Total precision inspection of machine tools with virtual metrology

Hao Tieng, Haw Ching Yang, Fan Tien Cheng

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

Abstract

The purpose of this study is trying to apply VM [1][2] for measuring machining precision of machine tools. As shown in the left portion of Fig. 1, the AV M server [3] needs process data, which contain sensor data and machining parameters, as inputs for predicting machining accuracies. However, most machine tools do not possess sensors for providing raw process data such as vibration, current, etc. Furthermore, machining operations usually possess severe vibrations and loud noises. It makes the raw data collected from sensors attached to machine tools with low signal-to-noise (S/N) ratios, thereby affecting the prediction accuracy of VM. Even though the AVM has been an effective way to conduct workpiece measurements in high-tech industries [1], there exist challenges when applying the AVM to machining industry. Besides embedding essential sensors on the machine tool effectively, the challenges are 1) Segmentation: to accurately segment essential part of the raw process data from the original NC file, 2) Data Cleaning: to effectively handle raw process/sensor data with low S/N ratios, and 3) Feature Extraction: to properly extract significant features from the segmented raw process data. This work proposes a novel GED-plus-AVM (GAVM) system as depicted in Fig.1 to resolve all the challenges mentioned above. These challenges are judiciously addressed and successfully resolved in this paper.

Original languageEnglish
Title of host publication2015 IEEE Conference on Automation Science and Engineering
Subtitle of host publicationAutomation for a Sustainable Future, CASE 2015
PublisherIEEE Computer Society
Pages1446-1447
Number of pages2
ISBN (Electronic)9781467381833
DOIs
Publication statusPublished - 2015 Oct 7
Event11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden
Duration: 2015 Aug 242015 Aug 28

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2015-October
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other11th IEEE International Conference on Automation Science and Engineering, CASE 2015
CountrySweden
CityGothenburg
Period15-08-2415-08-28

Fingerprint

Machine tools
Machining
Inspection
Sensors
Signal to noise ratio
Feature extraction
Cleaning
Industry
Servers

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Tieng, H., Yang, H. C., & Cheng, F. T. (2015). Total precision inspection of machine tools with virtual metrology. In 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015 (pp. 1446-1447). [7294301] (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October). IEEE Computer Society. https://doi.org/10.1109/CoASE.2015.7294301
Tieng, Hao ; Yang, Haw Ching ; Cheng, Fan Tien. / Total precision inspection of machine tools with virtual metrology. 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society, 2015. pp. 1446-1447 (IEEE International Conference on Automation Science and Engineering).
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abstract = "The purpose of this study is trying to apply VM [1][2] for measuring machining precision of machine tools. As shown in the left portion of Fig. 1, the AV M server [3] needs process data, which contain sensor data and machining parameters, as inputs for predicting machining accuracies. However, most machine tools do not possess sensors for providing raw process data such as vibration, current, etc. Furthermore, machining operations usually possess severe vibrations and loud noises. It makes the raw data collected from sensors attached to machine tools with low signal-to-noise (S/N) ratios, thereby affecting the prediction accuracy of VM. Even though the AVM has been an effective way to conduct workpiece measurements in high-tech industries [1], there exist challenges when applying the AVM to machining industry. Besides embedding essential sensors on the machine tool effectively, the challenges are 1) Segmentation: to accurately segment essential part of the raw process data from the original NC file, 2) Data Cleaning: to effectively handle raw process/sensor data with low S/N ratios, and 3) Feature Extraction: to properly extract significant features from the segmented raw process data. This work proposes a novel GED-plus-AVM (GAVM) system as depicted in Fig.1 to resolve all the challenges mentioned above. These challenges are judiciously addressed and successfully resolved in this paper.",
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Tieng, H, Yang, HC & Cheng, FT 2015, Total precision inspection of machine tools with virtual metrology. in 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015., 7294301, IEEE International Conference on Automation Science and Engineering, vol. 2015-October, IEEE Computer Society, pp. 1446-1447, 11th IEEE International Conference on Automation Science and Engineering, CASE 2015, Gothenburg, Sweden, 15-08-24. https://doi.org/10.1109/CoASE.2015.7294301

Total precision inspection of machine tools with virtual metrology. / Tieng, Hao; Yang, Haw Ching; Cheng, Fan Tien.

2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society, 2015. p. 1446-1447 7294301 (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October).

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

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Tieng H, Yang HC, Cheng FT. Total precision inspection of machine tools with virtual metrology. In 2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society. 2015. p. 1446-1447. 7294301. (IEEE International Conference on Automation Science and Engineering). https://doi.org/10.1109/CoASE.2015.7294301