Identification of feature set for effective tool condition monitoring - a case study in titanium machining

Jie Sun; Wong Yoke San; Hong Geok Soon; Mustafizur Rahman; Wang Zhigang

doi:10.1109/COASE.2008.4626410

Identification of feature set for effective tool condition monitoring - a case study in titanium machining

Jie Sun^*, Wong Yoke San, Hong Geok Soon, Mustafizur Rahman, Wang Zhigang

^*Corresponding author for this work

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

5 Citations (Scopus)

Abstract

Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximize machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analyzed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition.

Original language	English
Title of host publication	4th IEEE Conference on Automation Science and Engineering, CASE 2008
Pages	273-278
Number of pages	6
DOIs	https://doi.org/10.1109/COASE.2008.4626410
Publication status	Published - 2008
Externally published	Yes
Event	4th IEEE Conference on Automation Science and Engineering, CASE 2008 - Washington, DC, United States Duration: 23 Aug 2008 → 26 Aug 2008

Publication series

Name	4th IEEE Conference on Automation Science and Engineering, CASE 2008

Conference

Conference	4th IEEE Conference on Automation Science and Engineering, CASE 2008
Country/Territory	United States
City	Washington, DC
Period	23/08/08 → 26/08/08

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10.1109/COASE.2008.4626410

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Sun, J., San, W. Y., Soon, H. G., Rahman, M., & Zhigang, W. (2008). Identification of feature set for effective tool condition monitoring - a case study in titanium machining. In 4th IEEE Conference on Automation Science and Engineering, CASE 2008 (pp. 273-278). Article 4626410 (4th IEEE Conference on Automation Science and Engineering, CASE 2008). https://doi.org/10.1109/COASE.2008.4626410

@inproceedings{7f6a324a1b9a4540a06222ad2ea47c1c,

title = "Identification of feature set for effective tool condition monitoring - a case study in titanium machining",

abstract = "Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximize machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analyzed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition.",

author = "Jie Sun and San, {Wong Yoke} and Soon, {Hong Geok} and Mustafizur Rahman and Wang Zhigang",

year = "2008",

doi = "10.1109/COASE.2008.4626410",

language = "English",

isbn = "9781424420230",

series = "4th IEEE Conference on Automation Science and Engineering, CASE 2008",

pages = "273--278",

booktitle = "4th IEEE Conference on Automation Science and Engineering, CASE 2008",

note = "4th IEEE Conference on Automation Science and Engineering, CASE 2008 ; Conference date: 23-08-2008 Through 26-08-2008",

}

Sun, J, San, WY, Soon, HG, Rahman, M & Zhigang, W 2008, Identification of feature set for effective tool condition monitoring - a case study in titanium machining. in 4th IEEE Conference on Automation Science and Engineering, CASE 2008., 4626410, 4th IEEE Conference on Automation Science and Engineering, CASE 2008, pp. 273-278, 4th IEEE Conference on Automation Science and Engineering, CASE 2008, Washington, DC, United States, 23/08/08. https://doi.org/10.1109/COASE.2008.4626410

Identification of feature set for effective tool condition monitoring - a case study in titanium machining. / Sun, Jie; San, Wong Yoke; Soon, Hong Geok et al.
4th IEEE Conference on Automation Science and Engineering, CASE 2008. 2008. p. 273-278 4626410 (4th IEEE Conference on Automation Science and Engineering, CASE 2008).

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

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T1 - Identification of feature set for effective tool condition monitoring - a case study in titanium machining

AU - Sun, Jie

AU - San, Wong Yoke

AU - Soon, Hong Geok

AU - Rahman, Mustafizur

AU - Zhigang, Wang

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N2 - Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximize machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analyzed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition.

AB - Due to the rapid wear of the cutting tools when machining titanium alloy, tool condition monitoring (TCM) is most useful to avoid workpiece damage and maximize machining productivity. This paper uses sensor signals and feature analysis to identify a feature set for effective TCM. Firstly, basic requirements of sensor signals in tool condition identification are discussed, and the suitability of two candidate signals (acoustic emission and cutting force) commonly employed for machining monitoring are critically analyzed. Their effectiveness in TCM is investigated based on extracted features of these signals, singly or in combination. Experimental results based on titanium machining, which is an expensive process with high tool wear, indicate that this proposed method is capable to determine a suitable sensing method and an effective feature set to identify tool condition.

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SN - 9781424420230

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BT - 4th IEEE Conference on Automation Science and Engineering, CASE 2008

T2 - 4th IEEE Conference on Automation Science and Engineering, CASE 2008

Y2 - 23 August 2008 through 26 August 2008

ER -

Identification of feature set for effective tool condition monitoring - a case study in titanium machining

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