system, which can be effectively captured by the ORS. Especially, the spring stiffness is calibrated based on the FEM results and the accuracy of the dynamic modal responses of this model are verified when the mass of the workpiece decreases during the turning process. According to the results, two frequency bands are advocated for ORS based online monitoring of tool wear conditions.

Original language	English
Title of host publication	Proceedings of IncoME-VI and TEPEN 2021
Subtitle of host publication	Performance Engineering and Maintenance Engineering
Editors	Hao Zhang, Guojin Feng, Hongjun Wang, Fengshou Gu, Jyoti K. Sinha
Publisher	Springer, Cham
Pages	1057-1071
Number of pages	15
Volume	117
ISBN (Electronic)	9783030990756
ISBN (Print)	9783030990749
DOIs	https://doi.org/10.1007/978-3-030-99075-6_84
Publication status	Published - 18 Sep 2022
Event	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021 - Hebei University of Technology, Tianjin, China Duration: 20 Oct 2021 → 23 Oct 2021 Conference number: 6 https://tepen.net/conference/tepen2021/

Publication series

Name	Mechanisms and Machine Science
Publisher	Springer
Volume	117
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021
Abbreviated title	TEPEN-2021 and IncoME-VI
Country/Territory	China
City	Tianjin
Period	20/10/21 → 23/10/21
Internet address	https://tepen.net/conference/tepen2021/

UN SDGs

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

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Cite this

Li, C., Shi, D., Li, B., Wang, H., Feng, G., Gu, F., & Ball, A. D. (2022). Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing. In H. Zhang, G. Feng, H. Wang, F. Gu, & J. K. Sinha (Eds.), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering (Vol. 117, pp. 1057-1071). (Mechanisms and Machine Science; Vol. 117). Springer, Cham. https://doi.org/10.1007/978-3-030-99075-6_84

Li, Chun ; Shi, Dawei ; Li, Bing et al. / Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing. Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. editor / Hao Zhang ; Guojin Feng ; Hongjun Wang ; Fengshou Gu ; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. pp. 1057-1071 (Mechanisms and Machine Science).

@inproceedings{efd1d9c13dd548fe805e46b12cfdcae4,

title = "Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing",

abstract = "Cutting tool plays an important role in modern manufacturing industry, however, tool wear is unavoidable during machining which could reduce the efficiency. Aiming at studying an appropriate and efficient tool condition monitoring method to improve the accuracy of finished parts, the roughness of the turned surface, a novel On-Rotor Sensing (ORS) is installed on the rotating workpiece to obtain vibration signals. To get an in-depth understand of the vibration data, a multi-degree-of-freedom (MDOF) system consisted of spindle, chuck and workpiece is established and its multi-mode natural frequency is obtained by finite element model (FEM) method. It is found that the dynamic response of the spindle rotor determines machining accuracy in the turning process and shows that the first several modes in the frequency range within 2000 Hz are the main responses of the system, which can be effectively captured by the ORS. Especially, the spring stiffness is calibrated based on the FEM results and the accuracy of the dynamic modal responses of this model are verified when the mass of the workpiece decreases during the turning process. According to the results, two frequency bands are advocated for ORS based online monitoring of tool wear conditions.",

keywords = "Dynamic characteristics of spindle, Finite element model, On-Rotor Sensing, Tool wear condition, Vibration",

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

note = "Funding Information: Acknowledgements This work is supported by the 2020 Guangdong Province Scientific Research Platform (No. 2020KTSCX188), the Beijing Municipal Science and Technology Project (No. Z201100008320004) and the National Natural Science Foundation of China (No. 2018A030313418). Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, TEPEN-2021 and IncoME-VI ; Conference date: 20-10-2021 Through 23-10-2021",

year = "2022",

month = sep,

day = "18",

doi = "10.1007/978-3-030-99075-6_84",

language = "English",

isbn = "9783030990749",

volume = "117",

series = "Mechanisms and Machine Science",

publisher = "Springer, Cham",

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editor = "Hao Zhang and Guojin Feng and Hongjun Wang and Fengshou Gu and Sinha, {Jyoti K.}",

booktitle = "Proceedings of IncoME-VI and TEPEN 2021",

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Li, C, Shi, D, Li, B, Wang, H, Feng, G, Gu, F & Ball, AD 2022, Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing. in H Zhang, G Feng, H Wang, F Gu & JK Sinha (eds), Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. vol. 117, Mechanisms and Machine Science, vol. 117, Springer, Cham, pp. 1057-1071, 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021, Tianjin, China, 20/10/21. https://doi.org/10.1007/978-3-030-99075-6_84

Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing. / Li, Chun; Shi, Dawei; Li, Bing et al.

Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. ed. / Hao Zhang; Guojin Feng; Hongjun Wang; Fengshou Gu; Jyoti K. Sinha. Vol. 117 Springer, Cham, 2022. p. 1057-1071 (Mechanisms and Machine Science; Vol. 117).

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

TY - GEN

T1 - Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing

AU - Li, Chun

AU - Shi, Dawei

AU - Li, Bing

AU - Wang, Hongjun

AU - Feng, Guojin

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Conference code: 6

PY - 2022/9/18

Y1 - 2022/9/18

N2 - Cutting tool plays an important role in modern manufacturing industry, however, tool wear is unavoidable during machining which could reduce the efficiency. Aiming at studying an appropriate and efficient tool condition monitoring method to improve the accuracy of finished parts, the roughness of the turned surface, a novel On-Rotor Sensing (ORS) is installed on the rotating workpiece to obtain vibration signals. To get an in-depth understand of the vibration data, a multi-degree-of-freedom (MDOF) system consisted of spindle, chuck and workpiece is established and its multi-mode natural frequency is obtained by finite element model (FEM) method. It is found that the dynamic response of the spindle rotor determines machining accuracy in the turning process and shows that the first several modes in the frequency range within 2000 Hz are the main responses of the system, which can be effectively captured by the ORS. Especially, the spring stiffness is calibrated based on the FEM results and the accuracy of the dynamic modal responses of this model are verified when the mass of the workpiece decreases during the turning process. According to the results, two frequency bands are advocated for ORS based online monitoring of tool wear conditions.

AB - Cutting tool plays an important role in modern manufacturing industry, however, tool wear is unavoidable during machining which could reduce the efficiency. Aiming at studying an appropriate and efficient tool condition monitoring method to improve the accuracy of finished parts, the roughness of the turned surface, a novel On-Rotor Sensing (ORS) is installed on the rotating workpiece to obtain vibration signals. To get an in-depth understand of the vibration data, a multi-degree-of-freedom (MDOF) system consisted of spindle, chuck and workpiece is established and its multi-mode natural frequency is obtained by finite element model (FEM) method. It is found that the dynamic response of the spindle rotor determines machining accuracy in the turning process and shows that the first several modes in the frequency range within 2000 Hz are the main responses of the system, which can be effectively captured by the ORS. Especially, the spring stiffness is calibrated based on the FEM results and the accuracy of the dynamic modal responses of this model are verified when the mass of the workpiece decreases during the turning process. According to the results, two frequency bands are advocated for ORS based online monitoring of tool wear conditions.

KW - Dynamic characteristics of spindle

KW - Finite element model

KW - On-Rotor Sensing

KW - Tool wear condition

KW - Vibration

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SN - 9783030990749

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BT - Proceedings of IncoME-VI and TEPEN 2021

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PB - Springer, Cham

T2 - 6th International Conference on Maintenance Engineering, IncoME-VI and the Conference of the Efficiency and Performance Engineering Network, TEPEN 2021

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Li C, Shi D, Li B, Wang H, Feng G, Gu F et al. Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing. In Zhang H, Feng G, Wang H, Gu F, Sinha JK, editors, Proceedings of IncoME-VI and TEPEN 2021 : Performance Engineering and Maintenance Engineering. Vol. 117. Springer, Cham. 2022. p. 1057-1071. (Mechanisms and Machine Science). Epub 2022 Sep 18. doi: 10.1007/978-3-030-99075-6_84

Modelling the Dynamics of a CNC Spindle for Tool Condition Identification Based on On-Rotor Sensing

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