^{Data-drivenandknowledge-guided prediction model of milling tool lifegrade}

ABSTRACT

Models that predict tool life based on wear mechanism knowledge are typically inaccurate, as the use of simplified model parameters can have a significant effect on this prediction. While a tool life prediction model based on sample cutting data is limited to specific working conditions, which makes tool life prediction difficult to generalize, and needs a large amount of historical data as support. In this paper, the empirical formula of tool life based on wear mechanism knowledge was combined with a neural network, which can significantly improve prediction accuracy. Firstly, a concept of tool life grade is proposed, and its classification standard is outlined. Secondly, a prediction model based on the empirical life formula and experimental data was established. Thirdly, a tool wear prediction model based on a convolutional neural network (CNN) was established through the real-time tool condition data, and the corresponding life compensation strategy can be determined by comparing this with the historical data. Finally, the empirical life grade was adjusted to obtain the real-time tool life grade. A case example shows that the data-driven knowledge-guided prediction model can significantly improve the recognition accuracy of tool life grade.

KEYWORDS:

• Milling tool life grade
• wear mechanism knowledge
• condition data
• convolutional neural network
• real time prediction

Acknowledgements

This work was supported in part by the National Key R&D Program of China (2021YFB3301702), Major Special Science and Technology Project of Shaanxi Province, China (No.2018zdzx01-01-01), and the Natural Science Foundation of Shaanxi Province, China (No. 2021JM-173).

Disclosure statement

No potential conflict of interest was reported by the authors.

Contribution Statement

Fuqiang Zhang provided the research idea; Fengli Xu wrote the paper and developed a software testing system; Xueliang Zhou and Jiew Leng conducted review and editing; Kai Ding provided the funding acquisition; Shujun Shao and Chao Du provided the data set.

Additional information

Funding

The work was supported by the National Key R&D Program of China [2021YFB3301702]; Natural Science Foundation of Shaanxi Province, China [2021JM-173]; Major Special Science and Technology Project of Shaanxi Province, China [2018zdzx01-01-01].