期刊
MEASUREMENT
卷 189, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2021.110622
关键词
Small samples; Tool condition monitoring; Recurrence plot; Multi-scale edge-labeling graph neural network
资金
- Zhejiang Provincial Natural Science Foundation of China [LQ21E050003]
- Wenzhou Key Innovation Project for Science and Technology of China [ZG2021027]
This research proposed a new improved multi-scale edge-labeling graph neural network (MEGNN) to enhance the recognition accuracy of DL-based TCM under small samples. By expanding each channel signal of a cutting force sensor to multi-dimensional data, the MEGNN method outperforms three DL-based methods (CNN, AlexNet, ResNet) in experiments.
Tool wear condition monitoring (TCM) is an important part of machining automation. In recent years, deep learning (DL) based TCM methods have been widely researched. However, almost DL-based methods need sufficient learning samples to obtain good accuracy, which is hard for TCM in terms of cost and time. In order to enhance the recognition accuracy of DL-based TCM under small samples, this paper proposed a new improved multi- scale edge-labeling graph neural network (MEGNN). Each channel signal of a cutting force sensor is expanded to multi- dimensional data through phase space reconstruction. Then, these multi- dimensional data are encoded into a gray recurrence plot (RP), and aggregated into a color RP, which is input to MEGNN to extract features for establishing a fully connected graph. Finally, the tool wear condition is estimated through the updated edge labels using a weighted voting method. Applications of the proposed MEGNN- based method to PHM 2010 milling TCM dataset and our experiments demonstrate it outperforms three DL-based methods (CNN, AlexNet, ResNet) under small samples.
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