US 12,434,344 B1
Tool wear state monitoring method and system based on multiple types of signals
Qian Qiao, Zhuhai (CN); Xinpeng Que, Zhuhai (CN); Ying Pan, Zhuhai (CN); Dawei Guo, Zhuhai (CN); and Lapmou Tam, Zhuhai (CN)
Filed by IDQ Science and Technology (Guangdong, Hengqin) Co., Ltd., Zhuhai (CN)
Filed on Jul. 10, 2025, as Appl. No. 19/266,096.
Application 19/266,096 is a continuation of application No. PCT/CN2025/088019, filed on Apr. 9, 2025.
Claims priority of application No. 202510219463.5 (CN), filed on Feb. 26, 2025.
Int. Cl. B23Q 17/09 (2006.01); G05B 19/4065 (2006.01)
CPC B23Q 17/0971 (2013.01) [B23Q 17/0966 (2013.01); B23Q 17/098 (2013.01); G05B 19/4065 (2013.01)] 10 Claims
OG exemplary drawing
 
1. A tool wear state monitoring method based on multiple types of signals, characterized by comprising:
S1: obtaining cutting force data, acoustic emission signal data and vibration signal data of a tool in a machining process;
S2: extracting a plurality of statistical features from the cutting force data and the acoustic emission signal data;
S3: extracting a singularity feature from the vibration signal data by combining a singularity analysis with a wavelet transform;
S4: building a tool wear state monitoring model based on a random forest;
S5: using the extracted statistical features and the singularity feature as an input to preliminarily train the tool wear state monitoring model, and outputting preliminary tool wear results;
S6: evaluating whether the preliminary tool wear result meets a preset condition; if yes, proceeding to S9; otherwise, proceeding to S7;
S7: using a preliminarily trained tool wear state monitoring model to evaluate the importance of each statistical feature and singularity feature to determine key features that affect a tool wear state;
S8: based on the key features, refining the tool wear state monitoring model;
S9: obtaining real-time cutting force data, real-time acoustic emission signal data and real-time vibration signal data; and
S10: based on the real-time cutting force data, the real-time acoustic emission signal data and the real-time vibration signal data, monitoring the tool wear state through the trained tool wear state monitoring model.