首页|A new tool wear condition monitoring method based on deep learning under small samples
A new tool wear condition monitoring method based on deep learning under small samples
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NSTL
Elsevier
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.
Small samplesTool condition monitoringRecurrence plotMulti-scale edge-labeling graph neural networkACOUSTIC-EMISSIONMACHINEALGORITHMNETWORKSENSOR
NSTL
Elsevier
