The design of tool status intelligent monitoring system for CNC machine

Zezheng Qin; Xiaodong Wang; Jiangang Li

doi:10.1109/ICInfA.2018.8812465

The design of tool status intelligent monitoring system for CNC machine

Zezheng Qin
, Xiaodong Wang
, Jiangang Li

Harbin Institute of Technology Shenzhen

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

2 Scopus citations

Abstract

Based on the background of more and more repeated machining of single workpieces, this paper proposes an intelligent tool condition monitoring system. The vibration acceleration and current signals of the main shaft are collected, and the signal data is analyzed by wavelet packet decomposition and reconstruction and time domain and frequency domain to obtain characteristic values related to the tool state. The feature values extracted from the signal are then processed using normalization and PCA to obtain low-dimensional samples for representing the overall feature information for subsequent determination. Finally, C-SVM pattern recognition is used to implement tool condition monitoring.

Original language	English
Title of host publication	2018 IEEE International Conference on Information and Automation, ICIA 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	19-25
Number of pages	7
ISBN (Electronic)	9781538680698
DOIs	https://doi.org/10.1109/ICInfA.2018.8812465
State	Published - Aug 2018
Externally published	Yes
Event	2018 IEEE International Conference on Information and Automation, ICIA 2018 - Wuyishan, Fujian, China Duration: 11 Aug 2018 → 13 Aug 2018

Publication series

Name	2018 IEEE International Conference on Information and Automation, ICIA 2018

Conference

Conference	2018 IEEE International Conference on Information and Automation, ICIA 2018
Country/Territory	China
City	Wuyishan, Fujian
Period	11/08/18 → 13/08/18

Keywords

Multi-information fusion
SVM
Tool wear
Wavelet packet decomposition and reconstruction

Access to Document

10.1109/ICInfA.2018.8812465

Cite this

Qin, Z., Wang, X., & Li, J. (2018). The design of tool status intelligent monitoring system for CNC machine. In 2018 IEEE International Conference on Information and Automation, ICIA 2018 (pp. 19-25). Article 8812465 (2018 IEEE International Conference on Information and Automation, ICIA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICInfA.2018.8812465

@inproceedings{24e076899bca46eeb4636da8db34a889,

title = "The design of tool status intelligent monitoring system for CNC machine",

abstract = "Based on the background of more and more repeated machining of single workpieces, this paper proposes an intelligent tool condition monitoring system. The vibration acceleration and current signals of the main shaft are collected, and the signal data is analyzed by wavelet packet decomposition and reconstruction and time domain and frequency domain to obtain characteristic values related to the tool state. The feature values extracted from the signal are then processed using normalization and PCA to obtain low-dimensional samples for representing the overall feature information for subsequent determination. Finally, C-SVM pattern recognition is used to implement tool condition monitoring.",

keywords = "Multi-information fusion, SVM, Tool wear, Wavelet packet decomposition and reconstruction",

author = "Zezheng Qin and Xiaodong Wang and Jiangang Li",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Information and Automation, ICIA 2018 ; Conference date: 11-08-2018 Through 13-08-2018",

year = "2018",

month = aug,

doi = "10.1109/ICInfA.2018.8812465",

language = "英语",

series = "2018 IEEE International Conference on Information and Automation, ICIA 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "19--25",

booktitle = "2018 IEEE International Conference on Information and Automation, ICIA 2018",

address = "美国",

}

Qin, Z, Wang, X & Li, J 2018, The design of tool status intelligent monitoring system for CNC machine. in 2018 IEEE International Conference on Information and Automation, ICIA 2018., 8812465, 2018 IEEE International Conference on Information and Automation, ICIA 2018, Institute of Electrical and Electronics Engineers Inc., pp. 19-25, 2018 IEEE International Conference on Information and Automation, ICIA 2018, Wuyishan, Fujian, China, 11/08/18. https://doi.org/10.1109/ICInfA.2018.8812465

The design of tool status intelligent monitoring system for CNC machine. / Qin, Zezheng; Wang, Xiaodong; Li, Jiangang.
2018 IEEE International Conference on Information and Automation, ICIA 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 19-25 8812465 (2018 IEEE International Conference on Information and Automation, ICIA 2018).

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

TY - GEN

T1 - The design of tool status intelligent monitoring system for CNC machine

AU - Qin, Zezheng

AU - Wang, Xiaodong

AU - Li, Jiangang

PY - 2018/8

Y1 - 2018/8

N2 - Based on the background of more and more repeated machining of single workpieces, this paper proposes an intelligent tool condition monitoring system. The vibration acceleration and current signals of the main shaft are collected, and the signal data is analyzed by wavelet packet decomposition and reconstruction and time domain and frequency domain to obtain characteristic values related to the tool state. The feature values extracted from the signal are then processed using normalization and PCA to obtain low-dimensional samples for representing the overall feature information for subsequent determination. Finally, C-SVM pattern recognition is used to implement tool condition monitoring.

AB - Based on the background of more and more repeated machining of single workpieces, this paper proposes an intelligent tool condition monitoring system. The vibration acceleration and current signals of the main shaft are collected, and the signal data is analyzed by wavelet packet decomposition and reconstruction and time domain and frequency domain to obtain characteristic values related to the tool state. The feature values extracted from the signal are then processed using normalization and PCA to obtain low-dimensional samples for representing the overall feature information for subsequent determination. Finally, C-SVM pattern recognition is used to implement tool condition monitoring.

KW - Multi-information fusion

KW - SVM

KW - Tool wear

KW - Wavelet packet decomposition and reconstruction

UR - https://www.scopus.com/pages/publications/85072307482

U2 - 10.1109/ICInfA.2018.8812465

DO - 10.1109/ICInfA.2018.8812465

M3 - 会议稿件

AN - SCOPUS:85072307482

T3 - 2018 IEEE International Conference on Information and Automation, ICIA 2018

SP - 19

EP - 25

BT - 2018 IEEE International Conference on Information and Automation, ICIA 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Information and Automation, ICIA 2018

Y2 - 11 August 2018 through 13 August 2018

ER -

The design of tool status intelligent monitoring system for CNC machine

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this