Development of signal transmission and reduction modules for status monitoring and prediction of machine tools

I. Chun Sun, Kuo-Shen Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine tools such as computer numerical control milling machines play key roles in modern manufacturing industry. The quality of machined products are largely depended on the status of machines in various aspects. As a result, appropriate condition monitoring would be essential for both quality control and the safety and life assessment of machine tools especially for the current Industry 4.0 era. In this work, the effort for developing a low cost customized wireless data transmission module and the associated data processing for extracting signatures and tendencies in both time and frequency domains for status monitoring and longevity evaluation are presented. The system is realized via Arduino, Bluetooth, and LabView. Finally, the developed system is used on a five-axis CNC miller for examining the feasibility of the developed system by monitoring the operating status of the machine. In the future, with more data collected, it is expected that more sophisticated models would be developed for better describing and predicting the machine status for enhancing the manufacturing reliability.

Original languageEnglish
Title of host publication2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages711-716
Number of pages6
ISBN (Electronic)9784907764579
DOIs
Publication statusPublished - 2017 Nov 10
Event56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017 - Kanazawa, Japan
Duration: 2017 Sep 192017 Sep 22

Publication series

Name2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017
Volume2017-November

Other

Other56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017
CountryJapan
CityKanazawa
Period17-09-1917-09-22

Fingerprint

machine tools
signal transmission
Machine Tool
Machine tools
manufacturing
modules
industries
milling machines
Monitoring
numerical control
Module
Milling machines
Prediction
Bluetooth
Condition monitoring
data transmission
quality control
predictions
Data communication systems
Quality control

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Control and Optimization
  • Control and Systems Engineering
  • Instrumentation

Cite this

Sun, I. C., & Chen, K-S. (2017). Development of signal transmission and reduction modules for status monitoring and prediction of machine tools. In 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017 (pp. 711-716). (2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/SICE.2017.8105459
Sun, I. Chun ; Chen, Kuo-Shen. / Development of signal transmission and reduction modules for status monitoring and prediction of machine tools. 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 711-716 (2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017).
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abstract = "Machine tools such as computer numerical control milling machines play key roles in modern manufacturing industry. The quality of machined products are largely depended on the status of machines in various aspects. As a result, appropriate condition monitoring would be essential for both quality control and the safety and life assessment of machine tools especially for the current Industry 4.0 era. In this work, the effort for developing a low cost customized wireless data transmission module and the associated data processing for extracting signatures and tendencies in both time and frequency domains for status monitoring and longevity evaluation are presented. The system is realized via Arduino, Bluetooth, and LabView. Finally, the developed system is used on a five-axis CNC miller for examining the feasibility of the developed system by monitoring the operating status of the machine. In the future, with more data collected, it is expected that more sophisticated models would be developed for better describing and predicting the machine status for enhancing the manufacturing reliability.",
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Sun, IC & Chen, K-S 2017, Development of signal transmission and reduction modules for status monitoring and prediction of machine tools. in 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017. 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 711-716, 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017, Kanazawa, Japan, 17-09-19. https://doi.org/10.23919/SICE.2017.8105459

Development of signal transmission and reduction modules for status monitoring and prediction of machine tools. / Sun, I. Chun; Chen, Kuo-Shen.

2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 711-716 (2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017; Vol. 2017-November).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Sun IC, Chen K-S. Development of signal transmission and reduction modules for status monitoring and prediction of machine tools. In 2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 711-716. (2017 56th Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2017). https://doi.org/10.23919/SICE.2017.8105459