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Optimization strategy for tool life based on dynamic modal parameter identification.

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Title: Optimization strategy for tool life based on dynamic modal parameter identification.
Authors: Wang, Qi1,2 (AUTHOR) wangq@czu.cn, Chen, Xi1 (AUTHOR), An, Qinglong2 (AUTHOR), Chen, Ming2 (AUTHOR), Guo, Hun1 (AUTHOR), He, Yafeng1 (AUTHOR)
Source: International Journal of Advanced Manufacturing Technology. Jul2025, Vol. 139 Issue 1, p343-353. 11p.
Subjects: Cutting force, Data conversion, Gaussian distribution, Machine tools, Prediction models
Abstract: The selection of cutting parameters and the stability of the machining process related to tool modal parameters have a direct and significant impact on tool life. Traditionally, it is obtained through hammering experiments. However, throughout the machining, modal parameters usually vary with the continuous changes. This paper proposes a tool life optimization strategy based on online identification of tool modal parameters. Firstly, a cutting force prediction model is established through an orthogonal experimental platform, and then the dynamic chip thickness obtained from the measured cutting force is used to calculate the modal parameters of the time-varying tool. The identified modal parameters exhibit a normal distribution trend related to the cutting cycle. Finally, modal parameters are used for parameter optimization in online machining to optimize tool life. The experiment shows that the method can accurately identify modal parameters without hammering experiments, while it can effectively reduce tool wear and improve tool life. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature 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: Optimization strategy for tool life based on dynamic modal parameter identification.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Qi%22">Wang, Qi</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> wangq@czu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Xi%22">Chen, Xi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22An%2C+Qinglong%22">An, Qinglong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Ming%22">Chen, Ming</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Guo%2C+Hun%22">Guo, Hun</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Yafeng%22">He, Yafeng</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Advanced+Manufacturing+Technology%22">International Journal of Advanced Manufacturing Technology</searchLink>. Jul2025, Vol. 139 Issue 1, p343-353. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Cutting+force%22">Cutting force</searchLink><br /><searchLink fieldCode="DE" term="%22Data+conversion%22">Data conversion</searchLink><br /><searchLink fieldCode="DE" term="%22Gaussian+distribution%22">Gaussian distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+tools%22">Machine tools</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The selection of cutting parameters and the stability of the machining process related to tool modal parameters have a direct and significant impact on tool life. Traditionally, it is obtained through hammering experiments. However, throughout the machining, modal parameters usually vary with the continuous changes. This paper proposes a tool life optimization strategy based on online identification of tool modal parameters. Firstly, a cutting force prediction model is established through an orthogonal experimental platform, and then the dynamic chip thickness obtained from the measured cutting force is used to calculate the modal parameters of the time-varying tool. The identified modal parameters exhibit a normal distribution trend related to the cutting cycle. Finally, modal parameters are used for parameter optimization in online machining to optimize tool life. The experiment shows that the method can accurately identify modal parameters without hammering experiments, while it can effectively reduce tool wear and improve tool life. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Advanced Manufacturing Technology is the property of Springer Nature 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: 10.1007/s00170-025-15902-3
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      – Code: eng
        Text: English
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        PageCount: 11
        StartPage: 343
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        Type: general
      – SubjectFull: Data conversion
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      – SubjectFull: Gaussian distribution
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      – SubjectFull: Machine tools
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      – SubjectFull: Prediction models
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      – TitleFull: Optimization strategy for tool life based on dynamic modal parameter identification.
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            – D: 01
              M: 07
              Text: Jul2025
              Type: published
              Y: 2025
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