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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 02683768 |
| DOI: | 10.1007/s00170-025-15902-3 |
