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Estimation of tool wear based on adaptive sensor fusion of force and power in face milling.

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Title: Estimation of tool wear based on adaptive sensor fusion of force and power in face milling.
Authors: Bhattacharyya, P.1 (AUTHOR) prabir_r@isical.ac.in, Sengupta, D.1 (AUTHOR)
Source: International Journal of Production Research. Feb2009, Vol. 47 Issue 3, p817-833. 17p. 3 Diagrams, 3 Charts, 9 Graphs.
Subjects: Machine tool monitoring, Regression analysis, Milling machinery, Robust control, Machining, Mechanical wear
Abstract: Sensor fusion of multi-sensory measurements is believed to enhance accuracy of tool condition monitoring methods for sensor signals which are at least partly complementary. In this paper, a novel statistic based on the least squared error criteria is used to evaluate performances of models arising from sensor fusion. An adaptive sensor fusion of cutting force and electrical power signals, which performs optimally even in case of failure in any acquiring channel, is proposed. Combinations of signal processing techniques are used to extract accurate and robust features, which are refined by feature space filtering techniques to obtain improved and robust estimators of tool wear. It is shown that the model using sensor fusion of force and power produces superior results to those using single measurements. Error bounds of the estimates are also provided as prediction limits. Significant improvements are obtained compared to the existing methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Estimation of tool wear based on adaptive sensor fusion of force and power in face milling.
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  Data: <searchLink fieldCode="AR" term="%22Bhattacharyya%2C+P%2E%22">Bhattacharyya, P.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> prabir_r@isical.ac.in</i><br /><searchLink fieldCode="AR" term="%22Sengupta%2C+D%2E%22">Sengupta, D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Production+Research%22">International Journal of Production Research</searchLink>. Feb2009, Vol. 47 Issue 3, p817-833. 17p. 3 Diagrams, 3 Charts, 9 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Machine+tool+monitoring%22">Machine tool monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis%22">Regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Milling+machinery%22">Milling machinery</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Machining%22">Machining</searchLink><br /><searchLink fieldCode="DE" term="%22Mechanical+wear%22">Mechanical wear</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Sensor fusion of multi-sensory measurements is believed to enhance accuracy of tool condition monitoring methods for sensor signals which are at least partly complementary. In this paper, a novel statistic based on the least squared error criteria is used to evaluate performances of models arising from sensor fusion. An adaptive sensor fusion of cutting force and electrical power signals, which performs optimally even in case of failure in any acquiring channel, is proposed. Combinations of signal processing techniques are used to extract accurate and robust features, which are refined by feature space filtering techniques to obtain improved and robust estimators of tool wear. It is shown that the model using sensor fusion of force and power produces superior results to those using single measurements. Error bounds of the estimates are also provided as prediction limits. Significant improvements are obtained compared to the existing methods in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Production Research is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00207540701403376
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 817
    Subjects:
      – SubjectFull: Machine tool monitoring
        Type: general
      – SubjectFull: Regression analysis
        Type: general
      – SubjectFull: Milling machinery
        Type: general
      – SubjectFull: Robust control
        Type: general
      – SubjectFull: Machining
        Type: general
      – SubjectFull: Mechanical wear
        Type: general
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      – TitleFull: Estimation of tool wear based on adaptive sensor fusion of force and power in face milling.
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              Text: Feb2009
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              Y: 2009
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