Method and device for monitoring wear of a high-precision milling machine tool
By acquiring multimodal sensing signals and using micro-perturbations for temporal resampling and feature alignment, the problem of signal susceptibility to noise interference in high-precision milling machine tool wear monitoring was solved, enabling continuous and accurate monitoring of tool wear.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHENZHEN ZHAOHE TECH CO LTD
- Filing Date
- 2026-05-29
- Publication Date
- 2026-07-17
AI Technical Summary
In the existing technology, high-precision milling machine tool wear monitoring methods are easily affected by machine tool background noise, making it difficult to achieve effective signal extraction and feature recognition. Furthermore, machine vision monitoring cannot capture dynamic wear behavior under extreme cutting loads.
By acquiring multimodal sensing signals (acoustic emission, vibration, and current signals) during the cutting process, injecting active modulation signals within a dynamic sliding window, and utilizing the frequency modulation features generated by micro-perturbations for time-domain resampling and feature alignment, the tool wear state is determined by combining a deep learning model.
It enables continuous monitoring during the cutting process, reduces the impact of background noise on the signal, and improves the accuracy and real-time performance of tool wear condition identification.
Smart Images
Figure CN122401167A_ABST