Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor

Chun Li; Bing Li; Lichang Gu; Guojin Feng; Fengshou Gu; Andrew D. Ball

doi:10.1109/WCMEIM52463.2020.00091

Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor

Chun Li, Bing Li, Lichang Gu, Guojin Feng, Fengshou Gu, Andrew D. Ball

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

10 Citations (Scopus)

Abstract

To realize online monitoring of workpieces in the machining processes with universal lathes, this paper presents a novel vibration method based on a new On-Rotor Sensor (ORS) technology. The dynamic responses of a turning process were firstly examined by finite element analysis (FEA) and impact test analysis to gain a basic understanding of the vibration characteristics measured by ORS when the workpiece is under stationary states with both fixed-pinned and fixed-free boundaries. Experimental evaluations were then conducted under different depths of cut (DOC) for workpieces turning under both the pinned and free conditions. It has been shown that vibration responses are predominated by a low-frequency mode around 500 Hz and a high mode around 1200Hz, which corresponds to the first two lateral modes of the workpiece. The root mean squared (RMS) values filtered in these two bands allow turning processes with different DOCs and workpiece diameters to be differentiated. Besides, unstable turnings can also be detected by the standard deviation of RMS. These results show the proposed method is very promising for online real-time assessment of manufacturing quality, paving fundamentals for intelligent manufacturing.

Original language	English
Title of host publication	Proceedings - 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	402-407
Number of pages	6
ISBN (Electronic)	9781665441094
ISBN (Print)	9781665431125
DOIs	https://doi.org/10.1109/WCMEIM52463.2020.00091
Publication status	Published - 4 Dec 2020
Event	3rd World Conference on Mechanical Engineering and Intelligent Manufacturing - Virtual, Shanghai, China Duration: 4 Dec 2020 → 6 Dec 2020 Conference number: 3

Conference

Conference	3rd World Conference on Mechanical Engineering and Intelligent Manufacturing
Abbreviated title	WCMEIM 2020
Country/Territory	China
City	Virtual, Shanghai
Period	4/12/20 → 6/12/20

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

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Li, C., Li, B., Gu, L., Feng, G., Gu, F., & Ball, A. D. (2020). Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor. In Proceedings - 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2020 (pp. 402-407). [9409391] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCMEIM52463.2020.00091

@inproceedings{f01ccce166224f15933fc2ded36d16d3,

title = "Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor",

abstract = "To realize online monitoring of workpieces in the machining processes with universal lathes, this paper presents a novel vibration method based on a new On-Rotor Sensor (ORS) technology. The dynamic responses of a turning process were firstly examined by finite element analysis (FEA) and impact test analysis to gain a basic understanding of the vibration characteristics measured by ORS when the workpiece is under stationary states with both fixed-pinned and fixed-free boundaries. Experimental evaluations were then conducted under different depths of cut (DOC) for workpieces turning under both the pinned and free conditions. It has been shown that vibration responses are predominated by a low-frequency mode around 500 Hz and a high mode around 1200Hz, which corresponds to the first two lateral modes of the workpiece. The root mean squared (RMS) values filtered in these two bands allow turning processes with different DOCs and workpiece diameters to be differentiated. Besides, unstable turnings can also be detected by the standard deviation of RMS. These results show the proposed method is very promising for online real-time assessment of manufacturing quality, paving fundamentals for intelligent manufacturing.",

keywords = "Finite Element Analysis, On-rotor sensing, Online process monitoring, Turning process, Vibration-based monitoring",

author = "Chun Li and Bing Li and Lichang Gu and Guojin Feng and Fengshou Gu and Ball, {Andrew D.}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2020 ; Conference date: 04-12-2020 Through 06-12-2020",

year = "2020",

month = dec,

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doi = "10.1109/WCMEIM52463.2020.00091",

language = "English",

isbn = "9781665431125",

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Li, C, Li, B, Gu, L, Feng, G, Gu, F & Ball, AD 2020, Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor. in Proceedings - 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2020., 9409391, Institute of Electrical and Electronics Engineers Inc., pp. 402-407, 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, Virtual, Shanghai, China, 4/12/20. https://doi.org/10.1109/WCMEIM52463.2020.00091

Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor. / Li, Chun; Li, Bing; Gu, Lichang et al.

Proceedings - 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 402-407 9409391.

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

TY - GEN

T1 - Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor

AU - Li, Chun

AU - Li, Bing

AU - Gu, Lichang

AU - Feng, Guojin

AU - Gu, Fengshou

AU - Ball, Andrew D.

N1 - Conference code: 3

PY - 2020/12/4

Y1 - 2020/12/4

N2 - To realize online monitoring of workpieces in the machining processes with universal lathes, this paper presents a novel vibration method based on a new On-Rotor Sensor (ORS) technology. The dynamic responses of a turning process were firstly examined by finite element analysis (FEA) and impact test analysis to gain a basic understanding of the vibration characteristics measured by ORS when the workpiece is under stationary states with both fixed-pinned and fixed-free boundaries. Experimental evaluations were then conducted under different depths of cut (DOC) for workpieces turning under both the pinned and free conditions. It has been shown that vibration responses are predominated by a low-frequency mode around 500 Hz and a high mode around 1200Hz, which corresponds to the first two lateral modes of the workpiece. The root mean squared (RMS) values filtered in these two bands allow turning processes with different DOCs and workpiece diameters to be differentiated. Besides, unstable turnings can also be detected by the standard deviation of RMS. These results show the proposed method is very promising for online real-time assessment of manufacturing quality, paving fundamentals for intelligent manufacturing.

AB - To realize online monitoring of workpieces in the machining processes with universal lathes, this paper presents a novel vibration method based on a new On-Rotor Sensor (ORS) technology. The dynamic responses of a turning process were firstly examined by finite element analysis (FEA) and impact test analysis to gain a basic understanding of the vibration characteristics measured by ORS when the workpiece is under stationary states with both fixed-pinned and fixed-free boundaries. Experimental evaluations were then conducted under different depths of cut (DOC) for workpieces turning under both the pinned and free conditions. It has been shown that vibration responses are predominated by a low-frequency mode around 500 Hz and a high mode around 1200Hz, which corresponds to the first two lateral modes of the workpiece. The root mean squared (RMS) values filtered in these two bands allow turning processes with different DOCs and workpiece diameters to be differentiated. Besides, unstable turnings can also be detected by the standard deviation of RMS. These results show the proposed method is very promising for online real-time assessment of manufacturing quality, paving fundamentals for intelligent manufacturing.

KW - Finite Element Analysis

KW - On-rotor sensing

KW - Online process monitoring

KW - Turning process

KW - Vibration-based monitoring

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DO - 10.1109/WCMEIM52463.2020.00091

M3 - Conference contribution

AN - SCOPUS:85105365537

SN - 9781665431125

SP - 402

EP - 407

BT - Proceedings - 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing, WCMEIM 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing

Y2 - 4 December 2020 through 6 December 2020

ER -

Online Monitoring of a Shaft Turning Process based on Vibration Signals from On-Rotor Sensor

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